Model Assessment Research Articles

Inverse problem models for assessment of rainfall-runoff-sediment processes applied to a small river basin in the Amazon

Inverse problem models for assessment of rainfall-runoff-sediment processes applied to a small river basin in the Amazon

Effect of light fasting diet therapy on lipid metabolism and sex hormone levels in patients with polycystic ovary syndrome combined with infertility

Objective To investigate the effects of light fasting diet therapy, including flaxseed powder supplementation, on lipid metabolism and sex hormone levels in patients with polycystic ovary syndrome (PCOS) combined with infertility. Methods A total of 104 PCOS patients with combined infertility were divided into the control group (n = 52) and intervention group (n = 52) using a random number table method. Over a two-month study period, the control group received light fasting diet therapy with rice flour as a placebo, while the intervention group received light fasting diet therapy supplemented with flaxseed powder. Differences in general clinical data, anthropometric indicators, follicle-stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E2), progesterone (P), fasting plasma glucose (FPG), fasting insulin (FINS), Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C)) were compared between the two groups. Results After treatment, both groups showed significant reductions in FSH, LH, E2, and P levels compared to baseline (p < .05). In the intervention group, the mean FINS and HOMA-IR levels were significantly lower than those in the control group following 2 months of treatment (p < .05). Furthermore, TC, TG, and LDL-C levels were significantly reduced in the intervention group compared to the control group (p < .05). Conclusion Light fasting diet therapy, especially with flaxseed powder, improves obesity, lipid, and glucose metabolism disorders in PCOS patients with infertility. However, longitudinal studies are needed to better understand these associations and assess long-term effects before considering this intervention as a standard clinical recommendation for PCOS.

Mango Consumption Is Associated with Increased Insulin Sensitivity in Participants with Overweight/Obesity and Chronic Low-Grade Inflammation

Background/Objectives: Chronic low-grade inflammation is associated with insulin resistance and poor glycemic control, leading to the development of type 2 diabetes mellitus (T2DM). The present study investigated the effect of regular mango intake on inflammation and insulin sensitivity in participants with overweight or obesity and chronic low-grade inflammation. Methods: A human clinical study was performed using a randomized, controlled, two-arm, parallel design with a 2 h oral glucose tolerance test (OGTT) administered before and after 4 weeks (4 W) of mango or control product intake (1 cup/twice a day). Fasting and time course blood sampling for 2 h post-OGTT were analyzed for effects on plasma metabolic and inflammation endpoints using analysis of covariance and repeated-measure approaches (SAS 9.4). Results: Forty-eight adults (37.6 ± 2.8 years, 30.5 ± 4.1 BMI kg/m2) completed the study. Markers of inflammation (IL-6, TNFα, hs-CRP) were not different at the end of 4 W (p &gt; 0.05). The intervention did not significantly influence fasting glucose concentrations; however, insulin was significantly lowered with the mango compared to the control intervention (8.2 ± 1.2 vs. 15.3 ± 1.2 µIU/mL respectively, p = 0.05). Furthermore, the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), along with the disposition index (DI), was significantly improved in the mango compared to the control interventions (HOMA-IR, 2.28 ± 1.19 vs. 4.67 ± 1.21, p = 0.03; DI, 2.76 ± 1.02 vs. 5.37 ± 1.03, p = 0.04). Mean insulin concentrations were also significantly lower at W4 compared to W0 after the OGTT in the mango vs. control intervention (intervention × week effect, p = 0.04). Relative expression of nuclear factor erythroid 2-related factor 2 (Nrf-2), a gene regulating endogenous antioxidant defense, was non-significantly increased twofold in the mango intervention (W4 vs. W0). Conclusions: Collectively, the data suggest that mango intake increased insulin sensitivity in individuals with chronic low-grade inflammation, possibly through activating Nrf-2 genes and increasing cellular antioxidant status. The data warrant further research on consuming mango fruit as part of a dietary pattern to address insulin resistance and the mechanisms underpinning the actions of mango intake.

