Conventional Analysis Research Articles

Temporal trends of 46 pesticides and 8 transformation products in surface and drinking water in Québec, Canada (2021-2023): Potential higher health risks of transformation products than parent pesticides.

Temporal trends of 46 pesticides and 8 transformation products in surface and drinking water in Québec, Canada (2021-2023): Potential higher health risks of transformation products than parent pesticides.

Age-related ten-year outcomes after percutaneous coronary intervention of in-stent restenosis.

Older patients are often underrepresented in clinical trials investigating the treatment of coronary drug-eluting stent (DES) restenosis, but outcome data is urgently needed in an ageing society. Thus, the aim of this observational, retrospective study was to close this lack of evidence. Between January 2007 and February 2021, 3511 patients with 5497 in-stent restenosis (ISR) lesions were treated at two large-volume centers in Munich, Germany. We compared the rates of cardiac death, myocardial infarction (MI) and repeat revascularization in 1105 patients (31.5%) older than 75years with 2406 patients (68.5%) younger than 75years. Survival was analyzed using the Kaplan-Meier method. Differences between the groups were tested with the log-rank test. Conventional multivariable analysis with adjustment for relevant variables was performed. Older patients were more frequently female (30.1% vs. 17.9%, p<0.001) and presented less frequently with stable angina (67.8% vs. 72.0%, p<0.001). After 10years, the rates of cardiac death were 56.8% in older patients and 27.4% in younger patients (HRadj, 2.45 [95% CI, 2.09-2.88], p<0.001). Accounting for the risk of death, no difference was found regarding the rates of MI while target lesion revascularization (TLR) occurred less frequently in older patients (24.2% vs. 33.7%; HRadj, 0.77 [95% CI, 0.66-0.89], p<0.001). In the long-term, rates of cardiac death after percutaneous coronary intervention of DES-ISR were higher and TLR rates were lower in patients older than 75years. There was no difference in the rates of MI.

Probing the generation, reactivity, and kinetics of high-valent manganese-oxo phthalocyanines: Insights into oxidation mechanisms.

Probing the generation, reactivity, and kinetics of high-valent manganese-oxo phthalocyanines: Insights into oxidation mechanisms.

Conventional and advanced exergy analysis of a dual-evaporator transcritical CO2 refrigeration system enhanced with ejectors

Conventional and advanced exergy analysis of a dual-evaporator transcritical CO2 refrigeration system enhanced with ejectors

A transdisciplinary integrated approach to improve identification outcomes for decomposed decedents in medicolegal death investigations.

A transdisciplinary integrated approach to improve identification outcomes for decomposed decedents in medicolegal death investigations.

Conventional and advanced exergy analysis of a novel SOFC-MGT hybrid power system coupled with external solar methane hydrogen production process

Conventional and advanced exergy analysis of a novel SOFC-MGT hybrid power system coupled with external solar methane hydrogen production process

Attitudes, barriers, and facilitators toward tools supporting appropriate prescribing among healthcare professionals: a cross-sectional study

Abstract Purpose Potentially inappropriate prescriptions are associated with an increased risk of drug-drug interactions, adverse events, and unfavorable clinical outcomes, especially in older adults. Although different tools to improve appropriate prescribing have been developed to support healthcare professionals, their application and the barriers to their use remain insufficiently explored. This study aimed to assess Italian healthcare professionals’ knowledge of these tools and identify obstacles to their adoption. Methods The study used a purposefully designed questionnaire to assess knowledge, adoption, and barriers related to appropriateness tools. The tools included were identified through a literature review and subsequently refined via expert consensus. Open-ended responses were analyzed using a conventional content analysis approach, and the analyses focused on differences across professional groups. Results The survey collected 657 responses from pharmacists (35%), nurses (26%), general practitioners (22%), geriatricians/internists (9%), and other physicians (8%). The Beers and STOPP/START criteria were used by 38% and 34% of participants, respectively, with geriatricians and other physicians being the primary users. Additionally, 34% of participants reported using specific software integrated into their institutional computer systems. Among 294 respondents identifying barriers to appropriate prescribing, the most common were lack of time (14%), lack of knowledge (10%), and accessibility/costs of digital tools (8%). Key facilitators included specific training (38%), integrated software/apps (29%), and more time with patients (11%). Conclusions The adoption of tools supporting appropriate prescribing remains limited among healthcare professionals in Italy, with significant differences among professionals. Policymakers and healthcare institutions should focus on education, interprofessional collaboration, and user-friendly digital solutions to improve prescribing process and patient safety.

