Opioid analgesic drugs are widely used in modern medicine for pain management, anesthesia, and palliative care, making the study of their physicochemical properties essential for drug design and optimization. This study aims to investigate the potential of domination degree-based topological indices (DTIs) in quantitative structure–property relationship (QSPR) modeling and to apply a decision-making approach for ranking opioid analgesic drugs. QSPR models were developed using DTIs of chemical graphs, with logarithmic and multilinear regression analyses employed to establish correlations between DTIs and key physicochemical properties. In addition, the VIKOR multi-criteria decision-making method was integrated with QSPR analysis to systematically rank twenty opioid analgesic drugs. The results revealed strong correlations between DTIs and physicochemical properties, confirming their predictive ability, while the VIKOR-based ranking was found to be highly consistent across properties. These findings highlight the reliability of DTIs in predicting drug characteristics and demonstrate the practical utility of combining QSPR modeling with decision-making tools for drug characterization, discovery, and prioritization.

Cross-Population Validation of the Pediatric CKD Risk-Prediction Tool.

Integrated surface-enhanced Raman spectroscopy with LASSO for rapid quantitative analysis of prochloraz residue in citrus.

Defining and measuring gambling satisfaction as an indicator of gambling problems: Consumer perspectives, predictors, and scale utility.

Predicting treatment intolerance in operable head and neck cancer using the modified head and neck surgery risk index.

To investigate whether stress distribution patterns can predict biomechanical progression in keratoconus eyes using longitudinal data. The corneal contribution to stress, (CCS) = r/2t, was calculated based on the Hoop stress formula without intraocular pressure. Here, r is radius of curvature and t is corneal thickness. CCS was calculated from Pentacam tangential curvature and thickness maps (Oculus, Wetzlar, Germany) and investigated the difference in magnitude of stress between the 2-mm zones of minimum and maximum CCS (CCSmin, CCSmax), and the difference between them (CCSdiff). We included patients with diagnosed keratoconus and healthy controls. Exclusion criteria were use of contact lenses, previous corneal surgery, corneal scar, other corneal diseases, and bad quality of Pentacam images. A linear mixed model was used to determine predictive ability of CCSdiff. A P -value <0.05 was considered significant. A total of 114 eyes of 70 patients with keratoconus and 31 eyes of 31 healthy controls were included with a mean age of 24 ± 6 and 24 ± 4 years, respectively. Patients with keratoconus had a mean follow-up time of 2 years (range 0.2-13.6 years). At baseline, in keratoconus, CCSmax was 8.3 ± 1.1 and CCSmin was 6.6 ± 0.6. For healthy eyes, mean values were 7.4 ± 0.5 and 6.5 ± 0.5, respectively. CCSdiff correlated significantly with maximum zonal tangential curvature (Cspot) (r = 0.83, P < 0.001). CCSdiff at baseline predicted progression over time of Cspot ( P < 0.001). The difference between minimum and maximum stress contribution, CCSdiff, changes in time, and its baseline values predict progression in patients with keratoconus.

In recent years, researchers and policymakers have focused on leaks in water systems as a critical issue because of their negative impact on human society. Most classical methods can only provide approximate leakage locations, typically identifying the general area of a node or pipe section in the network. In this paper, frequency ratio (FR) and deep learning neural network (DLNN) models were used to identify water leaks in water distribution networks (WDNs). Eight predictor variables were used to assess leakage susceptibility in the WDN of Khenchela City, in northeastern Algeria. According to both models, the two most significant predictor variables in the studied WDN are pipe material and age. The predictive ability of these models was evaluated using the receiver operating characteristic (ROC) curve, based on 339 recorded water leakage locations. The results were very satisfactory, with the DLNN model showing slightly higher accuracy than the FR model, achieving area under the curve (AUC) values of 86.7% and 82.3%, respectively. Although the FR model was applied for the first time in this field, it demonstrated strong potential as a decision-support tool for water leak detection.

