Search Results (118)

Objectives: Metabolic dysfunction-associated steatohepatitis (MASH) is a leading cause of cirrhosis within the spectrum of metabolic dysfunction-associated steatotic liver disease (MASLD). However, the prognostic impact of diabetes mellitus (DM) in MASH-associated cirrhosis remains unclear. This study aimed to compare clinical outcomes between cirrhotic patients with and without DM. Methods: Patients with MASH-related cirrhosis were stratified into DM (DM-MASH) and non-DM (non-DM MASH) groups. The diagnosis of MASH was based on histological evidence of steatohepatitis with underlying metabolic dysfunction. The non-DM group included both obese individuals and lean/normal-weight individuals with ≥1 metabolic risk factors. Mortality, liver-related events (LREs; ascites, variceal bleeding, encephalopathy, and hepatocellular carcinoma), and sarcopenia were compared using Kaplan–Meier analysis, log-rank tests, and Fisher’s exact test. Risk factors for sarcopenia were assessed using logistic regression. Results: Median survival was significantly shorter in DM-MASH patients compared to non-DM MASH patients (1523 vs. 2618 days; p < 0.05). The incidence of LREs during follow-up was also higher in the DM-MASH group. The prevalence of sarcopenia was significantly greater among DM-MASH patients (36.1% vs. 19.7%; p < 0.05). In multivariate analysis, DM emerged as an independent predictor of sarcopenia in patients with MASH-related cirrhosis. Conclusions: DM is associated with worse outcomes in MASH-driven cirrhosis, including increased sarcopenia and reduced survival. DM may serve as a prognostic marker for identifying high-risk patients with MASH-associated cirrhosis. Full article

Despite its economic profitability, fur farming in Europe, responsible for half of global production, faces a growing ethical backlash. Animal welfare concerns, particularly regarding mink, foxes, and raccoon dogs kept in restrictive cages, have intensified due to advocacy, scientific reviews, and COVID-19 outbreaks. In response, several EU nations have implemented bans or stricter regulations. However, limited research exists on EU public opinion. This study analyses data from Eurobarometer 533 (March 2023), surveying 26,368 citizens across 27 EU countries, to assess attitudes toward fur farming. Respondents selected from three policy preferences: a full ban, EU-wide regulation, or acceptance of current practices. Multinomial logistic regression and chi-square tests revealed significant socio-demographic and ideological influences. Older individuals were more supportive of current practices (p = 0.001), while higher education levels correlated with support for a ban or stricter regulation (p = 0.003). Income positively influenced support for regulation (p = 0.002), and women (p = 0.008), urban residents (p = 0.001), and those with regular animal contact (p = 0.007) were more likely to support reform. Right-leaning respondents (p = 0.012) and residents of countries without fur farming bans (p < 0.001) were less supportive. These findings suggest that values, demographics, and national legislation significantly shape public opinion. Aligning policy with evolving societal values requires integrated legislative reform, public engagement, and equitable transition strategies to ensure meaningful and sustainable improvements in animal welfare across the EU. Full article

Background: Recent studies have indicated that serum calprotectin, a marker of inflammation, is associated with obesity and disorders of glucose and lipid metabolism. The aim of this study was to evaluate the relationship between serum calprotectin and cardiometabolic risk factors in presumably healthy young adults. Methods: The study enrolled 118 (61 females, 57 males) non-obese, normoglycemic, subjects aged 25–40 years, selected from the general population among participants of the diabetes preventive screening program in 2014–2015. Basic anthropometric measurements and the following laboratory tests were performed on all participants: glucose, glycated hemoglobin (HbA1c), lipid profile, insulin, Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), high sensitivity C-reactive protein (hs-CRP), calprotectin and adiponectin. Results: The serum calprotectin concentration was significantly higher in men compared to women (p = 0.016), and in overweight subjects (p < 0.001) and those with abdominal obesity (p < 0.001), compared to lean individuals. Serum calprotectin was positively correlated with body mass index (BMI), waist circumference, HbA1c, hs-CRP, insulin, HOMA-IR and triglycerides, and negatively with HDL-cholesterol and adiponectin. In the univariable logistic regression analysis, overweight (OR = 2.529; p = 0.015), abdominal obesity (OR = 3.217; p = 0.006), hs-CRP > 1 mg/L (OR = 5.00; p < 0.001), HOMA-IR > 2.0 (OR = 4.394; p < 0.001), and HbA1c > 32 mmol/mol (OR = 2.166; p = 0.021) were significant predictors of increased calprotectin concentration (≥540.8 ng/mL; ≥median). However, in models adjusted for sex, BMI and hs-CRP, the significant association remained only for increased HbA1c and HOMA-IR values. Conclusions: Association of serum calprotectin with overweight, hs-CRP and laboratory indicators of glucose metabolism and insulin resistance suggest its significance as a laboratory biomarker of initial metabolic impairment. Full article

