Search Results (6,098)

Objective: To evaluate the impact of body composition parameters, including specifically sarcopenic obesity (SO), on postoperative and oncological outcomes in patients undergoing radical cystectomy (RC) for bladder cancer, thereby addressing a paucity of data in this setting. Methods: A retrospective observational study was conducted in patients who underwent RC. Preoperative CT scans were analyzed using semi-automatic segmentation software to assess body composition parameters, with measurements of adipose and muscle tissue obtained at the level of the L3 vertebra. Results: A total of 249 patients were included, of whom 127 (52.5%) met the criteria for sarcopenia, 53 (21.3%) for obesity, and 14 (5.6%) for SO. Multivariate analysis identified previous abdominal surgery (OR 2.56, 95% CI 1.24–5.23, p = 0.011), total serum protein level (OR 0.57, 95% CI 0.36–0.88, p = 0.013), and SO (OR 7.01, 95% CI 1.06–37.05, p = 0.045) as independent predictors of 90-day postoperative complications. Patients with SO experienced significantly higher rates of abdominal wall complications (p = 0.03). However, in multivariate analyses, SO was not associated with overall survival (despite a p value of 0.04 at univariate analysis), cancer-specific survival, or progression-free survival. Conclusions: Preoperative CT-based assessment of body composition is a valuable tool in the surgical evaluation of patients undergoing RC. SO appears to be an independent predictor of short-term postoperative complications and should be considered when planning prehabilitation strategies. Full article

Agriculture plays a crucial role in discussions about environmental challenges because of its ecological footprint and high vulnerability to environmental shocks. To better understand the social and behavioral dynamics among food producers and their perceptions of climate change-related risks, this paper draws on forty-one in-depth, semi-structured interviews with farmers and ranchers in Colorado (USA). Leveraging the concept of social capital, the paper extends the concept analytically in a direction missed by previous research highlighting network structures, such as by focusing on its bonding, bridging, and linking characteristics. Instead, focus centers on the inclusiveness and diversity of values, beliefs, worldviews, and cultural orientations within those networks, arguing that these elements can be just as influential, if not more so in certain instances, than structural qualities. The concept of social capital heterogeneity is introduced to describe a network’s level of diversity and inclusivity. The findings do not question the importance of studying network structures when trying to understand how food producers respond to threats like climate change; an approach that remains useful for explaining social learning, technology adoption, and behavioral change. However, this method misses elements captured through a subjective, interpretivist perspective. With social capital heterogeneity, we can use social capital to explore why farmers and ranchers hold specific values and risk perceptions, peering deeper “within” networks, while tools like quantitative social network analysis software help map their structures from the “outside.” Additionally, social capital heterogeneity provides valuable insights into questions about “effective” agro-environmental governance. The paper concludes by discussing practical implications of the findings and reviewing the limitations of the research design. Full article

Ergonomic assessments for specialized vehicle cockpits are often costly, subjective, or fragmented. To address these issues, this study proposes and validates a quantifiable comprehensive evaluation method combining optical motion capture with simulation. The methodology uses motion capture to acquire accurate, dynamic operator posture data, which drives a digital human model in a virtual environment. A novel assessment framework then integrates the results from six ergonomic tools into a single, comprehensive score using a multi-criteria weighting model, overcoming the ‘information silo’ problem of traditional software. In a case study optimizing a flatbed transporter cockpit, the method guided a redesign that significantly improved the overall ergonomic score from 0.422 to 0.277. The effectiveness of the optimization was validated by a 40% increase in key control accessibility and a significant reduction in electromyography (EMG) signals in the neck, shoulder, and lumbar regions. This study provides an innovative, data-driven methodology for the objective design and evaluation of customized human–machine systems, demonstrating its utility in reducing physical strain and enhancing operator comfort, with broad applicability to other complex industrial environments. Full article