Climate Refugees: A Lacuna In A Non-existent Refugee Law

In a technological advancing world, climate-induced displacement has been escalating for years now, yet climate refugees still lack legal recognition under both and Indian and international frameworks. Although the 1951 Refugee Convention defines refugees as those individuals who are fleeing persecution based on race, religion, nationality, social group, or political opinion, the convention has failed to including those displaced by climatic events such as floods, droughts, rising sea levels, etc. The title of this paper, “Climate Refugees: A Lacuna In A Non-existent Refugee Law” is a rhetorical emphasis to address the significance of the subject matter that has been ignored for far too long. Thus, the title is deliberately provocative to address the dual failure in the part of the policy-makers of the country, leading the readers to engage with the rhetoric with their own suggestions as this paper examines the absence of refugee laws in India, which has not ratified the 1951 Convention, leaving refugees without formal recognition or protection. It would also highlight the legal gaps concerning climate refugees through an analysis of international conventions, regional agreements, and case studies, underlining the urgent need for legal reforms in the subject. The paper further highlights potential solutions for the same including climate humanitarian visas, the Model International Mobility Convention, and human rights law that protect displaced populations. By focusing on the complexities and challenges present in addressing climate-induced displacement, this paper aims to find potential solutions for those seeking refugee from displacement due to climate change.

An in-Depth Review of Cardiovascular Disease Prognosis Using Algorithms Based Upon Artificial Intelligence

High mortality rates are a result of cardiovascular diseases (CVDs), which pose serious global health challenges. It is possible to decrease the risk of having an acute myocardial infarction and the mortality rate among people with cardiovascular diseases by promptly detecting cardiovascular events. The multifaceted pathological techniques and diverse determinants engaged in the menace assessment of CVDs, heart attacks, interpretations of medical imaging, therapeutic decisionmaking, and diagnosis of disease necessitate revisions to conventional data analysis methods. Artificial intelligence (AI) is a term used to describe technology that uses complex computer algorithms to analyse massive amounts of data. AI is now widely used in the medical field. AI methods have proven to be able to diagnose and treat a variety of CVDs more quickly, including hypertrophic cardiomyopathy, congenital heart disease, valvular heart disease, atrial fibrillation, and heart failure. We examined 92 papers from reliable sources, including Google Scholar, Springer, Elsevier, and others, for this thorough review. AI has shown great promise in clinical settings for the diagnosis of cardiovascular diseases, the improvement of supporting tools, the classification and stratification of disorders, and the prediction of outcomes. Intellectual AI systems have been carefully designed to examine complicated relationships in large amounts of healthcare data, enabling them to perform more complex jobs than traditional methods.

Decoding dynamic brain networks in Parkinson’s disease with temporal attention

Detecting brief, clinically meaningful changes in brain activity is crucial for understanding neurological disorders. Conventional imaging analyses often overlook these subtle events due to computational demands. IMPACT (Integrative Multimodal Pipeline for Advanced Connectivity and Time-series) addresses this challenge by converting 3D/4D fMRI scans into time-series signals using a standardized brain atlas. This approach integrates regional signals, network patterns, and dynamic connectivity, and employs machine learning to detect subtle fluctuations. In Parkinson’s disease diagnosis across two independent cohorts (n=43 and n=40), it achieves high accuracy (area under the curve = 0.97-0.98), outperforming conventional methods. Analyses reveal transient connectivity disruptions that align with dopaminergic mechanisms, while interpretability highlights the critical time windows and regions driving classification. This reproducible, standardized pipeline is readily adaptable to other conditions where short-lived brain changes serve as key diagnostic markers.

Improving the policy-making and executive structure of the child welfare system in Iran, a qualitative content analysis study

ABSTRACT Despite the government’s pivotal role in protecting children, Iran still lacks a defined, integrated system for child welfare. Therefore, this study aims to identify the policy-making and executive requirements needed to achieve this goal. This study applied the conventional qualitative content analysis to in-depth interviews conducted with child welfare experts in Iran from April 2019 to May 2020. The study’s findings identified concepts that framed the proposed policy-making and implementation framework for the child welfare system, categorized into two main groups: “the foundation and infrastructure of the child welfare system” and “ the executive structure of the child welfare system.”