Background: This study deploys the Lean Six Sigma DMAIC framework to achieve systemic optimization of the school subscription process in Tunisia’s public transport service, a critical administrative operation affecting efficiency and customer satisfaction across the urban mobility network. Methods: Beyond conventional applications, the research integrates advanced analytical and process engineering tools, including capability indices, measurement system analysis (MSA), variance decomposition, and root-cause prioritization through Pareto–ANOVA integration, supported by a structured control plan aligned with ISO 9001:2015 and ISO 31000:2018 risk-management standards. Results: Quantitative diagnosis revealed severe process instability and nonconformities in information flow, workload balancing, and suboptimal resource allocation that constrained effective capacity utilization. Corrective interventions were modeled and validated through statistical control and real-time performance dashboards to institutionalize improvements and sustain process stability. The implemented actions led to a 37.5% reduction in cycle time, an 80% decrease in process errors, a 38.5% increase in customer satisfaction, and a 38.9% improvement in throughput. Conclusions: This study contributes theoretically by positioning Lean Six Sigma as a data-centric governance framework for stochastic capacity optimization and process redesign in public service systems, and practically by providing a replicable, evidence-based roadmap for operational excellence in governmental organizations within developing economies. Full article

Logistics and warehouse operations experience an increasing pressure to adopt sustainable practices. The logistics industry generates substantial material waste, with cardboard being the primary packaging material. Adopting Circular Economy (CE) principles to control this waste is important for enhancing sustainability. However, there is a lack of studies on transforming warehouses into more sustainable operations. This paper studies the ability to transform the linear supply chain of a distribution warehouse into a circular supply chain by applying lean manufacturing principles to eliminate cardboard waste. A structured framework is presented to outline the project’s methodology and illustrate the steps taken to apply the concept of CE. The paper also tests the capability to simulate warehouse operations with engineering software using limited available data to generate various scenarios. This study contributes by showing how discrete-event simulation combined with VSM and 6R principles can provide operational insights under data-constrained conditions. Bridging the gap between theory and practice. Multiple operational scenarios were modelled and run, including peak and off-peak demand periods, as well as a sensitivity analysis for recycling durations. A comparative evaluation is shown to demonstrate the effectiveness of each alternative and determine the most feasible solution. Results indicate that introducing recycling activities created some bottlenecks in the system and reduced its efficiency. Furthermore, suggestions for future improvements are presented, ensuring that on-site actions are grounded in a simulation that reflects reality. Full article

Background: Malnutrition is common in chronic hemodialysis (HD) patients and often remains underdiagnosed. While body composition, functional status, and anthropometric measures can support nutritional assessment, their associations with nutritional status are not fully established in this population. This study aimed to evaluate the diagnostic performance of various measures for assessing malnutrition in chronic HD patients, using the Subjective Global Assessment (SGA) as the reference standard. Methods: This cross-sectional study involved chronic HD patients, stratified by nutritional status using the SGA. Data collection consisted of clinical interviews, anthropometric and functional measurements, bioelectrical impedance analysis (BIA), and biochemical analyses. Statistical analysis included Spearman’s correlation, logistic regression, receiver operating characteristic (ROC) curve analysis with area under the curve (AUC) to assess predictive accuracy, standardized effect sizes to show the magnitude of differences, and kappa statistics to evaluate concordance between variables. Results: This study included 103 chronic HD patients. Malnutrition was diagnosed in 50.5% of patients based on the SGA. Phase angle (PA) was the strongest single predictor of malnutrition (AUC = 0.79; specificity 0.88; sensitivity 0.58). PA ≤ 5.1° was significantly associated with higher malnutrition risk (OR: 10.23; 95% CI: 3.93–30.61; p < 0.001). Handgrip strength (HGS) also demonstrated good diagnostic value (AUC = 0.71; specificity 0.84; sensitivity 0.59). A multivariable model incorporating eight parameters—gender, post-dialysis ECW/ICW ratio, post-dialysis lean and fat mass, serum albumin, normalized protein catabolic rate (nPCR), arm circumference (AC), and HGS—achieved an AUC of 0.88 (95% CI: 0.81–0.95) and pseudo-R² of 0.46, demonstrating improved predictive performance. Conclusions: An integrated panel of anthropometric, bioimpedance, functional, and biochemical markers provides superior diagnostic accuracy compared to individual predictors, supporting a holistic diagnostic approach in HD patients. Full article