This study aims to classify the Heat Risk Index (HRI), a critical component in climate change adaptation efforts, and to demonstrate how the cooling effect of trees influences HRI levels in areas suitable for afforestation. Istanbul, a global metropolis, was selected as the study area. Spatial analyses were conducted at the neighborhood scale. Within this scope, an afforestation scenario was implemented for a selected neighborhood to explore how HRI values could be reduced. The neighborhood-level approach constitutes the distinctive aspect of this study. The HRI analysis was classified into five levels using three interrelated variables: lack of tree canopy, population density, and land surface temperature (LST). ArcGIS Pro 3.5.2, a geographic information systems software, was employed as the primary analytical tool. The analysis revealed that 24.97% of Istanbul’s neighborhoods fell into the “relatively high” risk category, while 36.45% fell into the “higher–intermediate” risk category. In this context, a critical neighborhood sample from the higher–intermediate risk group, representing the largest proportion, was selected for scenario testing. The scenario demonstrated that a 6% increase in afforestation within the neighborhood lowered its HRI classification by one level. As a result, the method applied in this scenario was proven applicable for use in climate adaptation planning. Full article

This article presents an analysis of a logistics process using process mining methods. Additionally, it highlights the possibilities of analyzing industrial process data using the process mining tools available in the ProM software 6.12. The paper explains what process mining is, its types and perspectives, and its potential applications in business process management. This analysis is based on an event log, while the process model is presented in the form of a Petri net. The process research was carried out using ProM software and its available tools. The dataset is characterized, and the results of the conducted studies are presented, including an analysis of the event log, process flow network analysis, and conformance checking between the model and the event log, as well as time-based, organizational, and error detection analyses. Solutions to the identified problems are proposed. In summary, this article presents an analysis of a logistics process that was generated based on system process data, and demonstrates the possibilities of using selected algorithms from the ProM software. Full article

Background: Rehabilitation of atrophic posterior mandibles using dental implants is often complicated by anatomical limitations, particularly the proximity of the inferior alveolar nerve (IAN). Techniques such as IAN lateralization and transposition enable implant placement but are associated with neurosensory disturbances (NSDs). This systematic review and meta-analysis aimed to assess the incidence, duration, and predictors of NSDs following IAN repositioning for implant placement and to evaluate the effectiveness of adjunctive methods like piezo-surgery and platelet-rich fibrin (PRF) in minimizing complications. Methods: Following PRISMA 2020 guidelines, a comprehensive search of electronic databases and gray literature identified 20 studies, including randomized controlled trials, prospective cohorts, and retrospective analyses published between 2009 and 2024. Outcomes analyzed included incidence of NSDs, recovery rates, implant stability quotient (ISQ), marginal bone loss, and implant success rates. Meta-analysis was performed using RevMan 5.3 software, with heterogeneity and publication bias assessed using standard Cochrane tools. Results: Transient NSDs occurred in 15–40% of cases, with higher rates observed in transposition techniques. Most patients experienced recovery within 6 months. Piezoelectric surgery significantly reduced the incidence and duration of NSDs compared to rotary instruments. Meta-analysis revealed no statistically significant differences between lateralization and transposition techniques in ISQ, marginal bone loss, success rate, or NSDs at 3 months (p > 0.05). PRF was associated with accelerated nerve recovery. IAN repositioning is effective for implant placement in atrophic mandibles with a risk of transient NSDs. Conclusions: Lateralization combined with piezo-surgery and PRF shows favorable outcomes in minimizing nerve injury and optimizing implant success. The PROSPERO registration code is as follows: CRD420251086835. Full article

Public safety personnel (PSP), including correctional workers, firefighters, paramedics, police, and public safety communicators, are at increased risk for posttraumatic stress injury, yet face barriers in receiving timely support. Mobile health (mHealth) applications (apps) offer promising avenues for confidential, on-demand access to relevant information and support. The purpose of this study was to assess implementation of PeerOnCall, a new mHealth platform designed by and for PSP (the platform includes two parallel apps: one for frontline workers and one for peer support providers). A multi-site mixed methods implementation trial was conducted over 3−6 months in 42 public safety organizations across Canada. App usage trends were tracked through software analytics, and facilitators and barriers to app use were explored via interviews with organizational champions. Over 11,300 employees across 42 organizations were invited to use the PeerOnCall app over the trial period, with approximately 1759 PSP (15% of total) downloading the app. Variation within and across sectors was evident in app downloads and feature use. Approaches to communication (mode, timing, and messenger), and organizational culture related to mental health and help outreach affected uptake levels. PeerOnCall is a promising tool to facilitate access to peer support; however, culturally relevant strategies are needed to overcome barriers and integrate this tool into workplace practices. Full article

