Sampling Error Research Articles

The Relationship Between Gender and Hypertension Severity in the Working Area of Puskesmas Cempaka Putih, Banjarmasin

Background & Objective: Hypertension is a global health issue, increasing morbidity and mortality due to cardiovascular complications. Gender differences affect blood pressure regulation, with men generally having higher blood pressure before menopause, while postmenopausal women face a greater risk due to hormonal changes. However, research on gender and hypertension severity remains inconclusive in Indonesia. This study aims to analyze the relationship between gender and hypertension severity at Puskesmas Cempaka Putih, Banjarmasin. Method: This cross-sectional study used a quantitative approach. A total of 68 hypertensive patients were selected through purposive sampling. Blood pressure was measured using a sphygmomanometer, while secondary data were obtained from medical records. The Chi-Square test was used to assess the relationship between gender and hypertension severity. Result: Among respondents, 38.2% were men and 61.8% were women. Of the men, 65.4% had Grade 1 hypertension, while 34.6% had Grade 2 hypertension. Among women, 50% had Grade 1 hypertension, and 50% had Grade 2 hypertension. The Chi-Square test yielded p = 0.214, indicating no significant association between gender and hypertension severity. Conclusion: Although gender differences in hypertension severity were observed, no significant correlation was found. Other factors, such as lifestyle and adherence to antihypertensive treatment, may play a more significant role. Future research with a larger sample and additional variables is recommended to further investigate gender differences in hypertension severity.

Analisis Perbedaan Kadar Nikel Antara Front Penambangan dan Stockpile Pada PT Rockstone Multi Mining Indonesia

PT Rockstone Multi Mining Indonesia Central Sulawesi is one of the companies that manages laterite nickel ore into ferronickel (FeNi) products. This study aims to determine the percentage of nickel content found in the mining front to the stockpile and to find out what percentage of the change in nickel content from the mining front to the stockpile and analyze the factors of the change in the level from the mining front to the stockpile. This study uses the field observation method. In the mining front data , with a total of 17 domes, the average level of Ni Front of reclamation is 1.91 while the average level of Ni stockpile is 1.90. When summing up the average value of the mining front and stockpile levels , a decrease in nickel content of 0.01% was obtained. The factors that cause changes in nickel levels are waterlogging, dredging of the Stockpile floor, the use of used sacks for samples, unclean buckets, unclean dump truck tubs, sampling errors, and preparation errors.

Bayesian assessment of profitability for products with highly uncertain demand based on multiple samples

Abstract The achievable capacity index (ACI) is well suited to measuring the profitability of a newsboy-type product, wherein profitability is defined as the probability of achieving a target profit under the optimal order quantity. In retail/manufacturing situations where storage and production capacity are limited, profitability evaluation using ACI can be considered to select advantageous products. Although frequentist approaches are useful towards estimating the true ACI, the Bayesian approach may be preferable when prior information about the demand is available. In estimating the ACI, it may be necessary to deal with situations in which the demand is highly uncertain, the sample data consists of distinct subsets drawn from a demand population (i.e., multiple samples), and sampling error must be eliminated to avoid overestimates of profitability. This study seeks to overcome these issues by developing a Bayesian procedure for testing profitability based on multiple samples, focusing on exponentially distributed demand that can exhibit high uncertainty. The lower confidence bound of ACI is derived to express profitability estimates without sampling error. We also provide some tables to facilitate decision-making in evaluating profitability. We find that variations in confidence level and the evaluated criterion significantly influence the direction of decision-making, guiding managers to focus on controlling these factors. The practicality of the proposed method was demonstrated through a case study.

Studying Repeat Victimization: A Consideration of Measurement Issues

Decades of research have recognized the phenomenon of repeat victimization and its policy relevance. Although there are continuing efforts to explain the theoretical underpinnings of repeat victimization, there are still measurement-related issues that limit our understanding of the topic and ability to inform interventions, including varying operational definitions, data constraints, and sampling and nonsampling error. In this article, we review theoretical advances in the literature over the past decade, propose operationalizations that can foster greater consistency across studies, comprehensively assess the data constraints around commonly used public data sources to study repeat victimization, and empirically demonstrate how one of these constraints—missing data—can be considered. Recommendations for future research in the area of repeat victimization are provided.