Machine learning based prediction of cognitive metrics using major biomarkers in SuperAgers

As populations age, understanding cognitive decline and age-related diseases like dementia has become increasingly important. “SuperAgers,” individuals over 65 with cognitive abilities similar to those in their 40s, provide a unique perspective on cognitive reserve. This study analyzed 55 blood biomarkers, including cellular components and metabolism/inflammation-related factors, in 39 SuperAgers and 42 typical agers. While conventional statistical analyses identified significant differences in only four biomarkers, advanced feature selection and machine learning techniques revealed a broader set of 15 key biomarkers associated with SuperAger status. A predictive model built using these biomarkers achieved an accuracy of 76% in cognitive domain prediction. To address the limitation of small sample sizes, data augmentation leveraging large language models improved the model’s robustness. Shapley Additive exPlanations (SHAP) provided interpretability, revealing the impact of specific blood factors on cognitive function. These findings suggest that certain blood biomarkers are not only associated with cognitive performance but may also serve as indicators of cognitive reserve. By utilizing simple blood tests, this research presents a clinically significant method for predicting cognitive function and identifying SuperAger status in healthy elderly individuals, offering a foundation for future studies on the biological mechanisms underpinning cognitive resilience.

Four Different Artificial Intelligence Models Versus Logistic Regression to Enhance the Diagnostic Accuracy of Fecal Immunochemical Test in the Detection of Colorectal Carcinoma in a Screening Setting.

This study aimed to evaluate the diagnostic accuracy (DA) of four artificial intelligence (AI) models compared to logistic regression (LR) in enhancing the performance of the fecal immunochemical test (FIT) for the detection of colorectal carcinoma (CRC). The study cohort comprised 544 patients with colorectal neoplasia (CRN), including 58 CRC and 486 non-CRC cases, recruited from the Barretos Cancer Hospital. Each patient provided three consecutive fecal samples, which were analyzed using two fecal occult blood (FOB) assays: ColonView-FIT (CV) and HemoccultSENSA. Four AI models - gradient boosting machine (GBM), neural network (NN), random forest (RF), and support vector machine (SVM) - were developed, incorporating clinical features and CV results. Diagnostic performance was assessed via hierarchical summary receiver operating characteristic (HSROC) curves. In conventional analysis, the area under the curve (AUC) values for different AI models ranged from 0.926 to 0.977, while the highest AUC values were reached by gradient boosting machine (GBM), neural network (NN), and random forest (RF) models (0.974, 0.976 and 0.977, respectively). In the HSROC analysis, the AUC values for i) 'low risk' variables, ii) 'high risk' variables, and iii) AI models were as follows: i) AUC=0.503 (95% CI=0.390-0.613), ii) AUC=0.773 (95% CI=0.713-0.837), and iii) AUC=0.958 (95% CI=0.930-0.989). In all comparisons of the AUC values, the difference was highly significant (p<0.0001). AI models outperformed conventional LR and non-AI diagnostic features in improving FIT-based CRC screening. This is the first study to show that combining clinical data with FIT results in AI frameworks can significantly improve diagnostic accuracy in CRC screening.

Differentiation of canine and feline neoplasms using multi-modal imaging and machine learning

Canine/feline (sub-)cutaneous tumors, which include lipomas, mastocytomas and soft tissue sarcomas, introduce diagnostic challenges due to inherent tissue heterogeneity, accompanied by diverse clinical pathogenesis. Current study integrates conventional imaging techniques optical (white light and autofluorescence) as well as high frequency ultrasound imaging to train machine learning classifiers: linear discriminant analysis, support vector machine and random forest. Study resulted in ~ 100% classification efficiency between benign lipoma and combined mastocytoma and sarcoma tissues for all the classifiers. For the differentiation between mastocytoma and sarcoma tumors, both support vector machine and random forest outperformed conventional linear discriminant analysis classifier. Support vector machine displayed the highest classification efficiency for bimodal groups: (i) ultrasound + fluorescence and (ii) ultrasound + white light as well as (iii) fluorescence + white light. However, it failed for trimodal ultrasound + optics combination, indicating possible upper limit for imaging mode addition. The multimodal effect was obtained using both statistically significant set of features as well as optimal set of features, determined using sequential feature addition. Resulting classification efficiency for combined ultrasound + fluorescence approach was > 85% and even higher for ultrasound + white light or ultrasound + optics multimodal approaches reaching ~ 95%. In the classification of mastocytoma and sarcoma, support vector machine classifier was able to detect significant (p < 0.05) multimodal effect for bimodal groups of: (i) fluorescence + white light, (ii) ultrasound + fluorescence and (iii) ultrasound + white light. On the contrary, random forest demonstrated relevant increment only for the combination of fluorescence and white light. Inferior features of ultrasound or fluorescence have been evaluated to be competitive with the features of highly-efficient white light as they were automatically selected during the process of feature optimization. In addition, another phenomenon of manifestation of multimodality has been observed: in multimodal groups, ultrasound features tended to substitute the features of white light, not just simply be added to them. Multimodal approach was determined to be highly-required for the classification of heterogeneous mastocytoma and sarcoma tumors, which display more similar morphological characteristics. However, when differentiating very distinct lipomas from mastocytomas or sarcomas, the multimodal approach was not a requisite.