Background/Objectives: Chronic kidney disease (CKD) and diabetic kidney disease (DKD) are growing public health challenges. While diet and body composition influence metabolic and renal health, their combined role remains underexplored. This study investigates the association between the Planetary Health Diet Index (PHDI), body mass distribution, and the prevalence of CKD and DKD in U.S. adults. Methods: We analyzed data from 8093 adults aged ≥40 years from NHANES 2003–2018. PHDI was computed using two 24 h dietary recalls. Body composition was assessed using dual-energy X-ray absorptiometry (DXA), focusing on the android-to-gynoid fat ratio (AGFR) and lean mass ratio (AGLR). Survey-weighted linear and logistic regressions evaluated cross-sectional associations between PHDI score, body composition indices, and prevalence of CKD and DKD. Mediation analyses explored AGLR, AGFR, and body mass index (BMI) as potential mediators of the association between PHDI score and either CKD or DKD. Results: Higher PHDI scores were mildly associated with lower odds of CKD (OR per 10-point increase: 0.91; 95% CI: 0.83–0.99) and DKD (OR: 0.86; 95% CI: 0.76–0.97). Greater PHDI scores correlated with lower BMI, AGFR, and AGLR. Among participants with diabetes, AGLR mediated 17% of the relationship between a 10-point increase in PHDI score and decreased DKD prevalence, suggesting central lean mass distribution as a relevant pathway. No significant mediation was observed for AGFR, BMI, or for CKD. Conclusions: Adherence to PHD is associated with healthier body composition and lower prevalence of CKD and DKD. These findings support the promotion of dietary strategies that enhance metabolic and renal health in middle-aged and older individuals. Full article

Background/Objectives: Microvascular complications are a major source of disability in type 2 diabetes mellitus (T2DM). We investigated whether body composition indices derived from multifrequency bioelectrical impedance analysis (BIA) independently predict neuropathy, retinopathy, nephropathy, and stroke, and whether they improve risk discrimination beyond the established clinical variables. Methods: In this cross-sectional analytical study (March 2024–February 2025), 124 adults with T2DM ≥ 12 months attending the outpatient diabetes clinic of the Universidad Técnica de Ambato (Ecuador) were enrolled. After an overnight fast and 15 min supine rest, thirteen whole-body BIA metrics including skeletal muscle mass (SMM), intracellular water (ICW), phase angle (PhA), and visceral fat area (VFA) were obtained with a segmental analyzer (InBody S10). Complications were ascertained with standard clinical and laboratory protocols. Principal component analysis (PCA) summarized the correlated BIA measures; multivariable logistic regression (adjusted for age, sex, diabetes duration, HbA1c, BMI, and medication use) generated odds ratios (ORs) per standard deviation (SD). Discrimination was assessed with bootstrapped receiver-operating characteristic curves. Results: The first principal component, driven by SMM, ICW, and PhA, accounted for a median 68% (range 65–72%) of body composition variance across all complications. Each SD increase in SMM lowered the odds of neuropathy (OR 0.54, 95% CI 0.41–0.71) and nephropathy (OR 0.70, 0.53–0.92), whereas VFA raised the risk of neuropathy (OR 1.55, 1.22–1.97) and retinopathy (OR 1.47, 1.14–1.88). PhA protected most strongly against stroke (OR 0.55, 0.37–0.82). Composite models integrating SMM, PhA, and adiposity indices achieved AUCs of 0.79–0.85, outperforming clinical models alone (all ΔAUC ≥ 0.05) and maintaining good calibration (Hosmer–Lemeshow p > 0.20). Optimal probability cut-offs (0.39–0.45) balanced sensitivity (0.74–0.80) and specificity (0.68–0.72). Conclusions: A lean tissue BIA signature (higher SMM, ICW, PhA) confers independent protection against neuropathy, retinopathy, nephropathy, and stroke, whereas visceral adiposity amplifies the risk. Because the assessment is rapid, inexpensive, and operator-independent, routine multifrequency BIA can be embedded into diabetes clinics to triage patients for early specialist referral and to monitor interventions aimed at preserving muscle and reducing visceral fat, thereby enhancing microvascular risk management in T2DM. Full article

This study proposes a fuzzy logic-based approach to better manage supply chain uncertainty and improve decision-making flexibility. The developed framework categorizes supply chain activities into procurement, production, distribution and reverse logistics and integrates Lean, Agile, Resilient, and Green (LARG) KPIs within a hierarchical structure. The tool was implemented using Microsoft Excel^TM to enhance usability for practitioners. To test its applicability, the model was applied to a real case study. The results show that lean and resilient practices are consistently well-established across all supply chain phases, while agility and green practices vary significantly depending on the operational area—particularly between internal function (i.e., production and reverse logistics) and external ones (i.e., procurement and distribution). These findings help to better understand how the LARG capabilities are distributed across the different operational areas of the supply chain and offer practical guidance for managers seeking targeted performance improvement. Although the numerical results are context-specific, the framework’s adaptability makes it suitable for diverse supply chain environments. Full article

