Low-risk Category Research Articles

Prognostic Testing for Prostate Cancer-A Cost-Effectiveness Analysis Comparing a Prostatype P-Score Biomarker Approach to Standard Clinical Practice.

The Prostatype score (P-score) is a prognostic biomarker that integrates a three-gene (IGFBP3, F3, and VGLL3) signature derived from prostate biopsy samples, with key clinical parameters, including prostate-specific antigen (PSA) levels, Gleason grade, and tumor stage at diagnosis. The test has demonstrated superior predictive accuracy for prostate cancer outcomes compared with traditional risk categorization systems such as D'Amico. Notably, it reclassifies a higher proportion of patients into the low-risk category, making them eligible for active surveillance. This study assessed the cost-effectiveness of the P-score in comparison with D'Amico and the Swedish National Prostate Cancer Register (NPCR) risk categorization systems. A two-step decision analytic model was developed. The model consisted of a decision tree-informed Markov structure estimating the lifetime outcomes of 60-year-old men with diagnosed prostate cancer. Prostate cancer was classified as low-risk, intermediate-risk, or high-risk using either the P-score or D'Amico. Initial therapy was based on observed treatment patterns from the Swedish NPCR. Costs (SEK, year 2022) and quality-adjusted life years (QALYs) were estimated from a healthcare perspective and discounted at 3% per year; incremental cost-effectiveness ratio (ICER) was the primary outcome. The P-score led to cost savings and generated an additional 0.19 QALYs compared with D'Amico. The added costs of the genetic test and higher costs of active surveillance and radiotherapy were counterbalanced by savings from reduced costs of surgery, treatment-related side-effects, and metastatic disease. The gain in QALYs was primarily due to the avoidance of metastatic disease and a reduction in treatment-related side-effects. The results of this study suggest that the P-score is likely to be a cost-effective alternative to D'Amico for prognostic evaluation of newly diagnosed prostate cancer in Sweden and compared with NPCR when health-related quality of life was included.

Influence of radiotherapy on cardiac implantable devices and leads-asingle-institution analysis and critical evaluation of current guidelines.

Increasing life expectancy and advances in cancer treatment will lead to more patients needing both radiation therapy (RT) and cardiac implantable electronic devices (CIEDs). CIEDs, including pacemakers and defibrillators, are essential for managing cardiac arrhythmias and heart failure. Telemetric monitoring of CIEDs checks battery status, lead function, settings, and diagnostic data, thereby identifying software deviations or damage. This study evaluates the German Society for Radiation Oncology (DEGRO)/German Society for Cardiology (DGK) guideline, assessing real-world complications and risk factors and analyzing pacemaker and implantable cardioverter-defibrillator (ICD) lead function for their predictive value concerning device malfunction. Atotal of 54patients with pacemakers or ICDs who underwent radiation therapy were identified. Demographics, treatment courses, and device information from physical and digital records were extracted. DEGRO/DGK risk groups and pacemaker dependency at the start of RT were assessed. Delineation of the devices and lead insertion sites was performed in the treatment planning system. Dosimetric information from the treatment plans was then correlated with reports of standardized device checks. Over 80% of patients were treated with dual-chamber pacemakers or cardiac resynchronization therapy (CRT), and 16.7% had ICDs. One third of patients were pacemaker dependent. 59.3% of patients were in the low-risk category, 29.3% in the medium-risk, and 11.1% in the high-risk category. Thoracic irradiation resulted in the highest median dose to devices. Lead parameter deviations exceeding thresholds were found in 14.8% for the stimulation threshold and 13.5% for sensing. Device malfunctions occurred in 3.7% of cases, both involving electrical resets and neutron-producing radiation (beam energy 10megaelectron volt (MV) or higher). Collecting lead parameters in addition to secure malfunctions like electrical restarts and memory erasure revealed asignificant proportion of treatment courses with temporary changes, though no correlation with individual treatment plans or adverse events was found. The focus on reducing neutron-producing radiation could be further supported.

Clinical Implications of Breast Cancer Intrinsic Subtypes.

Estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancers have different genomic architecture and show large-scale gene expression differences consistent with different cellular origins, which is reflected in the luminal (i.e., ER+) versus basal-like (i.e., ER-) molecular class nomenclature. These two major molecular subtypes have distinct epidemiological risk factors and different clinical behaviors. Luminal cancers can be subdivided further based on proliferative activity and ER signaling. Those with a high expression of proliferation-related genes and a low expression of ER-associated genes, called luminal B, have a high risk of early recurrence (i.e., within 5years), derive significant benefit from adjuvant chemotherapy, and may benefit from adding immunotherapy to chemotherapy. This subset of luminal cancers is identified as the genomic high-risk ER+ cancers by the MammaPrint, Oncotype DX Recurrence Score, EndoPredict, Prosigna, and several other molecular prognostic assays. Luminal A cancers are characterized by low proliferation and high ER-related gene expression. They tend to have excellent prognosis with adjuvant endocrine therapy. Adjuvant chemotherapy may not improve their outcome further. These cancers correspond to the genomic low-risk categories. However, these cancers remain at risk for distant recurrence for extended periods of time, and over 50% of distant recurrences occur after 5years. Basal-like cancers are uniformly highly proliferative and tend to recur within 3-5years of diagnosis. In the absence of therapy, basal-like breast cancers have the worst survival, but these also include many highly chemotherapy-sensitive cancers. Basal-like cancers are often treated with preoperative chemotherapy combined with an immune checkpoint inhibitor which results in 60-65% pathologic complete response rates that herald excellent long-term survival. Patients with residual cancer after neoadjuvant therapy can receive additional postoperative chemotherapy that improves their survival. Currently, there is no clinically actionable molecular subclassification for basal-like cancers, although cancers with high androgen receptor (AR)-related gene expression and those with high levels of immune infiltration have better prognosis, but currently their treatment is not different from basal-like cancers in general. A clinically important, minor subset of breast cancers are characterized by frequent HER2 gene amplification and high expression of a few dozen genes, many residing on the HER2 amplicon. This is an important subset because of the highly effective HER2 targeted therapies which are synergistic with endocrine therapy and chemotherapy. The clinical behavior of HER2-enriched cancers is dominated by the underlying ER subtype. ER+/HER2-enriched cancers tend to have more indolent course and lesser chemotherapy sensitivity than their ER counterparts.

Ceramide-based risk score CERT-1 improves risk prediction for overall mortality and adverse cardiovascular outcomes in patients with and without cardiovascular disease: A prospective cohort study.

Whether the plasma-based ceramide-based risk score CERT1 improves risk prediction for cardiovascular disease (CVD) is uncertain. Baseline and follow-up data were combined from two cohorts, 334 patients with established/suspected CVD and 196 patients with type 2 diabetes followed for a median of 74 months (interquartile range 54-79 months). For the calculation of CERT1 risk score, we measured four specific plasma ceramides [Cer(d18:1/16:0), Cer(d18:1/18:0) and Cer(d18:1/24:1)] and their ratios to Cer(d18:1/24:0). Based on the CERT1 risk score, patients were split into four risk categories (low, moderate, increased or high risk). The primary outcome was a composite of overall mortality and incident nonfatal CVD outcomes (including myocardial infarction, ischaemic stroke or permanent atrial fibrillation). One hundred and thirty-nine patients developed the primary composite outcome (72 nonfatal CVD outcomes and 67 total deaths) during follow-up. Baseline CERT1 risk categories were significantly associated with the risk of developing the primary composite outcome (adjusted HR for high vs. low-risk category 2.43, 95% CI 1.39-4.22, p = 0.002, and adjusted HR for increased vs. low-risk category 2.16, 95% CI 1.28-3.63, p = 0.004). Receiver operator characteristic curve analysis showed that adding CERT1 risk score to traditional CVD risk factors and pre-existing CVD, improved the discriminatory capability of the regression model for predicting the primary composite outcome (AUROC 0.691 [95% CI 0.674-0.769] vs. 0.722 [95% CI 0.642-0.742], p = 0.0275). The ceramide-based risk score CERT1 risk score improves risk prediction for long-term risk of overall mortality and adverse cardiovascular outcomes in patients with and without CVD.

Analisis Risiko Warehouse Management System Dalam Supply Chain Management Di PT. Magnesium Gosari Internasional

This research aims to analyze risk management in the Warehouse Management System (WMS) within Supply Chain Management at PT. Magnesium Gosari Internasional using the ISO 31000:2009 framework. The research methodology employs a qualitative approach through direct observation, in-depth interviews, and documentation studies. Data analysis was conducted by identifying, assessing, and evaluating risks based on their probability and impact levels. The research identified 21 risks classified into 5 main categories: Receiving, Putaway, Demand, Storage, and Shipment. Of the total identified risks, 7 risks fall into the low-risk category, 5 risks in the medium-risk category, and 9 risks in the high-risk category. This research provides risk treatment recommendations that can be implemented to enhance the effectiveness of risk management in the company's warehouse system.