Background/Objectives: Quantitative analysis of amyloid PET imaging plays a crucial role in diagnosing Alzheimer’s disease (AD), particularly in cases where visual interpretation is equivocal. Multiple commercial software tools are available for this purpose, yet differences in their quantification and diagnostic performance remain understudied, especially for Neurophet SCALE PET. Methods: We retrospectively analyzed ¹⁸F-florbetaben PET/CT scans from 129 patients with cognitive impairment, comprising 39 patients with AD and 90 with non-AD diagnoses, using three software tools: MIMneuro, CortexID Suite, and Neurophet SCALE PET. Standardized uptake value ratios (SUVRs) were obtained for six brain regions known for amyloid accumulation. Diagnostic accuracy was evaluated using ROC curve analysis, while inter-software correlations and reliability were assessed via Pearson correlation coefficients and intraclass correlation coefficients (ICC). Results: All three software programs significantly distinguished AD from non-AD patients in most brain regions. MIMneuro and Neurophet SCALE PET demonstrated the highest diagnostic performance, with MIMneuro achieving an AUC of 1.000 in the anterior cingulate gyrus. While MIMneuro and Neurophet SCALE PET showed moderate-to-strong SUVR correlations (r = 0.715–0.865), CortexID Suite showed limited correlation with the other tools. Inter-software reliability was moderate only in selected regions (ICC ≈ 0.5), indicating potential variability in SUVR measurements across platforms. Conclusions: MIMneuro, CortexID Suite, and Neurophet SCALE PET are effective for the semi-quantitative analysis of amyloid PET and can aid in the diagnosis of AD. However, clinicians should be cautious when interpreting SUVRs across different software tools due to limited inter-software consistency. Standardization efforts or consistent use of a single platform are recommended to avoid diagnostic discrepancies. Full article

Background/Objectives: Moderate or severe asthma exacerbations may require pharmacological interventions in addition to standard treatment. In this context, magnesium sulfate has been proposed as a second-line therapeutic option, owing to its physiological effects on bronchial smooth muscle. Therefore, the objective of this meta-analysis is to determine the effectiveness of intravenous or nebulized magnesium sulfate in patients with a moderate-to-severe asthmatic crisis. Methods: This systematic review and meta-analysis included randomized controlled trials published between 1990 and 2024, using the PubMed, Scopus, Science Direct, Web of Science, LILACS, Cochrane Library, Springer, and Scielo databases. The risk of bias was assessed using the RoB 2 tool, the quality of evidence with the Jadad scale, and the certainty of the evidence per outcome was evaluated following the GRADE guidelines. The meta-analysis was developed using the statistical software Jamovi 2.3.28^® and RevMan 5.4^®. Results: Fourteen studies with a total of 2242 patients with a moderate-to-severe asthmatic crisis were included. Of these, ten studies evaluated the severity score, eight evaluated hospitalization, five evaluated the length of the hospital stay, and three evaluated intensive care unit admission. The meta-analysis demonstrates that the use of magnesium sulfate is associated with a statistically significant reduction in the risk of hospitalization (RR: 0.79, 95% CI: from 0.67 to 0.94, p = 0.02). However, no effects were observed on the severity score (SMD: −0.37, 95% CI: from −0.92 to 0.17, p = 0.16), the length of the hospital stay (SMD: −0.75, 95% CI: from −1.90 to 0.40, p = 0.14), or admission to intensive care units (RR: 0.62, 95% CI: from 0.28 to 1.36, p = 0.23). Subgroup and sensitivity analyses did not yield significant findings or produce any modification of the effect. Conclusions: Magnesium sulfate reduces hospitalizations in moderate-to-severe pediatric asthma, although it does not improve other relevant clinical outcomes. Full article

Background: Monitoring autonomic balance provides valuable insights into recovery status and physiological readiness, both of which are essential for performance optimization in athletes. The Stress Score (SS) and Sympathetic–Parasympathetic Ratio (SPS), derived from Poincaré plot heart rate variability (HRV) indices, have been proposed as practical markers of sympathetic activity and overall autonomic balance. However, these traditional calculations often require lengthy recordings and specialized software, limiting their feasibility in field settings. This study introduces modified versions of these metrics derived from ultra-short-term (1 min) time–domain HRV recordings: the Modified Stress Score (MSS) and Modified Sympathetic–Parasympathetic Ratio (MSPS). Methods: Competitive male athletes (n = 20, age = 21.2 ± 2.1 year, height = 183.6 ± 8.9 cm, weight = 79.2 ± 10.3 kg) completed a maximal exercise test with HRV recorded before and after exercise. Results: Following natural log-transformation, MSS and MSPS demonstrated strong correlations with SS and SPS across all time points (r = 0.87–0.94, all p < 0.01) and displayed the expected physiological responses to exercise and recovery. Conclusions: These findings suggest that MSS and MSPS are practical, accessible tools for assessing autonomic balance in athletes. Their application may enhance our ability to monitor recovery status, guide individualized training strategies, and optimize performance in applied sport settings. Full article