Background: Sarcopenia is a progressive muscle disease that compromises mobility and quality of life in older adults. Although dual-energy X-ray absorptiometry (DXA) is the standard for assessing Appendicular Lean Mass Index (ALMI), it is costly and often inaccessible. This study aims to develop machine learning models using anthropometric measurements to predict low ALMI for the diagnosis of sarcopenia. Methods: A cross-sectional study was conducted on 183 Mexican adults (67.2% women and 32.8% men, ≥60 years old). ALMI was measured using DXA, and anthropometric data were collected following the International Society for the Advancement of Kinanthropometry (ISAK) protocols. Predictive models were developed using Logistic Regression (LR), Decision Trees (DTs), Random Forests (RFs), Artificial Neural Networks (ANNs), and LASSO regression. The dataset was split into training (70%) and testing (30%) sets. Model performance was evaluated using classification performance metrics and the area under the ROC curve (AUC). Results: ALMI indicated strong correlations with BMI, corrected calf girth, and arm relaxed girth. Among models, DT achieved the best performance in females (AUC = 0.84), and ANN indicated the highest AUC in males (0.92). Regarding the prediction of low ALMI, specificity values were highest in DT for females (100%), while RF performed best in males (92%). The key predictive variables varied depending on sex, with BMI and calf girth being the most relevant for females and arm girth for males. Conclusions: Anthropometry combined with machine learning provides an accurate, low-cost approach for identifying low ALMI in older adults. This method could facilitate sarcopenia screening in clinical settings with limited access to advanced diagnostic tools. Full article

Indonesia’s high logistics costs—approximately 14.6% of its GDP—pose a significant challenge to national economic competitiveness. Key contributing factors include complex geography, fragmented multimodal transport systems and inefficient container terminal operations, particularly concerning the handling of empty containers. This study investigates operational optimization in a container terminal using Agile and Lean principles, without additional investment or infrastructure expansion. It compares throughput before and after optimization, focusing on equipment productivity and reduction in idle time, especially related to equipment and human resources. Field implementation began in 2015, followed by simulation-based validation using system dynamics modeling. The terminal demonstrated a sustained increase in capacity beginning in 2016, eventually exceeding its original design capacity while maintaining acceptable berth and Yard Occupancy Ratios (BOR and YOR). Agile practices improved empty container handling, while Lean methods enhanced berthing process efficiency. The findings confirm that significant reductions in port operational costs, shipping operational costs, voyage turnover time, and logistics costs can be achieved through strategic operational reforms and better resource utilization, rather than through capital-intensive expansion. The study provides a replicable model for improving terminal efficiency in ports facing similar constraints. Full article

There is currently a growing need for energy, which, combined with climate change, has increased the focus on renewable energies. Among them, biomass energy takes the lion’s share, and this can create forestry pressures or lead to the excessive consumption of resources. To mitigate this situation, residual biomass from agroforestry has emerged as a valuable resource, supporting energy transition and mitigating these challenges. However, this biomass is traditionally burned, leading to large fires, as a result of the high logistical costs associated with the lack of information/coordination between those involved in the chain. Therefore, the primary objective of this work is to address this gap by presenting an information management model based on a web application, which aims to increase transparency, integrate stakeholders, and improve logistical decisions. In methodological terms, this study follows the principles of human-centered design, as well as an agile software development methodology. The results include the creation of a new, flexible information management ecosystem, which allows each stakeholder to take on different roles according to their needs in the chain. In addition, lean information management principles have been included in order to reduce waste in information content and flow. Full article

Background/Objectives: Abdominal aortic aneurysm (AAA) repair is a prophylactic intervention aimed at preventing rupture. As the population ages, surgical decision-making becomes increasingly complex, especially in older and frailer patients. Imaging biomarkers, such as psoas muscle area (PMA) and density (PMD), have been proposed as surrogates for frailty and potential predictors of surgical outcomes. However, their clinical utility remains uncertain. Methods: In this retrospective, single-center study, we evaluated 199 patients who underwent elective AAA repair between 2015 and 2019. Preoperative computed tomography angiography (CTA) was used to measure PMA and PMD at the level of the third lumbar vertebra. Lean psoas muscle area (LPMA) was calculated as the product of PMA and PMD. Sarcopenia was defined as the lowest tertile of each measurement. Outcomes were assessed using Fisher’s exact test, Kaplan–Meier survival analysis, and logistic regression. Results: No significant associations were found between PMA, PMD, or LPMA and early or late postoperative complications or mortality. Conclusions: Psoas muscle indices, measured on routine CTA scans, do not reliably predict postoperative outcomes in AAA patients. These findings suggest that further studies integrating broader clinical and functional assessments are needed to improve risk stratification and inform preoperative decision-making in this patient population. Full article