A novel recommender framework with chatbot to stratify heart attack risk

Cardiovascular diseases are a major cause of mortality and morbidity. Fast detection of life-threatening emergency events and an earlier start of the therapy would save many lives and reduce successive disabilities. Understanding the specific risk factors associated with heart attack and the degree of association is crucial in the clinical diagnosis. Considering the potential benefits of intelligent models in healthcare, many researchers have developed a variety of machine learning (ML)-based models to identify patients at risk of a heart attack. However, the common problem of previous works that used ML concepts was the lack of transparency in black-box models, which makes it difficult to understand how the model made the prediction. In this study, an automated smart recommender system (Explainable Artificial Intelligence) for heart attack prediction and risk stratification was developed. For the purpose, the CatBoost classifier was applied as the initial step. Then, the SHAP (SHapley Additive exPlanation) explainable algorithm was employed to determine reasons behind high or low risk classification. The recommender system can provide insights into the reasoning behind the predictions, including group-based and patient-specific explanations. In the final step, we integrated a Large Language Model (LLM) called BioMistral for chatting functionally to talk to users based on the model output as a digital doctor for consultation. Our smart recommender system achieved high accuracy in predicting a patient risk level with an average AUC of 0.88 and can explain the results transparently. Moreover, a Django-based online application that uses patient data to update medical information about an individual’s heart attack risk was created. The LLM chatbot component would answer user questions about heart attacks and serve as a virtual companion on the route to heart health, our system also can locate nearby hospitals by applying Google Maps API and alert the users. The recommender system could improve patient management and lower heart attack risk while timely therapy aids in avoiding subsequent disabilities.

The Impact of Fly Ash and Sea Sand on Strength and Durability of Concrete

Fly ash is a solid waste produced by power plants. To overcome the bad effects on the environment, fly ash is used as a substitute for cement in making concrete. Apart from that, the innovation of aggregate replacement materials in mixing concrete also has several alternatives, one of which is the use of sea sand as a substitute for fine aggregate. This research was conducted to determine the best proportion of sea sand use 10–40% with two treatments (washed and unwashed) and the use of fly ash of 20-30% on the corrosion resistance of reinforcing steel and the mechanical properties of concrete. The research results show that using up to 30% washed sea sand and 10% unwashed sea sand can increase compressive strength when compared to normal concrete. The use of up to 30% fly ash together with 20% sea sand has the same compressive strength as normal concrete. Apart from that, the use of sea sand and fly ash together has a low-risk category for reinforcing steel corrosion (10% risk of corrosion).

Morphometric parameterization of flash flood risk assessment

Individual morphometric characteristics of river basins significantly impact their activity; they affect the flow rate of water, water accumulation in the river basin, water runoff time and other attributes that affect their intensity during flash floods and hydrological droughts. The aim of the paper is to express the risk and impact of flash floods in the Topla River basin (Slovakia) and its sub-basins with regard to the morphometric characteristics of the area. The studied locality, especially the spring of the Topla river, belongs to the area with an existing, potentially significant flood risk threatening not only important localities but also the population. The objective is to support the territories with flood mitigation measures by assessing the basin morphometric factors and classifying such areas into high, medium and low-risk categories of flash floods. The model situation is developed in the ArcGIS program, where the selected important morphological characteristics are assigned to all sub-basins for the Topla river together with its assessment. For each characteristic, the level of risk affecting the flash flood is determined. An important part of this study is developing a river basin susceptibility scale to flood activity in terms of morphometric characteristics obtained from available literature, empirical relationships and human experiences. The result of the sensitivity risk map of the upper part of the Topla river basin is to show where adverse impacts on people and the surrounding environment are expected after flash floods hit (or in the event of hydrological droughts).

The gut microbiome and eating behavior outcomes: A systematic review.

Dysfunctional eating behaviors, and their modulators, are an important component in the prevalence of malnutrition. The gut microbiome, acting through the microbiota-gut-brain axis, is implicated as a modifiable factor in eating behavior. ObjectivesThis systematic review investigated the influence of the gut microbiome on human eating behavior and their modulators (appetite, satiety, energy/food intake, weight loss/gain). Literature was retrieved from PubMed, Embase, and Web of Science until July 2022. In total, 37 studies were included, with interventions including pre/post/syn-biotics. The primary outcome was eating behaviors, and their modulators. The gut microbiome was associated with eating behaviors, and their modulators, in 28 studies, particularly affecting appetite/appetite hormones, energy/food intake, and weight gain. Intake of probiotics and synbiotics were inconsistently related to appetite and appetite hormone levels, (n = 6 and n = 2 respectively). Prebiotic supplementation showed a consistent trend in the reduction of appetite (n = 4), regulation of appetite hormone levels (n = 10), and increase in self-reported satiety (n = 4). Conversely, energy intake (n = 18) and weight gain/loss (n = 7) were inconsistently associated with probiotic interventions across studies, populations, and interventions. In terms of quality of evidence, most publications had a high risk or some concerns of risk of bias, with fewer than 25% falling into the low-risk category. This review provides an overview of the links between the gut microbiome and human eating behavior across human phenotypes. While explicit associations between specific microbiome taxa and eating behavior are identified, further evidence is needed to substantiate causal relationships. Future research with standardized methods and prospective designs is needed.