The challenges of prenatal care services during the COVID-19 pandemic: qualitative evidence of primary health care providers’ perspective in Iran

BackgroundThe COVID-19 pandemic affected almost all healthcare services, including prenatal care. Providing care to pregnant women during the pandemic was affected by general policies related to the control of COVID-19 and thus faced many challenges. Exploring the challenges of prenatal care during the pandemic can be helpful for effective planning and interventions to reduce the fundamental challenges. The present study aimed to explore the midwives’ perception of care challenges during the COVID-19 pandemic.MethodsThis qualitative research was conducted using conventional content analysis in 13 health centers of Hamadan, Iran, between July and September 2023. A semi-structured interview was conducted with 13 midwives who had experience providing prenatal care services to pregnant mothers during the COVID-19 pandemic and were selected using the purposive sampling method. Granheim and Lundman’s 5-step content analysis approach was used for data analysis.ResultsThree themes describing challenges to prenatal care during the COVID-19 pandemic emerged, including the unfamiliarity and unpreparedness of the health system in facing COVID-19, disruption in the quantity and quality of services, and socio-economic and systemic challenges.ConclusionPNC providers have experienced various challenges during the current COVID-19 pandemic. The results of the present study can be used to reduce the problems and challenges of providing prenatal care services in future pandemics. Effective interventions such as strong and managed organizational support, maintaining and sustaining the midwifery staffing, empowering PNC providers, supplying personal protective equipment, adopting strategies to improve mental health, and considering special measures and various incentives for midwives are necessary to overcome similar crises in the future.

Clinical Findings in Temporal Lobe Epilepsy Associated With Isolated Amygdala Enlargement.

Mesial temporal lobe epilepsy (mTLE) infrequently presents with isolated amygdala enlargement (AE), but its relevance remains ambiguous. We therefore investigated clinical, imaging, and histopathological findings in mTLE-AE compared to non-lesional mTLE (mTLE-NL) patients, and additionally strategies for identifying AE. We detected AE by automated volumetry of otherwise unremarkable magnetic resonance images of mTLE patients, compared with a healthy comparator. Autoimmune inflammation as an AE cause was excluded using the Graus criteria. We compared clinical and neuropsychological variables between mTLE-AE and mTLE-NL. Secondary assessment of AE was by neuroradiologist visual detection. Of 63 mTLE patients, 15 had mTLE-AE. In these, normalized mean volume was 1857.58 mm3 (SD = 207.38) for the left, 1973.09 mm3 (SD = 214.91) for the right amygdala, 2003.34 mm3 (SD = 218.85) for the larger and 1827.34 mm3 (SD = 179.85) for the smaller amygdala. Mean volume in the healthy control subjects was 1853.4 mm3 for the left (SD = 212.44) and 1895.2 mm3 for the right amygdala (SD = 224.29). Clinical parameters including age, sex, epilepsy duration, history of febrile convulsions, drug resistance, neuropsychological performance, surgical outcome, and medications did not differ significantly between mTLE-AE and mTLE-NL. Histopathological findings in mTLE-AE included dysmorphic neurons, potential tumors, and focal cortical dysplasia. Neuroradiologists independently described AE in 37 of 63 mTLE patients. mTLE-AE has no specific clinical profile compared to non-lesional mTLE and features diverse underlying pathologies. Volumetric detection appears more conservative than conventional qualitative visual analysis, but may miss cases of subtle AE. Combining automated volumetry with visual assessment may improve AE detection.

The secret life of mathematical concepts

Abstract This article features a theoretical example—a seemingly simple example that possesses, nonetheless, the potential to advance theory. Since the example radiates with the glow of silence and movement, it resists conventional analysis. Using words and images to trouble the grammar of an orderly world with hierarchal relations and depersonalized language commonly found in conventional analyses, the article aligns with a grammar of animacy to follow the agentive, sensorial, and relational encounter between an elementary student and the secret life of fractions and geometry concepts. The article offers two-way implications for research and practice, namely: (a) “what’s in it that could have an effect on my research” and (b) “how am I implicated in the effect of my own research.”