Polysomnography, used for diagnosing obstructive sleep apnea (OSA), is commonly available with a home sleep apnea test (HSAT) and in-laboratory polysomnography. In-laboratory polysomnography, the gold standard test, is expensive and has a long waitlist, whereas HSATs are inexpensive and sensitive but may not adequately detect respiratory effort-related arousal (RERA), the presence of which results in a higher respiratory disturbance index (RDI) than the apnea-hypopnea index (AHI) and may indicate more severe OSA. In certain patients, identifying factors associated with a higher RDI than AHI may decrease the need for full polysomnography. The present retrospective analytical study of adult patients with OSA diagnosed by in-laboratory polysomnography aimed to evaluate factors associated with a higher RDI than AHI. Factors associated with a higher RDI than AHI were computed via multivariable logistic regression analysis. Of 72 patients with OSA, 27 (37.50%) had an RDI higher than AHI. There were five factors in the predictive model for RDI higher than AHI: Age, sex, hypertension, diabetes, body mass index (BMI) and snoring, tired, observed apnea, high blood pressure, body mass index, age, neck circumference, and gender score. Only BMI was independently associated with a higher RDI than AHI [adjusted odds ratio, 1.10 (95% confidence interval: 1.01, 1.19)] and a BMI of 26.03 kg/m2 had a sensitivity of 81.48% and a specificity of 33.33% in detecting higher RDI than AHI. The receiver operating characteristic (ROC) curve of BMI for RDI higher than AHI was 60.66% (95% confidence interval: 46.53%, 74.78%). Although the area under the ROC curve had modest predictive ability, patients with suspected OSA with a BMI of 26.03 kg/m2 may require in-laboratory polysomnography, whereas other patients may be tested via HSAT.

Brachial artery Doppler as a novel predictor of post-induction hypotension: a prospective observational study.

Exploring nutritional indicators of cardiovascular mortality risk in elderly hypertensive patients: The long-term predictive advantage of PNI.

Gamma knife radiosurgery for cystic vestibular schwannomas: Morphological and dosimetric correlation in a single-institution retrospective study.

To evaluate the utility of tumour size, R.E.N.A.L. (Radius, Exophytic/Endophytic, Nearness to collecting system or sinus, Anterior/Posterior, Location), PADUA (Preoperative Aspects and Dimensions Used for an Anatomical), and SPARE (Simplified PADUA R.E.N.A.L.) nephrometry scoring systems (NSs) in predicting the likelihood of achieving trifecta and pentafecta outcomes, as well as the risk of postoperative acute kidney injury (AKI) and kidney disease progression at 12 months following robot-assisted partial nephrectomy (RAPN). Retrospective review was performed on Institutional Review Board-approved, prospectively maintained database to identify all cases of primary, non-metastatic renal masses who underwent RAPN between 2011 and 2024. All preoperative radiological images were reviewed by a single resident physician, who characterised all tumours based on the NSs. Trifecta outcome was defined as cases with warm ischaemia time of <25 min, negative surgical margin, and absence of complications. Pentafecta included the trifecta outcomes with the addition of >90% renal function preservation and absence of kidney disease progression at 12 months. Logistic regression and receiver operating characteristic (ROC) analyses were completed to determine the predictive ability for the tumour size and each of the NSs. Based on 1787 patients, trifecta and pentafecta were achieved in 52.7% and 22.2%, respectively. All three NSs were established as significant predictors of trifecta, pentafecta, and postoperative AKI, with all demonstrating comparable yet limited predictive performance (area under the ROC curve [AUC] 0.573-0.645). Tumour diameter (AUC 0.649-0.665) was shown to outperform the NSs in predicting the likelihood of achieving trifecta and pentafecta, as well as the risk of AKI. None of the NSs were identified as predictors of kidney disease progression at 12 months. Although lower R.E.N.A.L., PADUA, and SPARE NSs were associated with achieving surgical success and reduced risk of AKI, their predictive performance was modest and can be outperformed by a simpler and more objective measure, such as tumour diameter. Future studies remain necessary to re-evaluate the broader clinical utility of NSs in predicting surgical and functional outcomes, in addition to its original purpose as standardized tools for the anatomical characterization of renal tumours.

The test characteristics of ONSD and ODE tests in predicting the prognosis of patients with traumatic brain injury.