Borich needs assessment model to evaluate training needs of smallholder farmers in Zambia

Abstract The human capital theory states that individuals, such as smallholder farmers, can improve their skills through training and education to increase their future productivity and value in the marketplace and ultimately ensure food security. However, it is crucial to determine the gap in their skill set to allow for custom training programs. Against this backdrop, we employ the Borich needs assessment model to a dataset of 252 randomly selected smallholder farmers belonging to the Mulimi Farmer Scheme in Zambia to determine their training needs. Of these farmers, 35% had access to extension services and received technical farming support. However, only 30% of them attended some form of training in the past, mainly focusing on technical training. The biggest training need highlighted by the farmers was technical training, followed by farm business management training. Based on the Borich needs assessment model results, we recommend prioritizing the following areas for training: farm resource management, developing a business plan, and farm record keeping. However, considering the farmer’s diverse educational backgrounds and prior knowledge, we suggest tailoring the training approach to meet their specific needs.

انعكاس فهم الهيكلية المالية في تحديد وتقييم المخاطر الجوهرية وتعزيز جودة التدقيق الخارجي

Understanding the financial structure is one of the most important matters of interest to users of financial statements because it affects the financial performance of companies, due to the availability of many financing alternatives that the company wants to compose appropriately, and the company may be exposed to financial risks affecting the structure, which requires the external auditor to take action by determining the extent of its exposure to risks and evaluating the effectiveness of its strategies to manage those risks. The measurement process depends on different tools and methods that provide accurate insights into potential risks and their financial impact, in order to enhance the quality of the audit, which is measured by knowing the extent of compliance with auditing standards and performance standards in the audit process. And the extent of the auditor's ability to diagnose errors and material violations in the financial statements. The link between more than one variable in this research is an important step as previous studies did not address it, and the research aims to identify audit procedures when analyzing the financial structure in the financial statements by studying the financial ratios with a statement of the effects of some economic variables, such as interest rates and inflation rates on the result of the analysis, while comparing the availability of audit quality indicators. The research relied on the applied aspect on analyzing the financial statements for three years for the research sample, which includes Ashur International Investment Bank and Al-Nokhba Company, calculating financial ratios, applying the proposed audit risk identification and assessment model, and measuring the quality of the audit as a result of the results of the analysis of the reality of external audit reports. The research reached many conclusions, namely the existence of interconnected relationships between the research variables that contribute to planning the audit in a way that enhances its quality on the other hand, and giving a true picture of the fairness of the financial statements that serve the users of those statements to make appropriate decisions. These conclusions are important in the banking and contracting sectors.

Decentralized Public Transport Management System Based on Blockchain Technology

The development of intelligent transportation systems (ITSs) is penetrating many economies around the globe. This paper presents three key innovations in the field of intelligent transportation systems, as follows: (1) a novel tokenization approach where each vehicle is represented as a macro-token subdivided into 500,000 micro-tokens for precise condition monitoring, (2) a comprehensive mathematical model for vehicle state assessment incorporating multiple operational factors, and (3) the GDEPZ method for optimizing data transmission via satellite communication. These innovations enable the autonomous control of technical conditions, transparent fleet management, and efficient data processing in hard-to-reach areas. Various researchers in both industry and academia are looking into more efficient management methods for both vehicles and related data processing aspects. A vast trend related to the latter is the distributed data processing of transmitted data. This article discusses approaches to the use of blockchain technology in ITSs. It explores the use of blockchains in modern transport industries. In particular, the paper proposes a novel approach to the maintenance of public transportation vehicles and buses. The specificity of the proposed approach is the autonomous control of technical conditions using information systems. When using blockchain technology, building a transparent vehicle fleet management system is possible. The specificity of the proposed approach lies in data processing. Within the organization, confidence in data increases, the possibility of manipulating transportation is eliminated, and the decision-making chain is reduced. As a result, the system can manage itself. This also helps to increase the service life of vehicles, makes it possible to predict their malfunctions, and improves the quality of data on their technical conditions.

Supporting Literacy Assessment in West Africa: Using State-of-the-Art Speech Models to Assess Oral Reading Fluency

Abstract This paper reports on a set of three recent experiments utilizing large-scale speech models to assess the oral reading fluency (ORF) of students in Ghana. While ORF is a well-established measure of foundational literacy, assessing it typically requires one-on-one sessions between a student and a trained rater, a process that is time-consuming and costly. Automating the assessment of ORF could support better literacy instruction, particularly in education contexts where formative assessment is uncommon due to large class sizes and limited resources. This research is among the first to examine the use of the most recent versions of large-scale speech models for ORF assessment in the Global South.We find that the best performing model, Whisper V2, with no additional fine-tuning, produces transcriptions of Ghanaian students reading aloud with a Word Error Rate of 10.3. When these transcriptions are used to produce fully automated ORF scores, they closely align with scores generated by expert human raters, with a correlation coefficient of 0.98. These results were achieved on a representative dataset (i.e., students with regional accents, recordings taken in actual classrooms), using a free and publicly available speech with no additional fine-tuning. This model’s strong performance on real-world classroom data, combined with its accessibility and simplified implementation, suggests potential for scaling ORF assessment in lower-resource, linguistically diverse educational contexts.