Inadequate identification of high cardiovascular risk and carotid plaques in rheumatoid arthritis patients by the 2024 Predicting Risk of Cardiovascular EVENTs and the 2013 Atherosclerotic Cardiovascular Disease algorithms: findings from a Mexican cohort.

The American College of Cardiology/American Heart Association introduced the Predicting Risk of Cardiovascular EVENTs (PREVENT™) algorithm to estimate the 10-year risk of developing cardiovascular disease. We aimed to assess the cardiovascular risk (CVR) reclassification among rheumatoid arthritis (RA) patients using traditional CVR algorithms-the 2024 PREVENT™ and the 2013 Atherosclerotic Cardiovascular Disease (ASCVD)-and the presence of carotid plaque (CP). This was a cross-sectional study nested of a RA patients' cohort. A certified radiologist performed a high-resolution B-mode carotid ultrasound to identify the presence of CP. The CVR evaluation was performed by a cardiologist, blinded to carotid ultrasound results, using the PREVENT™ and the ASCVD algorithms. Cohen's kappa (k) coefficient assessed concordance between high-risk classification by CVR algorithms and CP presence. ROC curve analysis evaluated the algorithms' capacity to identify RA patients with CP. The cutoff point was determined by the Youden-Index, with p < 0.05 as statistically significant. A total of 210 RA patients were included. The reclassification of CVR due to CP was 34.3% for the PREVENT™ algorithm and 30.0% for the ASCVD algorithm. Of these, 44.4% and 71.4%, respectively, were initially classified as low risk. Concordance between CVR algorithms and carotid ultrasound showed slight agreement (k = 0.032 and k = 0.130, respectively). The PREVENT™ algorithm did not identify more than one-third of high-CVR RA patients with indication of starting statin therapy based on carotid ultrasound findings. The PREVENT™ and ASCVD algorithms showed poor performance in identifying RA patients with CP. Key Points • The presence of CP was identified in more than a third of the evaluated RA patients (35.7%), classifying them as high CVR. • CVR reclassification by the presence of CP was observed in 34.3% RA patients with the PREVENTTM algorithm and in 30.0% RA patients with the ASCVD algorithm. • Most of the reclassified patients belonged to the low-risk category, 44.4% with the PREVENTTM algorithm and 71.4% with the ASCVD algorithm. • When evaluating the concordance between the ASCVD algorithm and the carotid ultrasound for high-risk classification, a slight agreement was found (k = 0.130).

Potential impact of obstetric history on postmenopausal fragility fracture risk: A reassessment of the Fracture Risk Assessment Tool.

This study aimed to evaluate the impact of integrating obstetric parameters into the Fracture Risk Assessment Tool (FRAX) on the precision of risk assessment. In this retrospective study, patients who experienced postmenopausal fragility fractures of the distal radius, proximal femur, or lumbar vertebrae between January 1, 2021, and December 31, 2023, were included. Obstetric histories, along with standard FRAX parameters, were obtained by phone interviews. Based on the FRAX major osteoporotic fracture risk score calculated without bone mineral density, patients were classified into high-, intermediate-, and low-risk group categories. Differences in age at menarche, age at menopause, lactation duration, gravidity, and parity were analyzed across risk categories. A total of 328 patients (mean age: 64.5±5.8 years; range, 55 to 75 years) were included. The mean FRAX score was 16±8.8 (range, 3 to 58), and 85, 191, and 52 patients were classified as high-, intermediate-, and low-risk, respectively. A positive correlation was observed between FRAX scores and both later age at menarche and earlier menopause (p<0.001 and p=0.008, respectively). The mean age at menopause was significantly different between the high- and low-risk groups (46.4 vs. 49.3 years, p=0.016). The intermediate-risk group was also evaluated, showing no significant differences in obstetric parameters compared to the low-risk group (p>0.05). Although late menarche is not explicitly included in FRAX, its association with higher fracture risk was evident. The established influence of early menopause on FRAX scores supports its role in fracture risk estimation. However, the inclusion of additional obstetric parameters did not enhance the predictive accuracy of FRAX in this cohort.