The diagnosis and follow-up of eosinophilic oesophagitis (EoE) currently rely on repeated upper endoscopies (EGD) with biopsies, which are invasive, resource-intensive and environmentally costly. Non-invasive biomarkers for EoE are needed. We investigated the role of blood eosinophils and lymphocytes in the management of EoE. This was a prospective study conducted at four EoE referral centres. Consecutive adults undergoing EGD with biopsies for known or suspected EoE were enrolled. Based on oesophageal peak eosinophil count (PEC) and clinical history, patients were divided into EoE (histologically active or in remission) and non-EoE dysphagia (NED). Prior to the EGD, a full blood count was obtained. Clinical, endoscopic and histologic findings were recorded. Receiver operating characteristic curve analysis was used to assess the predictive ability of blood biomarkers (AUC). We enrolled 209 patients (123 EoE and 86 NED). For the diagnosis of EoE, an absolute eosinophil count (AEC) of 155 eosinophils/mm3 had an AUC of 85%. For the assessment of histological disease activity, an AEC of 325 eosinophils/mm3 had an AUC of 70.5% for the identification of histological remission following treatment. AEC showed a positive correlation with PEC on histology and the EoE endoscopic reference score with Spearman's Rho of 0.4 (p < 0.0001). Eosinophil absolute and relative counts in the peripheral blood could be used in the initial assessment of patients presenting with dysphagia to accurately differentiate EoE from NED and to predict histological remission of EoE.

The comparative performance of the DOAC score versus established bleeding risk scores in patients with atrial fibrillation (AF) receiving direct oral anticoagulants (DOACs) remains uncertain. This meta-analysis evaluated the predictive ability of the DOAC score compared with HAS-BLED and ORBIT. PubMed and Embase were systematically searched to identify studies assessing the predictive performance of the DOAC score in AF patients treated with DOACs. Pooled C-indices were calculated to compare discrimination. Reclassification metrics (net reclassification improvement [NRI], integrated discrimination improvement [IDI]), calibration analyses, and decision curve analyses (DCA) were synthesized qualitatively. Nine studies comprising 12 cohorts were included (n = 89 688). The DOAC score demonstrated significantly superior discrimination for major bleeding compared with HAS-BLED (C-index 0.68 vs. 0.63). No significant differences were observed for intracranial hemorrhage, gastrointestinal bleeding, or clinically relevant non-major bleeding, nor in comparisons with ORBIT. Reclassification analyses showed heterogeneous findings, with several studies reporting no incremental benefit of the DOAC score, although one large cohort demonstrated improved NRI and IDI over HAS-BLED. Calibration analyses revealed good performance across scores, though both HAS-BLED and DOAC tended to overestimate bleeding risk in high-risk groups. DCA suggested variable but occasionally greater net benefit of the DOAC score at clinically relevant risk thresholds. The DOAC score provides modest but statistically significant improvement in predicting major bleeding compared with HAS-BLED, with comparable performance to ORBIT. However, reclassification, calibration, and clinical utility vary across settings, underscoring the need for further prospective validation.

The limited ability of Numerical Weather Prediction (NWP) models to capture nonlinear dynamics and atmospheric uncertainty remains a major challenge in improving tropical cyclone forecasts, particularly over the eastern Indian Ocean. This study evaluates a Multi-Task Learning approach based on several Recurrent Neural Network (RNN) variants, namely LSTM, BiLSTM, GRU, and BiGRU, to simultaneously predict three key cyclone components: position (latitude and longitude), wind intensity, and cyclone category. Historical IBTrACS data from 2000 to 2025 with a 3-hour temporal resolution are used as model input, employing 48-hour sequences to forecast cyclone conditions at lead times of 12, 24, 48, and 72 hours. The results show that all models achieve stable convergence during training. At a 12-hour lead time, the BiLSTM model delivers the best performance, with a mean position error of 83.53 km and a Hit Rate of 0.966, outperforming the other models. For longer lead times (24–72 hours), the BiGRU model demonstrates the most stable positional accuracy, exhibiting the lowest error degradation as the forecast horizon increases. In addition, wind intensity predictions remain robust, with a Mean Absolute Error (MAE) below 4.6 knots up to 72 hours. These findings highlight the potential of multi-output RNN-based models to support more adaptive and efficient tropical cyclone forecasting systems.