Probabilistic failure assessment of bolt-stabilized pro-dip slopes considering shear effect

This study presents a probabilistic failure assessment model for bolt-stabilized pro-dip slopes, explicitly addressing the critical yet overlooked shear effect in conventional stability analyses. Conventional analytical approaches oversimplify the mechanical interactions between sliding slope masses and stabilizing bolts through the assumption of uniaxial tensile loading, thereby neglecting the combined tensile-shear stress states observed in real-world scenarios. To bridge this gap, the proposed model integrates force and moment equilibrium analyses, adopts the von Mises strength criterion for bolt rupture assessment, and systematically incorporates bedrock stratigraphy through differentiated parameter assignments. A comprehensive Monte Carlo simulation framework quantifies failure probabilities across three dominant modes: tensile-shear failure of bolts, rock-grout bond failure, and grout-bolt bond failure. Validation through the Freeway No.3 landslide case study demonstrates that shear-inclusive calculations yield significantly higher failure probabilities (exceeding 80% underestimation in shear-neglected models under high groundwater conditions). Parametric analyses reveal distinct behavioral regimes governed by bolt inclination, fixed length, and hydrological factors. The results emphasize that shear effect fundamentally alters failure mode dominance, with bolt failure becoming predominant under realistic stress conditions. This work provides a critical advancement in reliability-based design for pro-dip slopes, offering engineers a tool to mitigate catastrophic risks through physics-informed probabilistic assessments.

Altuvoct: Innovative Medicinal Products Benefit from Innovative Approaches to Regulatory Assessment

Efanesoctocog alfa (Altuvoct; BIVV001) is a fusion protein comprising domains of (i) factor VIII, (ii) the von Willebrand factor, and (iii) IgG1 coupled to two polypeptide linkers. The half-life of efanesoctocog alfa in plasma is about 40 h. The polypeptide linkers are released by thrombin activation, resulting in an active form of efanesoctocog alfa that results in the formation of a fibrin clot. Data from two single-arm ongoing studies were submitted: the XTEND-1 study enrolled 159 subjects aged 12–72 years, and the XTEND-kids study enrolled 74 subjects aged &lt;12 years; all subjects had severe haemophilia A. Single-arm studies are not amenable to conventional statistical analysis of ‘effect of cause’, and so a supplementary analysis was conducted on the basis of ‘cause of effect’, making use of the scheme described by Toulmin coupled to an analysis of causal inference. Overall, the claim that Altuvoct is indicated to treat people aged ≥2 years with severe (and moderate) haemophilia A was considered to be supported by the results of the submitted studies and associated modelling exercises; the benefit–risk evaluation of Altuvoct was found to be positive in the target population.

Acceptability and applicability of using virtual reality for training mass casualty incidents- a mixed method study

BackgroundBecause health professionals can end up being first responders to a mass casualty incident, they must train to improve preparedness and increase the preconditions of victim outcomes. Training and learning on how to handle a mass casualty incident is traditionally based on reading, lectures, training through computer-based scenarios, or sometimes through live simulations. Professionals should practice in realistic environments to narrow the theory–practice gap, and the possibility of repeating the training is important for learning. Virtual reality is a promising tool for realistic and repeatable simulation training, but it needs further evaluation. This study aimed to describe the acceptability and applicability of using VR for training in mass casualty incidents.MethodsA mixed-methods evaluation design was used, where the qualitative and quantitative findings were embedded into the discussion with a realist inquiry approach. A virtual reality simulation with mass casualty incident scenarios, named GoSaveThem (www.crash.nu), was used, and the participants were directed to perform triage. After the simulation, the participants filled in a questionnaire with open-ended questions and ratings on technical aspects, learning experiences, and improvement of preparedness. Eleven of the participants underwent interviews. The qualitative data was analyzed either summarily or with a conventional content analysis. Data were extracted from computer recordings of how long it took for each participant to triage the first 10 victims and to what extent the triage for the first 10 victims was correct. Descriptive statistical analyses were done, and a comparison was made to see if there were any differences between age, sex, educational background, and previous experiences that affected the outcome of triaging.ResultsTraining with virtual reality enables repeatable and realistic simulation training of mass casualty incidents. The participants expressed motivation to repeat the training and experience expanded virtual reality scenarios. This study shows that the acceptability and applicability of using VR for training MCIs were high overall in all examined dimensions for most users, with some exceptions.