Introduction: Decision-making regarding radiotherapy techniques for patients with nasopharyngeal cancer requires a comparison of photon and proton plans generated using planning software, which requires time and expertise. We developed a fully automated decision tool to select patients for proton therapy that predicts proton therapy (XT) and photon therapy (PT) dose distributions using only patient CT image data, predicts xerostomia and dysphagia probability using predicted critical organ mean doses, and makes decisions based on the Netherlands’ National Indication Protocol Proton therapy (NIPP) to select patients likely to benefit from proton therapy. Methods: This study used 48 nasopharyngeal patients treated at the Cancer Hospital of the Chinese Academy of Medical Sciences. We manually generated a photon plan and a proton plan for each patient. Based on this dose distribution, photon and proton dose prediction models were trained using deep learning (DL) models. We used the NIPP model to measure xerostomia levels 2 and 3, dysphagia levels 2 and 3, and decisions were made according to the thresholds given by this protocol. Results: The predicted doses for both photon and proton groups were comparable to those for manual plan (MP). The Mean Absolute Error (MAE) for each organ at risk in the photon and proton plans did not exceed 5% and showed a good performance of the dose prediction model. For proton, the normal tissue complication probability (NTCP) of xerostomia and dysphagia performed well, p > 0.05. There was no statistically significant difference. For photon, the NTCP of dysphagia performed well, p > 0.05. For xerostomia p < 0.05 but the absolute deviation was 0.85% and 0.75%, which would not have a great impact on the prediction result. Among the 48 patients’ decisions, 3 were wrong, and the correct rate was 93.8%. The area under curve (AUC) of operating characteristic curve (ROC) was 0.86, showing the good performance of the decision-making tool in this study. Conclusions: The decision tool based on DL and NTCP models can accurately select nasopharyngeal cancer patients who will benefit from proton therapy. The time spent generating comparison plans is reduced and the diagnostic efficiency of doctors is improved, and the tool can be shared with centers that do not have proton expertise. Trial registration: This study was a retrospective study, so it was exempt from registration. Full article

The present paper introduces a novel methodology for LiDAR point transformation and adjustment, grounded in two primary concepts. In the initial phase of the process, LiDAR data are mapped onto synthetic images, known as lidargrams, through the utilization of exterior orientation parameters (EOPs) of a virtual camera. Secondly, unique lidargram point identifiers (ULPIs) are assigned to each LiDAR point, ensuring the preservation of the relationship between specific LiDAR points and their corresponding lidargram projections. This process facilitates the reconstruction of ground points from their respective projections. The integration of these concepts facilitates the alignment and adjustment of blocks of lidargrams, thereby enabling the estimation of novel EOPs. The exchange of arbitrary EOPs and the intersection of the transformed point cloud based on the ULPIs are facilitated by these refined EOPs. The LiDAR data undergo a three-dimensional transformation using photogrammetric algorithms. This is in accordance with the fundamental principles of lidargrammetry. The accuracy of the new approach and its implementation in a research tool were verified on a range of data types, encompassing synthetic, semisynthetic, and real data. By evaluating the approach across a wide range of data sources, the authors were able to assess its effectiveness and reliability in different scenarios. The method’s flexibility is evidenced by its ability to reduce the final 3D root mean square error of discrepancies measured at check points by 30 times in synthetic data tests, 12 times in semisynthetic data tests, and 96 times in real data tests. The quantitative results obtained provide substantial support for the validity of the presented methodology. The efficacy of the proposed method was also evaluated by way of a comparative analysis with a selection of widely utilized LiDAR processing software developed by TerraSolid Ltd. Full article