Automated 3D Modeling vs. Manual Methods: A Comparative Study on Historic Timber Tower Structure Assessment

The present study focuses on the preservation of historic timber constructions, crucial cultural heritage assets that demand effective structural health monitoring (SHM) to ensure safety and integrity. SHM aims to detect and evaluate potential structural deviations that may compromise performance, requiring both detailed geometric data acquisition and 3D modeling. For this purpose, contactless tools such as photogrammetry, laser scanning, and other topographic methods are employed to gather point cloud data. This research utilizes a terrestrial laser scanner (TLS) to generate 3D models of the historic timber tower of St. Michaeler church in Vienna. A novel automated modeling method is compared with two manual modeling approaches. The first is a traditional as-designed structural model created in Dlubal RSTAB software, and the second is a manually generated as-built model created using a scan-to-BIM application in Revit. While the first model is based on 2D plan documents created from the TLS point cloud, the second and automated models use the point cloud as direct input. The findings demonstrate that this automated model significantly enhances early-stage structural assessment efficiency, providing reliable insights into structural conditions with minimal processing time. This research underscores the potential of automated 3D modeling in preliminary structural assessments of historic timber structures.

INDOFLOODS: A Comprehensive Database for Flood Events in India Enhanced with Catchment Attributes

Abstract Despite floods causing significant loss of life and property, datasets to characterize flooding events in developing countries such as India are not widely available, hampered by limited hydrometric records. Current flood databases are limited to continental river basins, case studies, and government reports, which doesn’t represent the diversity of flooding in terms of climate, basin morphometry, and triggering mechanisms. This first-of-its-kind flood events database called INDOFLOODS is developed using a unique approach of combining long-term station discharge observations with official flooding thresholds for warning and danger water level. Flooding information include start and end time, peak flood level and discharge and its date of occurrence, flood volume, event duration, time to peak, and recession time. Along with metadata such as upstream catchment area, coordinates, shapefiles, river and tributary names, the database is augmented with large number of geomorphological, climatological, event-scale precipitation, landcover, soil, lithology, and anthropogenic characteristics derived at the catchment scale. Preliminary data analysis based on envelope curves shows that the magnitude of extreme floods in India is higher than those reported in the United States. While every dataset has limitations, this collation of flooding events with a plethora of causative hydrogeomorphic factors in a standardized format will be a major asset for the community and serve as an example for how inconsistent data records in developing countries can be turned into useful flood event databases for data-driven studies. This large sample database is expected to cater to a wide range of applications advancing flood research and management, such as trend analysis, hazard and severity assessment, calibration and validation of hydrological and hydraulic models, and developing new metrics for impact assessment.

The Influence of Influencer Marketing and Brand Knowledge on Brand Switching Local Skincare

This study primarily aims to investigate the causal relationships between influencer marketing and brand knowledge on brand switching behavior among local skincare product consumers in East Jakarta, with particular emphasis on understanding the psychological and behavioral mechanisms driving consumer migration between brands. The research employs a quantitative methodology through a structured online survey distributed via multiple digital platforms (WhatsApp, Instagram, and Twitter), achieving a response rate of 87.5% with 154 valid responses from a target of 176 distributed questionnaires. The study population comprised both female (75.3%) and male (24.7%) consumers aged 17-35 years who have experience using local skincare products. Data analysis was conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS) through SmartPLS software, incorporating measurement model assessment (convergent and discriminant validity) and structural model evaluation (path coefficients and hypothesis testing). The results demonstrate statistically significant positive effects of both influencer marketing (β = 0.229, t = 3.131, p &lt; 0.002) and brand knowledge (β = 0.242, t = 2.857, p &lt; 0.004) on brand switching behavior. Specifically, influencer marketing's impact is manifested through trustworthiness, expertise, and attractiveness indicators, while brand knowledge influences through brand recognition, recall, and quality perception pathways. These findings have substantial implications for local skincare brands' marketing strategies, suggesting that effective influencer collaboration and comprehensive brand education can significantly influence consumer brand switching decisions. The study contributes to the growing body of literature on digital marketing dynamics in the Indonesian skincare industry while providing practical insights for marketing practitioners in the local beauty sector.