Due to changes in temperature and precipitation patterns, aquatic and semi-aquatic plant species are seriously threatened by climate change. This study evaluated how Marsilea minuta L., a small aquatic fern found in tropical and subtropical wetlands, would be affected by climate change across geographic regions. Maximum Entropy (MaxEnt) was used to simulate species distributions using 963 spatially filtered occurrence records and five bioclimatic variables (BIO1, BIO2, BIO6, BIO12, and BIO13), selected after a thorough multicollinearity analysis. The BCC-CSM1.1 general circulation model was used to anticipate future climate scenarios for 2050 and 2070 under Representative Concentration Pathways (RCP) 2.6 and 8.5. The model showed outstanding prediction ability (AUC = 0.91, TSS = 0.71). According to current distribution modeling, M. minuta has a limited climatic niche that is focused between 30°N and 30°S, with South Asia, Southeast Asia, and equatorial Africa providing the best habitat. The most significant predictor was found to be the annual mean temperature, which was followed by precipitation variables and the lowest temperature of the coldest month. With net habitat losses ranging from 7.3% under RCP 2.6 (2050) to 17.2% under RCP 8.5 (2070), future predictions showed progressive range contractions across all scenarios. The gains were limited to isolated areas at higher latitudes, whereas habitat losses were concentrated at range edges. According to limiting factor analysis, the minimum temperature of the coldest month limited 28.3% of areas, mostly at higher latitudes, whereas annual precipitation limited dispersion throughout 34.7% of the investigated areas. The Congo Basin and South Asia were found to be possible climate refugia that might sustain stable, favorable conditions in a variety of scenarios. According to response curve analysis, ideal conditions include low diurnal temperature ranges, frost-free winters, high wet-season precipitation surpassing 1200mm, and an annual mean temperature of 20-25°C. These findings emphasize M. minuta susceptibility to climate change and the necessity of proactive conservation measures, such as safeguarding recognized refugia. Improvement of wetland connectivity and incorporation of climate factors into more comprehensive wetland management initiatives. Because losses under high-emission scenarios significantly outweighed those under strict mitigation paths, the projected range reductions highlight the crucial relevance of greenhouse gas mitigation in limiting biodiversity consequences.

The consumerist behavior of Generation Z students has become an important issue because it relates to financial management ability, lifestyle patterns, and social influence within peer environments. This study aims to analyze the effect of financial literacy and lifestyle on consumerist behavior, with peer conformity as an intervening variable among Generation Z students at Sultan Agung Islamic University Semarang. This study employed a quantitative approach with an explanatory research method. The research sample consisted of 121 Generation Z students selected using the purposive sampling technique. Data were collected through a Likert-scale questionnaire and analyzed using SmartPLS 4.0. The results show that financial literacy and lifestyle have positive and significant effects on peer conformity and consumerist behavior. In addition, peer conformity also has a positive and significant effect on consumerist behavior. The R-Square value indicates that the research model has fairly good predictive ability. These findings confirm that financial literacy, lifestyle, and peer conformity play important roles in shaping the consumerist behavior of Generation Z students. The implications of this study emphasize the importance of improving financial literacy and controlling lifestyle and social influence so that students are able to manage their finances more wisely.

The LPP indexes baseline and treatment-related changes in anxiety sensitivity.

Friction is ubiquitous in daily life, from nanoscale machines to large engineering components. By probing the intricate interplay between system parameters and frictional behavior, scientists seek to unveil the underlying mechanisms that enable prediction and control of friction—an essential step toward carbon neutrality. Yet, reproducing frictional behavior in experiments is notoriously difficult. Here, we experimentally show that this challenge stems from the extreme sensitivity of tribological systems to tiny variations, e.g., in surface topography, typically presumed well controlled. Even after meticulous surface preparation to semiconductor industry standards and curtailing misalignment-induced oscillations, subtle variations remain and interact. In turn, such minute initial differences lead to statistically significant variations in friction and wear, giving rise to system-level chaotic behavior. Yet, by leveraging mid-scale features of surface topography and misalignment-induced oscillations—information often filtered out or overlooked—we established a predictive framework for high-friction regions under vastly different lubrication scenarios. While no single identified descriptor robustly predicts high friction, their combined occurrence provides strong predictive ability, which is further enhanced by machine learning.