POTENSI BAHAYA DAN RISIKO DENGAN METODE HIRADC PADA PROSES OPERASIONAL INDUSTRI PETROKIMIA PADA PERGANTIAN ADSORBENT 201-D PABRIK 1-B

The implementation of HIRADC (Hazard Identification Risk Assessment and Determinant Control) is one of the efforts that can be made to control potential hazards that cause work accidents. HIRADC (Hazard Identification Risk Assessment and Determinant Control) is the process of identifying hazards, assessing, and evaluating the risks contained in work activities and environments. The results of the risk evaluation are used to determine hazard control efforts so that they can reduce the level of risk that is likely to cause work accidents. The method used to complete the research results has achieved the expected objectives. The data presented uses a qualitative descriptive approach, which shows K3 in accordance with current conditions, developing policies, ongoing processes, and effects that occur or trends that are occurring in the company. These results are compared with data obtained in the field or from the HSE regarding the implemen- tation procedure of HIRADC (Hazard Identification Risk Assessment and Determi- nant Control) and compared with applicable standards. Based on the identification that has been carried out, 42 potential hazards have been identified from 21 stages of work activities, 6 of which are in the high risk category, 20 are in the low risk category, 16 are in the medium risk category. Furthermore, all these activities or processes are then analyzed in the HIRADC table and converted into percentages for each category to determine the risk priority. The results for the low risk level are 0.47%, the medium risk level is 0.38%, and the high risk level is 0.14%. This company has made efforts to reduce potential hazards in the work environment through substitution, engineering control, administrative control, and finally PPE

An integrated machine learning framework for developing and validating a prognostic risk model of gastric cancer based on endoplasmic reticulum stress-associated genes

BackgroundGastric cancer (GC), a prevalent and deadly malignancy, demonstrates poor survival outcomes. Evidence has emerged indicating that disruptions in endoplasmic reticulum homeostasis are significantly implicated in the onset and progression of various oncological conditions. This study was designed to construct a prognostic model based on genes related to endoplasmic reticulum stress(ERS) to predict survival outcomes in patients with GC. MethodsExpression profiling data for GC samples were extracted and analyzed from TCGA-STAD, revealing 214 genes related to endoplasmic reticulum stress that show differential expression when compared with normal gastric tissue. Building on these insights, a prognostic model was formulated using data from TCGA-STAD and validated through subsequent analyses of GEO datasets. The tumor immune dysfunction and exclusion(TIDE) algorithm was applied to determine the susceptibility of individuals in high- and low-risk categories to immunotherapy. The presence of immune and stromal cells within the tumor microenvironment was assessed with the aid of the ESTIMATE algorithm. Sensitivity variations to prevalent anticancer drugs between the risk groups were evaluated using the Genomics of Drug Sensitivity in Cancer(GDSC) database, and prospective therapeutic agents were confirmed through molecular docking techniques. ResultsThirty-one endoplasmic reticulum stress (ERS)-related differentially expressed genes (DEGs) crucial for prognosis in GC were pinpointed. These DEGs were then used to construct a prognostic model and were considered as independent prognostic factors for GC patients. This risk model proved to have a good predictive performance for estimating the overall survival of these patients. The patients placed into the high-risk group showed worse results and lower sensitivity to immunotherapy. Moreover, five specific targeted therapy drugs, namely BMS-754807, Dasatinib, JQ1, AZD8055 and SB505124, produced better results in the treatment of the high-risk group of patients. ConclusionsA new molecular prognostic model associated with ERS was established and validated for GC and showed relatively good discriminative and predictive ability. This model greatly expands the collection of weapons in the armoury of prognostic analysis in GC.

Common intestinal parasitic infections in an improved water access, sanitation, and hygiene profile setting in North Jakarta, Indonesia.