Evaluating the Quality of Abdullwahab Al-Maqaleh's Translation of Idiomatic Expressions and Culture-Bound Terms in Dave Eggers's The Monk of Mokha by Using House's (2015) Model

This study aims to assess and evaluate the quality of Abdullwahab Al-Maqaleh's Translation of idiomatic expressions and culture-bound Terms of Dave Eggers's Novel "The Monk of Mokha" by using House's (2015) Translation Quality Assessment Model. The descriptive analytical methodology is used in this study. The analysis is divided into two sections. The first section analyzes the source text and target text according to their register and genre. The analysis of the source text and target text shows a number of mismatches and errors (overtly erroneous errors) along these dimensions. The second section categorizes the related overt errors into five categories: not translated, slight change of meaning, significant change of meaning, breach of the target language system, and creative translation. The study shows that the target text has many mismatches at the level of field, tenor, and mode, such as lexical and syntactic mismatches. It reveals that many cultural terms were translated literally and did not reflect the intended meaning of the source text.

A catalogue of land-based adaptation and mitigation solutions to tackle climate change

Informing and engaging all actors in the land sector, including land-owners and managers, researchers, policy-makers and citizens, on the most effective sustainable land-based solutions and behavioural changes is a key strategy for achieving climate change adaptation and mitigation targets at the global as well as at EU and local level. One requisite to support actors in the land sector is to provide them publicly available, reliable and ready-to-use information related to the implementation of Land-based Adaptation and Mitigation Solutions (LAMS). Here we introduce a LAMS catalogue, a collection of meaningful quantitative and qualitative information on 60 solutions characterised according to a set of specifications (e.g., mitigation and adaptation potential, cost of implementation, suitability factors, synergies and trade-offs, drivers and barriers to the implementation). The catalogue offers a reliable, science-based tool useful for different users’ needs, including valuable references for deriving context-specific quantitative inputs to simulate and evaluate the performance of solutions over time using modelling tools, such as Integrated Assessment Models at any scale.

A New Model to Assess Compensation for Eco‐Environmental Damage

ABSTRACTTo improve procedures to initiate compensation for ecological damage, and to more quickly and efficiently quantify damage during the initial stages of damage, inspiration is drawn from the US Natural Resources Damage Assessment System to construct an ecological environment assessment model that includes indicators such as pollutant hazards, environmental functions, exceedance, pollution duration, pollutant quantity, and receptor sensitivity. According to rating values, a particular incident (a “case”) is apportioned to one of three levels: significantly minor, minor, and significant damage. These levels are used as a starting point for refining the case. We applied 126 cases from City A to this model. Results indicate that it is highly consistent with the actual situation. This model has good maneuverability. Based on existing standard specifications and practical cases, this model is validated. This model can be applied to provide technical guidance and a standardized response strategy to managers in instances of environmental impact.

Significant risk factors for oral mucosa cancer in the Altai Krai population

Relevance. Squamous cell carcinoma of the head and neck, including oral and oropharyngeal cancer, ranks as the seventh most common cancer worldwide, contributing to over 660,000 new cases and 325,000 deaths annually. Understanding the interplay of adverse factors, their association with disease development, and the creation of mathematical risk prediction models can play a crucial role in enhancing screening efforts and advancing primary prevention of malignant neoplasms.Objective. This study aimed to identify significant risk factors for oral mucosa cancer in the Altai Krai population and to develop a mathematical model for disease risk assessment.Material and methods. The study included 184 patients diagnosed with oral mucosa cancer, along with a control group of 416 healthy volunteers with no history or current diagnosis of oncological diseases. A total of 39 potential risk factors were analyzed across all participants. Statistical analyses were conducted to identify region-specific risk factors for Altai Krai. Binary logistic regression and ROC analysis were used to construct the risk prediction model.Results. Comparative analysis between the patient group and the control group revealed differences in 19 of the 39 evaluated factors. However, the final risk prediction model identified five key factors significantly influencing disease development. Advanced age, smoking status, the number of cigarettes smoked, and alcohol consumption were found to substantially increase the risk of oral mucosa cancer, while engagement in intellectual work-related activities was associated with a reduced risk. The resulting formula demonstrated high predictive accuracy, with an Area Under the ROC Curve (AUC) of 0.91, a standard error of 0.024, and a 95% confidence interval of 0.856–0.955 (z-statistic: 17.50; significance level: P (area = 0.5) &lt; 0.001). Both the sensitivity and specificity of the model were high.Conclusion. The developed risk assessment model shows great promise for helping screen and diagnose oral cancer in the Altai Krai population. This tool could give dentists and healthcare providers a simple, practical way to identify individuals at risk early by using well-established risk factors.