Kriging data with measurement error: A review and a generalized approach

Filtered kriging with parametric error (FKPE) is a mathematically sound method that generalizes and addresses the oversimplifications of previous kriging algorithms designed to filter error. The proposed approach is developed for handling grade-dependent (heteroscedastic), non-stationary, and spatially correlated sampling errors. The covariance between each pair of measurements or nodes is estimated from their model of errors and the spatial continuity of the underlying process. This research is driven by the fact that sampling and analytical errors are inherent in the samples used in the mining industry. In recent decades, data from quality control programs monitoring these errors have become widely available. FKPE handles more types of errors than other kriging algorithms to filter error and, for any number of subsets, it avoids jointly modelling the models of co-regionalization required by co-kriging methods. The precision gains of FKPE over other methods depend on how well the error model is fit to the data. Therefore, a detailed analysis of the error model and how to estimate its components is discussed. In a five-data toy example with high independent and grade-dependent error, kriging methods with oversimplified error models overweighted a high-grade datum by 50% compared to FKPE estimates. The performance of FKPE and other methods is illustrated by two synthetic examples.

Importance sampling of seismic tsunami sources with near-field emphasis for inundation PTHA: benchmarking with complete ensembles

Summary Site-specific Probabilistic Tsunami Hazard Assessment (PTHA) is a powerful tool for coastal planning against tsunami risk. However, its typically high computational demands led to the introduction of a Monte Carlo Stratified Importance Sampling (SIS) approach, which selects a representative subset of scenarios for numerical inundation simulations. We here empirically validate this sampling approach, for the first time to our knowledge, using an existing extensive dataset of numerical inundation simulations for two coastal sites in the Mediterranean Sea (Catania and Siracusa, both located in Sicily, Italy). Moreover, we propose a modified importance sampling function to prioritise seismic tsunami scenarios based on their arrival time at an offshore point near the target site, in addition to their wave amplitude and occurrence rate as leveraged in the previous work. This sampling function is applied separately in each earthquake magnitude bin, and allows denser sampling of near-field earthquakes to whose variations tsunamis are very sensitive. We compare the confidence intervals of the offshore PTHA estimates obtained with the new and the original importance sampling functions. Then, we benchmark our onshore PTHA estimates obtained with both functions against the inundation PTHA calculated using the full set of scenarios. We also test the assumption that onshore random errors follow a normal distribution, as found previously for the offshore case. As a result of the benchmarks, we find that the SIS approach works satisfactorily. Introducing the arrival time as an additional sampling factor enhances the precision of the estimates of both the mean and the percentiles for the two coastal sites considered. With this modification it is possible to deal efficiently with heterogeneous near-field earthquake sources involving coastal deformation at Catania and Siracusa, in addition to regional crustal and subduction sources. By comparing the sampling errors with the model (epistemic) uncertainty, an optimal trade-off between the number of simulations employed and the uncertainty of the PTHA model can be found, even for such a complex situation. A relatively small number of scenarios, on the order of a few thousand, is sufficient to perform site-specific PTHA for practical applications. These numbers correspond to 4–8% of the already reduced ensembles used in previous assessments at the same sites.

Disaster Risk Management and Emergency Response Legislations and Strategies Awareness in Tanzania: A Case of Dar Es Salaam City

This study evaluated major cities’ compliance and awareness to the disaster management and emergency response legislations and strategies in Tanzania with a focus on Dar es salaam city that have experienced persistent undesirable disasters and emergencies that arise from both natural and manmade. The study also focused at actions and programs embarked by the Dar es Salaam City Governments (Ilala, Temeke, Ubungo and Kinondoni) to implement the established disaster and emergency response legislations and strategies in dealing with the challenges identified and their way forward. The outcome of this research was contemplated to significantly assist individuals, community, the local government and other interested parties in dealing with disasters and emergencies, and enhance understanding and compliance and awareness to the disaster management and emergency response legislations and strategies urban and rural dwellers with disaster-resilient. This study deployed descriptive research design and survey to gather information about the research problem under the study. The survey provided quantitative account of views of sampled population and their attitudes, and provided chance to test correlation among population variables of the population samples in which 105 respondents were conveniently selected for this study. The study found that the majority of the population are aware the about the disasters that are likely to occur in their localities and their associated impacts both health and economic impacts, but however they are not satisfied with the strategies implemented by the government to deal with them. The study revealed that, the public officials responsible for emergency response and disaster management in Dar es Salaam are aware about the legislations and plans in dealing with disasters and emergency response but the compliance to them are below average due to various factors like resources constraints, insufficient personnel, low involvement of community, insufficient institutional coordination between emergency responders, insufficient infrastructures that affect response time to emergencies, inadequate urban land use planning and strategies and political influenced decision making. The study proposed that the government have to review and follow up implementation of developed urban land use plans base on disaster risk assessment and management reports, to intensify and provide public awareness campaigns on disaster risk management and emergency response strategies, capacity building to government officials and emergency responders on new urban development technologies and enhance their knowledge and competencies, and to review and establish a coordinated emergency response plans in major cities and establishment of early warning systems with coordinated communication systems among responders.