Intestinal parasitic infections (IPIs), caused by helminths and/or protozoa, continue to be a significant public health concern in Indonesia. Water access, sanitation, and hygiene practices (WASH) are influential factors for IPIs, especially among children. The aim of this study was to investigate the association between WASH and IPIs among school-aged children. A cross-sectional study involving 338 school-age children in an urban slum area in North Jakarta, Indonesia, was conducted using stool specimens subjected to microscopic and real-time polymerase chain reaction (rt-PCR) examination. The children underwent a finger-prick blood test and anthropometric measurements to determine anemia and nutritional status. Parents whose children participated in stool and blood examinations were interviewed using a modified WASH questionnaire. Helminth infections were not found in this study, whereas the overall prevalence of intestinal protozoa parasitic infection (IPPI) was 18.3% and 52.4% by microscopy and rt-PCR, respectively. Blastocystis spp. was found to have the highest prevalence (microscopy: 12%; rt-PCR: 48.6%), followed by Giardia intestinalis (microscopy: 0.6%; rt-PCR: 6.7%), Cryptosporidium spp. (microscopy: 5.1%; rt-PCR: 1.6%), and Entamoeba histolytica/dispar (microscopy: 0.6%; rt-PCR: 3.2%). Additionally, Dientamoeba fragilis was detected by rt-PCR at 4.1%. Furthermore, the discrepancies between microscopy and rt-PCR were observed in 8.9% (n = 28) of the examined specimens. The majority of the respondents had a low-risk category of WASH profile. School children aged 5-10 years old (OR = 2.06; 95%CI = 1.27-3.33) and those who drank unprocessed cooking water (OR = 1.95; 95%CI = 1.07-3.57) were significantly associated with IPPI. The present study demonstrated that rt-PCR provides a better understanding of IPI epidemiology and has potential as a monitoring strategy for managing IPIs. Even though this population exhibits an adequate WASH profile and is not directly associated with IPIs, conducting a more in- depth observation of WASH facilities and practices is recommended to ensure a comprehensive assessment of the WASH profile. Additionally, engaging stakeholders in health promotion programs to ensure the sustainability of a good WASH profile and awareness of parasitic infections will be advantageous in achieving optimal urban health.

A Multi-level Classification Model for Corrosion defects in Oil and Gas Pipelines Using Meta-Learner Ensemble (MLE) Techniques

Maintaining the integrity of oil and gas pipelines is necessary for the efficient and safe transport of hydrocarbons. Corrosion defects can lead to decreased operational efficiency, leaks, a reduction in operational efficiency, and even catastrophic pipeline failures. Machine learning techniques are useful in detecting corrosion defects, ensemble methods that combine multiple classifiers offer better predictive accuracy. The aim of this work is to develop multi-level classification model an efficient ensemble technique capable of predicting the level of corrosion defects and addressing class imbalances in oil and gas pipeline data. The study uses a two-level stacking ensemble learning method that enhances corrosion defect prediction called the Meta-Learner Ensemble. The model classifies corrosion defects into three categories: high, medium, and low. Prediction accuracy was improved by using a stacking classifier that combines multiple basic classifiers with a logistic regression meta-learner. Findings show that most corrosion defects fall within the low-risk category, with a significant number falling into the medium-to-high-risk range, highlighting the necessity for targeted maintenance. Considering the challenges of dataset imbalance, the stacking classifier achieved 94% accuracy, showing balanced performance across all risk categories. The stacking model outperformed the Random Forest and Logistic Regression models in terms of F1-scores, precision, and recall. This study demonstrates the application of stacking ensemble techniques in predicting corrosion risks and optimizing pipeline maintenance strategies. It provides vital information for improving pipeline safety and optimizing predictive maintenance practices by providing an in-depth assessment of various machine learning models, especially when real-time monitoring systems are integrated.

Risk Factors in Chronic Kidney Disease in Newly Diagnosed Type 2 Diabetes Mellitus Patients Attending a Tertiary Care Hospital in South Tamil Nadu, India: A Cross-sectional Study