Glucose Metabolic Abnormalities and Their Interaction With Defective Phosphate Homeostasis in Tumor-induced Osteomalacia.

Phosphate homeostasis was compromised in tumor-induced osteomalacia (TIO) due to increased fibroblast growth factor 23 (FGF23) secretion. Nevertheless, the glucose metabolic profile in TIO patients has not been investigated. This work aimed to clarify the glucose metabolic profiles in TIO patients and explore their interaction with impaired phosphate homeostasis. 20 TIO patients, 20 individuals with normal glucose tolerance, and 20 patients with type 2 diabetes mellitus (DM) were enrolled and underwent an oral glucose tolerance test (OGTT). Serum phosphate and FGF23 concentration were monitored during OGTT. In patients with TIO, 60% (12/20) exhibited impaired glucose tolerance (IGT) and 5% (1/20) had type 2 DM. Those with IGT or type 2 DM experienced more ambulatory difficulties (69.2% vs 42.9%), lower phosphate concentrations (0.43 ± 0.10 vs 0.53 ± 0.10, P = .042), and lower calcium concentrations (2.20 ± 0.08 vs 2.30 ± 0.40, P = .001) compared to TIO patients without these conditions. According to correlation analysis, serum phosphate levels were negatively correlated with plasma glucose levels at 60 minutes (P < .001), fasting plasma insulin levels (P < .05), and homeostasis model assessment for insulin resistance (P < .05). Those with high FGF23 levels had a higher glucose level at 60 minutes (10.5 [9.3, 12.3] vs 7.3 [6.4, 10.1], P = .048) than that of low group. After glucose loading, both FGF23 and phosphate levels exhibited a decreasing trend. The development of diabetes in TIO patients may be predisposed by ambulatory issues, low phosphate, and elevated FGF23 levels. Dysglycemia might further aggravate hypophosphatemia.

Anti-Inflammatory and Pain-Relieving Effects of Arnica Extract Hydrogel Patch in Carrageenan-Induced Inflammation and Hot Plate Pain Models

Arnica montana (AM), which belongs to the daisy family Asteraceae, has a longstanding traditional use in Europe and North America for pain and inflammation treatment. This study investigates the inhibitory effects of ‘Arnica montana extract hydrogel patch (AHP)’ on Carrageenan-induced paw edema and hot plate-induced pain models. AHP exhibited transdermal permeability without the occurrence of issues like crystal precipitation. This study employed two animal model assessments using AHP, in comparison with Arnicare Gel (AG), to evaluate anti-inflammatory and pain relief effects. AHP treatment for 2 days showed a decrease in paw edema thickness in mice as compared to vehicle or AG groups; Carrageenan-induced swelling increased maximally at 1 h with the AHP group demonstrating a higher reduction. Thus, the AHP group exhibited a lower ratio of right/left paw thickness and a superior reduction in swelling, supportive of its ability to diminish edema. A histological analysis showed that AHP treatment reduced inflammatory cell infiltration. Consistently, the mRNA levels of inflammatory markers (tnfa, il1b, and il6) were decreased to a greater extent than the AG group. Particularly, tnfa inhibition was better in the AHP group, and the levels of il1b and il6 transcripts showed ~80% and 40% lower. Likewise, AHP reduced pain scores in a hot plate-induced rat model, although AG failed to do so. Together, these results demonstrate that AHP has long-lasting inhibitory effects on fluid effusion and edema formation, the production of inflammatory mediators, and pain-sensation, supporting its anti-inflammatory and pain-relieving pharmacological effects.