Correlation of shear wave elastography with liver biopsy in children with Chronic Liver disease

Background & Objectives: Liver biopsy remains the gold standard for assessment of liver fibrosis; however, it is limited practically because of issues of sampling errors and invasiveness. This opens the way for other important non-invasive processes of diagnosis including the role and utilisation of Shear Wave Elastography in coming years for liver diseases. This post-hoc study will firstly aim to establish the correlation existing between the currently preferred gold standard liver biopsy and 2D-Shear Wave elastography’s diagnostic accuracy in paediatric patients presenting with suspected or confirmed liver disease. Methods: This retrospective study was conducted in Pakistan Kidney and Liver Institute and Research Center in Lahore, Pakistan from August 2017 to January 2024. In this analysis, 37 Paediatric patients with various liver diseases who underwent liver biopsy for autoimmune hepatitis, Budd-Chiari syndrome, Wilson disease and other liver-related pathologies were included. Results: Thirty-seven patients with a mean age of 10 years (ranging from 4–14 years) were enrolled. According to this study, sixteen out of thirty seven (43%) cases of chronic liver disease were caused by autoimmune hepatitis. Patients’ average liver stiffness as determined by SWE was 12.14 ± 0.75 kPa. According to elastography, the average liver stiffness in individuals with F0–F1 fibrosis was 6 ± 0.01 kPa, 7.67 ± 0.29 in stage F2, 8.62 ± 0.20 in stage F3, and 14.05 ± 3.69 kPa in stage F4. We discovered that the mean level of hepatic stiffness varied significantly depending on the degree of fibrosis (p = 0.0001). Conclusion: SWE gauges the liver tissue’s stiffness, which rises with fibrosis severity. Research has demonstrated that SWE, which is frequently equivalent to liver biopsy, has a high degree of accuracy in identifying and staging hepatic fibrosis. doi: https://doi.org/10.12669/pjms.41.2.9886 How to cite this: Imtiaz M, Khaqan MU, Mazhar M, Seerat I. Correlation of shear wave elastography with liver biopsy in children with Chronic Liver disease. Pak J Med Sci. 2025;41(2):585-589. doi: https://doi.org/10.12669/pjms.41.2.9886 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Experiment-Guided Refinement of Milestoning Network.

Milestoning is an efficient method for calculating rare event kinetics by constructing a continuous-time kinetic network that connects the reactant and product states. Its accuracy depends on both the quality of the underlying force fields and the trajectory sampling. The sampling error can be effectively controlled through various methods. However, the force fields are often not accurate enough, leading to quantitative discrepancies between simulations and experimental data. To address this challenge, we present a refinement approach for Milestoning network based on the maximum caliber (MaxCal), a general variational principle for dynamical systems, to combine simulations and experimental data. The Kullback-Leibler divergence rate between two Milestoning networks is analytically evaluated and minimized as the loss function. Meanwhile, experimental thermodynamic (equilibrium constants) and kinetic (rate constants) data are incorporated as constraints. The use of MaxCal implies that the refined kinetic network is minimally perturbed from the original one while satisfying the experimental constraints. The refined network is expected to align better with available experimental data. The refinement approach is demonstrated using the binding and unbinding dynamics of a series of six small molecule ligands for the model host system, β-cyclodextrin.

Determining interaction directionality in complex biochemical networks from stationary measurements

Revealing interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Some methods may reveal undirected network topology, e.g., using node-node correlation. Yet, the direction of the interaction, thus a causal inference, remains to be determined - especially in steady-state observations. We introduce a method to infer the directionality within this network only from a “snapshot” of the abundances of the relevant molecules. We examine the validity of the approach for different properties of the system and the data recorded, such as the molecule’s level variability, the effect of sampling and measurement errors. Simulations suggest that the given approach successfully infer the reaction rates in various cases.

Navigating the Uncertainty of B3 Breast Lesions: Diagnostic Challenges and Evolving Management Strategies.