Introduction: Chronic Kidney Disease (CKD) has become a major cause of global morbidity and mortality, especially in developing countries like India. When diagnosed at the end stage, the overall average cost incurred by the health system per haemodialysis session is INR 4,148, and the mean Out-of-Pocket Expenditure (OOPE) per patient is INR 2,838, which a poor can not afford. Indian CKD registry infers that Type 2 Diabetes Mellitus (T2DM) attributes for 31.2% of CKD cases. The high cost of haemodialysis and the expenditure spent out of pocket by impoverished patients warrants the need for the present study, which was aimed to assess CKD risk among newly diagnosed diabetics. Aim: To assess CKD risk among newly diagnosed diabetics using the Kidney Disease Improving Global Outcomes (KDIGO) 2021 guidelines, which utilise estimated Glomerular Filtration Rate (eGFR) and urine microalbuminuria as assessment tools. Materials and Methods: The present cross-sectional study was conducted in the Non Communicable Disease (NCD) Outpatient Department (OPD), Madurai Medical College (attached to the Government Rajaji Hospital), Madurai, Tamil Nadu, India, from July 1st 2022 to August 30th, 2022. Study was conducted among 200 newly diagnosed T2DM patients (diagnosed for less than 6 months), selected by consecutive sampling. After the initial administration of a questionnaire, blood and random spot urine samples were collected. CKD risk was assessed based on eGFR and urine microalbuminuria using the KDIGO 2021 guidelines, with eGFR calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. Statistical analysis was done using Statistical Package for the Social Sciences (SPSS) software 21.0, employing the Chi-square test and Pearson’s correlation tests. Results: Of the total 200 study participants, 130 (65%) were females and 70 (35%) were males with a mean age of 47.74±10.35 years. Of the study participants, 106 (53%), belonged to the low-risk category, 74 (37%) to the moderaterisk category, and 20 (10%) to the high/very high-risk category for CKD. A statistically significant association was observed between alcohol intake and CKD (p-value=0.034). A significant negative correlation (p-value &lt;0.001) was found between age and eGFR (Pearson’s coefficient=-0.400). In the present study, age and gender were not significant factors in CKD risk. While no link was found between carbonated drink consumption and CKD risk, alcohol intake did show a notable association. The study also noted a statistically significant negative correlation between age and eGFR. Conclusion: Identifying CKD risk in diabetic patients using KDIGO guidelines based on eGFR and microalbuminuria is crucial. Early detection is vital to slow progression, particularly from moderate to high-risk categories, thereby reducing personal and societal burdens. Regular monitoring with eGFR testing and follow-ups can improve outcomes and reduce the likelihood of advanced CKD. Collaborative efforts among primary care clinicians and specialists are essential for early CKD risk detection and management using these tools.

A lncRNA signature associated with endoplasmic reticulum stress supports prognostication and prediction of drug resistance in acute myelogenous leukemia.

Acute myelogenous leukemia (AML) is a type of blood cancer that is characterized by the accumulation of young and undeveloped myeloid cells in the bone marrow. It is considered a heterogeneous disease due to its diverse nature. Endoplasmic reticulum (ER) stress has emerged as a critical regulator of tumor development and drug resistance in various cancers. Long non-coding RNAs (lncRNAs) have been found to play a role in the development and prognosis of AML. Nonetheless, there is still limited understanding regarding the involvement of ER stress-related lncRNAs in AML prognosis and their predictive ability for drug resistance. The objective of this study was to examine the potential prognostic and predictive significance of an ER stress-related lncRNA signature in patients diagnosed with AML. Based on the bulk RNA sequence data, we constructed an ER stress-related lncRNA signature using least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. We established nomograms and calibration curves to assess the clinical value of the signature by analyzing overall survival (OS) rates between different risk groups. We also conducted tumor mutation burden (TMB) analysis, predicted immune responses, performed functional and biological enrichment analysis, and evaluated drug sensitivity to investigate the impact of the prognostic signature. Additionally, we built a consensus cluster to explore the need for personalized immunotherapy approaches in treating patients with AML. A prognostic signature was constructed using 227 ER stress-related lncRNAs that showed differential expression. Patients in the high-risk category demonstrated decreased OS rates in comparison to individuals in the low-risk category. The findings from the nomogram and receiver operating characteristic (ROC) curve analysis suggest a notable disparity in age between the different categories. Among the group at high risk, we noticed a considerably greater TMB in comparison to the low-risk group. Furthermore, individuals with both an elevated risk score and high TMB demonstrated the most unfavorable survival rates. Significant differences were observed in the immune responses between the groups classified as high- and low-risk. We then systematically evaluated three different clusters to assess immune responses and drug responses. Through analyzing the association between the risk score and various medications, we have discovered 18 potential drug contenders capable of effectively addressing AML. Furthermore, we conducted pathway analyses to determine the targeted pathways of these drugs. Our data serve as a valuable resource for decoding the immune responses, somatic mutational landscape, drug resistance, and potential biological functions in AML patients. Additionally, our findings offer valuable insights into the association between related lncRNAs and the immune microenvironment of AML. It provides us with promising insights that can help in the development of precise therapeutic strategies.

Advanced AI-Driven Prediction of Pregnancy-Related Adverse Drug Reactions.

Ensuring drug safety during pregnancy is critical due to the potential risks to both the mother and fetus. However, the exclusion of pregnant women from clinical trials complicates the assessment of adverse drug reactions (ADRs) in this population. This study aimed to develop and validate risk prediction models for pregnancy-related ADRs of drugs using advanced Machine Learning (ML) and Deep Learning (DL) techniques, leveraging real-world data from the FDA Adverse Event Reporting System. We explored three methods─Information Component, Reporting Odds Ratio, and 95% confidence interval of ROR─for classifying drugs into high-risk and low-risk categories. DL models, including Directed Message Passing Neural Networks (DMPNN), Graph Neural Networks, and Graph Convolutional Networks, were developed and compared to traditional ML models like Random Forest, Support Vector Machines, and XGBoost. Among these, the DMPNN model, which integrated molecular graph information and molecular descriptors, exhibited the highest predictive performance, particularly at the preferred term level. The model was validated against external data sets from SIDER and DailyMed, demonstrating strong generalizability. Additionally, the model was applied to assess the risk of 22 oral hypoglycemic drugs, and potential substructure alerts for pregnancy-related ADRs were identified. These findings suggest that the DMPNN model is a valuable tool for predicting ADRs in pregnant women, offering significant advancement in drug safety assessment and providing crucial insights for safer medication use during pregnancy.