Subclinical Carotid Disease Is Associated with Low Serum Vitamin D in Nondiabetic Middle-Aged Hypertensive Patients

Subclinical carotid artery disease anticipates major cardiovascular events, and previous studies show that low vitamin D levels are associated with arterial stiffening in hypertension. The aim of the study was to examine the relationship of 25-hydroxyvitamin D [25(OH)D] levels with subclinical carotid disease in hypertensive patients. In 223 middle-aged, nondiabetic, primary hypertensive patients free of major cardiovascular and renal complications, we measured 25(OH)D and parathyroid hormone (PTH) and assessed subclinical carotid arteries changes by B-mode ultrasonography. The carotid intima-media thickness (IMT) and presence of plaques were assessed together with measurements of indexes of carotid artery distensibility (coefficient of distensibility) or stiffening (Young’s elastic modulus; β-stiffness). Lower 25(OH)D levels were associated with older age (p &lt; 0.001), longer duration of hypertension (p = 0.019), higher fasting plasma glucose (p = 0.037), and insulin (p = 0.044), Homeostatic Model Assessment (HOMA) index (p = 0.044), and PTH (p &lt; 0.001). Insufficient and deficient 25(OH)D were associated with progressively greater carotid IMT (p &lt; 0.001), frequency of carotid plaques (p = 0.026), Young’s elastic modulus (p = 0.002), and β-stiffness (p &lt; 0.001), and progressively lower carotid coefficient of distensibility (p &lt; 0.001). Serum levels of 25(OH)D were negatively correlated with age (p &lt; 0.001), duration of hypertension (p = 0.006), fasting glucose (p &lt; 0.001), HOMA index (p = 0.032), PTH (p &lt; 0.001), carotid IMT (p &lt; 0.001), Young’s elastic modulus (p = 0.025), and β-stiffness (p &lt; 0.001), and positively related with carotid coefficient of distensibility (p &lt; 0.001). Multivariate regression analysis showed that both higher carotid IMT (p = 0.004) and lower coefficient of distensibility (p = 0.002) were related to lower 25(OH)D independent of age, severity, and duration of hypertension and metabolic variables. In conclusion, deficiency/insufficiency of 25(OH)D independently predicts subclinical carotid disease in uncomplicated, middle-aged, hypertensive patients and might predispose these patients to major cardiovascular complications.

Kidney Dysfunction Prediction by Serum Adropin in Type 2 Diabetes Iraqi Patients

Abstract Background: Diabetic nephropathy (DN) is a problem that arises from microvascular complications with type 2 diabetes mellitus (T2DM). It especially denotes the deterioration of renal function. Adropin, a regulatory peptide hormone, has attracted interest for its potential role in regulating metabolism, specifically glucose metabolism and insulin resistance. Objective: To assess Adropin’s role in the identification and severity of T2DM nephropathy. Methods: A cross-sectional study which involved an 88 patients with DN was conducted in the National Diabetes Center/Mustansiriyah University during the period from October 2023 to April 2024. Body mass index, blood analysis for sugar levels, lipid profile, renal function test, and albumin to creatinine ratio in urine were conducted. In addition, the content of human adropin was evaluated using an enzyme-linked immunosorbent assay. Results: Much more fasting blood sugar, glycated hemoglobin, insulin, homeostasis model assessment insulin resistance, cholesterol, triglycerides, low-density lipoprotein (LDL) and very LDL, urea, and creatinine were found in the microalbuminuria and macroalbuminuria groups of DN patients than in the normoalbuminuria group. The amount of adropin in the blood was lowered in people with microalbuminuria and macroalbuminuria (245.91 ± 59.47 and 179.86 ± 51.96 pg/ml, respectively) than in people with normoalbuminuria (377.97 ± 98.69 pg/ml) as well. While the subject attempts to identify the difference between the groups of DN, adropin works very well and is very sensitive. Conclusion: This study establishes a correlation between reduced adropin levels and impaired kidney function in individuals diagnosed with T2DM. Adropin content in the serum is able to serve as a biomarker for the early recognition of DN, by its role in glucose metabolism and insulin resistance.