Background: Structural variants (SVs) play a significant role in gene function and are implicated in numerous human diseases. With advances in sequencing technologies, identifying SVs through whole-genome sequencing (WGS) has become a key area of research. However, variability in SV detection persists due to the wide range of available tools and the absence of standardized methodologies. Methods: We assessed the accuracy of SV detection across various short-read (srWGS) and long-read (lrWGS) sequencing technologies—including Illumina short reads, PacBio long reads, and Oxford Nanopore Technologies (ONT) long reads—using deletion calls from the HG002 benchmark dataset. We examined how variables such as variant calling algorithms, reference genome choice, alignment strategies, and sequencing coverage influence SV detection performance. Results: DRAGEN v4.2 delivered the highest accuracy among ten srWGS callers tested. Notably, leveraging a graph-based multigenome reference improved SV calling in complex genomic regions. Moreover, we proved that combining minimap2 with Manta achieved performance comparable to DRAGEN for srWGS. For PacBio lrWGS data, Sniffles2 outperformed the other two tested tools. For ONT lrWGS, alignment with minimap2—among four aligners tested—consistently led to the best results. At up to 10× coverage, Duet achieved the highest accuracy, while at higher coverages, Dysgu yielded the best results. Conclusions: These results show for the first time that alignment software choice significantly impacts SV calling from srWGS, with results comparable to commercial solutions. For lrWGS, the performance depends on the technology and coverage. Full article

At Stavanger University Hospital (SUH), patients aged 50 years and above with a fracture after a fall are included in our Fracture Liaison Service (FLS) at the orthopaedic department, due to their high imminent fracture risk. The FLS at SUH keeps a quality registry, including index fractures, fall from standing/walking, preventive factors, Dual Absorptiometry X-ray (DXA) results and treatment status, in addition to risk factors such as chronic diseases. As in many other hospitals and countries, the capacity of the DXA scanner at SUH does not meet the needs of the ageing population. As such, FLS patients should be prioritised for DXA scanning according to their need for anti-osteoporotic treatment. The aims of this study were (1) to identify whether any risk factors are more strongly associated with osteoporosis than others, and (2) to use this information as a tool to prioritise patients for which the decision to initiate anti-osteoporotic treatment should be assessed by a DXA scan. Method: We used software from CheckWare to keep a structured health record, submitting journal text to the health record and data to our fracture quality registry from 1 June 2022 to 31 December 2024. The fracture coverage of the registry, as part of the medical record, was 100%. Both men and women aged over 50 years with fragility-related fractures were included in the analysis, with index fracture having been reported within 24 months prior to FLS assessment. Exclusion criteria: short life expectancy (<3 years), already started on anti-osteoporotic treatment, living in nursing home, age >97 years, or multi-trauma patients. Statistics were calculated using SPSS and logistic regression. The results are presented as odds ratio (OR) and 95% confidence interval (95% CI). Significant differences were considered at a p-value of <0.05. Results: A total of 6974 patients were included, 81% of which were female. After the DXA scan, 5307 of the patients were started on anti-osteoporotic treatment (76%). Patients aged 50–70 years were the largest group. Female patients or those aged 80 years or older had an increased odds ratio (OR) of starting treatment after a fracture. The index fractures included in the logistic regression analysis and were most likely to initiate anti-osteoporotic treatment in the FLS, were vertebral fracture (p < 0.000, OR 3.1, 95% CI: 2.4–4.0), hip fracture (p < 0.000, OR 2.60, 95% CI: 1.9–3.5), costa fracture (p-value = 0.028, OR:1.3, 95% CI:1.0–1.5), pelvic fracture (p-value < 0.000, OR 3.1, 95% CI: 1.8–5.1). Patients with lack of sufficient vitamin D had increased odds with OR of 1.7 (p-value < 0.00, 95% CI: 1.3–2.2) for having osteoporosis compared to the other FLS patients. Fall from standing, walking or sitting increased the odds for osteoporosis treatment (p-value < 0.000, OR 2.8, 95% CI: 2.3–3.3). Conclusions: The listed risk factors for needing treatment were high for most fractures, especially vertebral, hip, and pelvic fractures. Patients aged 80+ years and with a fracture from standing/walking could also start treatment directly, without waiting for a DXA scan. Thus, these patients should be shifted rapidly to FLS and started on treatment without delay. In this way, DXA scanning can be prioritised for patients for whom supporting information is needed regarding the decision to initiate anti-osteoporotic treatment, such as those with proximal humerus, wrist, or ankle fractures. Time to DXA scan could be shortened for these patients and 12 weeks may be achievable. Full article