Spatial Assessment of Soil Erosion Risk Using RUSLE and GIS in Dhumuga Watershed, Ambo, Ethiopia

Soil erosion has become a critical problem leading to land degradation and environmental risks globally. To grasp the rates of soil loss and identify the main factors driving these issues, it is vital to examine the specific impacts of soil erosion across different locations. Therefore, between 2021 and 2023, a research initiative was undertaken to assess, rank, identify, and map sections of the watershed that are particularly susceptible to soil erosion. The RUSLE components for factors R, K, L, S, C, and P were integrated using the ArcGIS 10.4.1 spatial analyst&amp;apos;s raster calculator tool to calculate and create maps that illustrate the risk and intensity of soil erosion in the Dhumuga watershed. The Dhumuga watershed was categorized into five groups based on average annual soil loss: 0–5 ton/ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; year&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; (very slight), 5–10 ton/ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; year&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; (slight), 10–20 ton/ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; year&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; (moderate), 20–50 ton/ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; year&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; (high), and &amp;gt; 50 ton/ha-1 year-1 (very high). The assessment of soil erosion severity was influenced by factors such as rainfall, soil type, DEM, land use, and land cover, employing the GIS-based RUSLE equation. The spatial risk of soil erosion was sorted into five categories based on severity, with 11.58% of the area categorized as very high risk (&amp;gt;50 ton ha&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt; year&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;), and 54.2% in the very low to low-risk category. On average, the watershed yielded an annual sediment production of up to 13.94 tons/ha/year, which is within an acceptable range. Considering these research findings, GIS-based analyses can be utilized to pinpoint areas at risk of soil erosion and identify vulnerable zones, offering crucial insights for future soil conservation and model enhancement.

A novel disulfidptosis-related LncRNA prognostic risk model: predicts the prognosis, tumor microenvironment and drug sensitivity in esophageal squamous cell carcinoma

BackgroundDisulfidptosis is a newly discovered type of cell death that differs from apoptosis, necrosis, ferroptosis and other death modes and is closely related to the occurrence and progression of tumors. However, the predictive potential and biological characteristics of disulfidptosis-related lncRNAs (DRGs-lncRNAs) in esophageal squamous cell carcinoma (ESCC) are unclear.MethodsRNA transcriptome data, clinical information and mutation data for ESCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation and Cox regression analyses were used to identify the DRGs-lncRNAs associated with overall survival (OS). LASSO regression analysis was used to construct the prognostic model. A nomogram was created to predict the prognosis of patients with ESCC. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to identify the signaling pathways associated with the model. TIMER, CIBERSORT, ESTIMATE and other methods were used to analyze immune infiltration, immune function, immune checkpoints and drug sensitivity. The tumor mutation burden (TMB) were assessed between different risk groups. Real-time polymerase chain reaction (RT‒PCR) was used to detect the expression of DRGs-lncRNAs in ESCC cell lines.ResultsA total of 155 lncRNAs significantly associated with disulfidptosis were identified. Through univariate Cox regression analysis, LASSO regression analysis and multivariate Cox regression analysis, 9 lncRNAs with independent prognostic significance were selected, and a prognosis model was established. Survival analysis with the prognostic model revealed that there were obvious differences in survival between the high- and low-risk groups. Further analysis revealed that the immune microenvironment, immune infiltration, immune function, immune checkpoints, and drug sensitivity significantly differed between the high-risk and low-risk groups. Patients who exhibited both high risk and high tumor mutation burden (TMB) survived shorter, while those who fell into the low risk and low TMB categories survived longer. In addition, RT‒PCR analysis revealed differential expression of DRG lncRNAs between ESCC cell lines and esophageal epithelial cell lines.ConclusionsWe established a DRG-lncRNA prognostic model that can be used to predict the prognosis, tumor mutation burden, immune cell infiltration, and drug sensitivity of ECSS patients. The results of this study provide valuable insights into the understanding of ESCC and provide valuable assistance for the individualized treatment of ESCC patients.