Effects of Physical Exercise on Neurofilament Light Chain and Glial Fibrillary Acidic Protein Level in Patients with Multiple Sclerosis: A Systematic Review and Bayesian Network Meta-Analysis

Background: The prognosis of people with multiple sclerosis (MS) has improved substantially in recent decades due to advances in diagnosis and treatment. Due to the unpredictable course and heterogenous treatment response in MS, there is a clear need for biomarkers that reflect disease activity in the clinical follow-up of these patients. We conducted a systematic review with Bayesian network meta-analysis with the aim of analyzing the effects of physical exercise on neurofilaments (NfL) and glial fibrillary acidic protein (GFAP) levels in patients with MS. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, starting with a PICO (patient/population, intervention, comparison, and outcome) question: what are the clinical effects of physical exercise (with independence of the type) on NfL and/or GFAP levels in patients with MS compared with other interventions or no intervention whatsoever? A systematically comprehensive literature search was conducted from January to March 2024 to identify original studies that answered the PICO question, using the main data sources. The quality of the studies included was assessed using the Quality Index of Downs &amp; Black. For studies included in the systematic review that followed a randomized controlled trial (RCT) design, the methodological quality of each paper was assessed using the Physiotherapy Evidence Database (PEDro) Scale. Risk of bias was also explored by two independent reviewers. Finally, all articles were classified according to the levels of evidence and grades of recommendation for diagnosis studies established by the Oxford Center for Evidence-Based Medicine. For continuous outcome measures with enough comparisons and a methodological quality greater than or equal to good according to the PEDro scale, a Bayesian network meta-analysis (NMA) was applied. The statistical analyses were performed in R (version 4.1.3, R Core Team 2023) using the “BUGSnet” and “gemtc” packages. Bayesian NMA can be used to obtain a posterior probability distribution of all the relative treatment effects, which allows us to quantify the uncertainty of parameter estimates and to rank all the treatments in the network. Results: Eight studies were included in this systematic review and six articles in the NMA, and they were appraised for quality. The characteristics of the included studies, types of training and described protocols, methodological quality, risk of bias, and clinical effects on the studied biomarkers were outlined. Qualitative synthesis, effects of different exercise modalities in NfL with the Bayesian NMA, selection of the final model and model assessment, and ranking of interventions are also shown. Conclusions: Our findings indicated that moderate-intensity exercise is more likely to reduce NfL concentration compared to high-intensity exercise, and, in turn, high-intensity exercise is more likely to reduce NfL concentration than low-intensity exercise. However, the effects of high-intensity exercise on GFAP levels were inconclusive.

Temporal dynamics hidden within species baselines – multifaceted decline of a lichen bioindicator (Bryoria fuscescens)

Abstract Rapid advances in species distribution modelling have been facilitated by open availability of ‘big data’ and powerful statistical methods. A key consideration remains the time window over which field recorded occurrence data are sampled to develop a baseline species distribution. Too narrow, and distributions are incomplete and affected by sampling bias, too broad and distributions may fail to meet an assumption of equilibrium, having been affected by dynamic change across a range of different predictors. Lichens are a case in point; being diverse, functionally important and the subject of bioclimatic modelling for conservation assessment, they are nevertheless a specialist taxonomic group that is comparatively less well recorded compared to birds, mammals or vascular plants, for example. In this study, we examined the distribution of the ‘hair-lichen’ Bryoria fuscescens, based on UK record data. We partitioned records into sub-decadal periods (1970s, 1990s, 2010s), and accounting for recording effort, we compared these distributions to three predictors: an historical reconstruction of two different pollutants (sulphur dioxide and nitrogen deposition), and the climate (minimum mean temperature). We asked whether the strength of evidence for the effect of environmental predictors on Bryoria fuscescens distribution varied among the different decades, while also considering a potential for lag-effects. We show that a Bryoria fuscescens distribution that appears static, is dynamic when referenced against patterns of field recording effort. Climate was consistently important in explaining Bryoria fuscescens distribution, which was also affected by the changing pattern of pollution over time. This included a lag-effect of peak sulphur dioxide in the 1970s, and accrued effects of nitrogen deposition that strengthen over time. Overall, we conclude that Bryoria fuscescens has undergone a long-term decline in extent over the last six decades, caused by complex multivariate effects of air pollution, probably combined with climate warming. The ability to resolve these trends for assessment against future conservation targets depends critically on maintaining field identification skills and a sufficiently robust recording effort.