Background: The global outbreak of Coronavirus Disease 2019 (COVID-19) has significantly altered everyday routines, even influencing dietary behaviors and food choices. This study aimed to investigate the impact of the COVID-19 pandemic on changes in the consumption of various food sources and to examine the likelihood of nutritional vulnerability while exploring potential age and sex differences. Methods: This study employed a cross-sectional design through an online survey using the Qualtrics platform. Participants’ nutritional risk was assessed both before and after the onset of the COVID-19 pandemic (retrospectively) using the Dietary Screening Tool. This study involved a large sample of 10,050 participants aged between 40 and 100 years. The main outcome measures included changes in food consumption after the onset of the COVID-19 pandemic (from 9 August to 15 September 2020) and the likelihood of being classified as “nutritionally vulnerable” across different age and sex groups. Paired-samples t-tests were used to compare dietary changes before and after the onset of the COVID-19 pandemic, chi-square tests were used to explore categorical differences, and binary logistic regression was used to assess the likelihood of nutritional vulnerability. Results: The analysis revealed significant sex- and age-related differences in food consumption patterns after the onset of the pandemic. Men had a 30% greater likelihood of decreased dairy and processed meat consumption than women after the onset of the COVID-19 pandemic. Significant reductions in the consumption of processed meats (130%), vegetables (96%), lean protein (33%), and dairy (11%) were observed among individuals aged 40–60 years compared to those aged 81–100 years. The 61–80-year age group had a greater reduction in dairy consumption (21%) than the 81–100-year age group. Furthermore, women exhibited 17% greater odds of being classified as “nutritionally vulnerable” after the onset of the COVID-19 pandemic. However, age did not emerge as a significant predictor of nutritional vulnerability. Conclusions: The findings of this study can inform public health practitioners when developing interventions for improving dietary habits during and after pandemics, especially among vulnerable populations. Full article

The article presents the results of research on the resilience of companies using management concepts such as Lean Management, Agile, and Six Sigma to the crises that companies have had to face in recent years: the COVID-19 pandemic, rising energy prices, and the war in Ukraine. The implementation of these management concepts should lead to process improvements and a reduction in the consumption of production resources, including energy. The aim of the study was to determine how these crises have affected the efficiency of companies and to determine whether the solutions used so far are sufficient or require modification. The authors used three research methods. Firstly, they analyzed the literature—scientific publications, studies, and expert reports. Secondly, they analyzed the financial results (net profits and share of Costs of Goods Sold in the value of Revenues) in the period before (2016–2019) and after the outbreak of the COVID-19 pandemic (2020–2023) of companies using Lean Management, Agile, and Six Sigma strategies and their combinations. To compare the effectiveness of these management methods, they also analyzed the financial results of international corporations and Polish companies. Third, they conducted a survey among Polish companies applying the Lean Management concept. The results of this research show that the crises of recent years, even if they caused a deterioration in financial performance, were short-lived as companies were able to adapt to the new conditions. Japanese companies using Lean Management increased their profits by an average of 55.56% between 2020 and 2023, and “Lean” American organizations even more (71.64%). Polish companies have been steadily increasing their profits for years (134.14% before the pandemic and 143.27% after the outbreak). The share of COGS will remain at a similar (high) level for many years to come. There are no significant increases in these costs due to crises in the companies’ environment (e.g., increase in energy prices), and, on the other hand, there is no tendency for them to decrease in a large proportion of companies. In the years 2020–2023, the largest decreases in the share of these costs occurred in companies combining Lean and Six Sigma (−11.85%). In companies that use the Agile strategy, there was an increase of 8.05%. However, these are average data, and the analysis of the results of companies from individual groups leads to the conclusion that it is not only the management concept that is important, but also how it is implemented in a given company. In addition, streamlining processes only by eliminating waste is not enough these days. It is necessary to use modern technologies (digital technologies, Industry 4.0). Increasing the efficiency of production or logistics processes leads to a reduction in energy consumption and external costs. However, new, specialized solutions are needed. The issue of energy efficiency is indeed gaining more and more importance in companies and is included in management concepts, e.g., in Lean Management. Full article