B3 breast lesions, classified as lesions of uncertain malignant potential, present a significant diagnostic and therapeutic challenge due to their heterogeneous nature and variable risk of progression to malignancy. These lesions, which include atypical ductal hyperplasia (ADH), papillary lesions (PLs), flat epithelial atypia (FEA), radial scars (RSs), lobular neoplasia (LN), and phyllodes tumors (PTs), occupy a "grey zone" between benign and malignant pathologies, making their management complex and often controversial. This article explores the diagnostic difficulties associated with B3 lesions, focusing on the limitations of current imaging techniques, including mammography, ultrasound, and magnetic resonance imaging (MRI), as well as the challenges in histopathological interpretation. Core needle biopsy (CNB) and vacuum-assisted biopsy (VAB) are widely used for diagnosis, but both methods have inherent limitations, including sampling errors and the inability to determine malignancy in some cases definitively. The therapeutic approach to B3 lesions is nuanced, with treatment decisions strongly influenced by factors such as the lesion size, radiological findings, histopathological characteristics, and patient factors. While some lesions can be safely monitored with watchful waiting, others may require vacuum-assisted excision (VAE) or surgical excision to rule out malignancy. The decision-making process is further complicated by the discordance between the BI-RADS score and biopsy results, as well as the presence of additional risk factors, such as microcalcifications. This review provides an in-depth analysis of the current diagnostic challenges and treatment strategies for B3 lesions, emphasizing the importance of a multidisciplinary approach to management. By synthesizing the most recent research, this article aims to provide clinicians with a clearer understanding of the complexities involved in diagnosing and treating B3 breast lesions while highlighting areas for future research, such as artificial intelligence and genomics, to improve the diagnostic accuracy and patient outcomes.

Integrating a multi-dimensional deep convolutional neural network with optimized sample selection for landslide susceptibility assessment

ABSTRACT To address the errors of negative samples in landslide susceptibility modeling and traditional methods in exploring the regularities hidden in the evaluation factors, this paper proposes a stacking one- and three-dimensional Convolutional Neural Network (Stacking-1D-3D-CNN) landslide susceptibility assessment method considering sample optimization selection. First, in order to select negative samples rationally, this paper adopts the Relative Frequency Ratio combined with Certainty Factor Method (RFR-CFM) to determine the negative samples; secondly, the Stacking-1D-3D-CNN proposed is combined with RFR-CFM for the first time for landslide susceptibility assessment. In this work, the negative samples determined by RFR-CFM and the Information Quality Model (IQM) were combined with historical disaster points to form a total modeling sample, and modeled at different ratios. Finally, it is compared with several other models in terms of the landslide hazard susceptibility zoning results, prone zone statistics, and model performance. The findings show that the degree of spatial aggregation of training samples and testing samples has a much greater impact on the accuracy of landslide susceptibility modeling than the impact of their proportions. Furthermore, compared with other models, RFR-CFM-Stacking-1D-3D-CNN has the highest AUC value, precision, recall, F-score, and accuracy, which are 0.95, 0.83, 0.89, 0.85, and 84.76%, respectively, and the lowest RMSE and MAE, 0.39 and 0.15, respectively. This proves the RFR-CFM sample selection method’s rationality and the Stacking-1D-3D-CNN model’s effectiveness.

Unsupervised Anomaly Detection for Improving Adversarial Robustness of 3D Object Detection Models

Three-dimensional object detection based on deep neural networks (DNNs) is widely used in safety-related applications, such as autonomous driving. However, existing research has shown that 3D object detection models are vulnerable to adversarial attacks. Hence, the improvement on the robustness of deep 3D detection models under adversarial attacks is investigated in this work. A deep autoencoder-based anomaly detection method is proposed, which has a strong ability to detect elaborate adversarial samples in an unsupervised way. The proposed anomaly detection method operates on a given Light Detection and Ranging (LiDAR) scene in its Bird’s Eye View (BEV) image and reconstructs the scene through an autoencoder. To improve the performance of the autoencoder, an augmented memory module with typical normal patterns recorded is introduced. It is designed to help the model to amplify the reconstruction errors of malicious samples with normal samples negligibly affected. Experiments on several public datasets show that the proposed anomaly detection method achieves an AUC of 0.8 under adversarial attacks and improves the robustness of 3D object detection.

Analysis of Metabolic Risk Factors for Microcirculation Disorders Post-Percutaneous Coronary Intervention and Predictive Model Construction: A Study on Patients with Unstable Angina.

This study aimed to analyze the metabolic risk factors for microcirculation disorders in patients with unstable angina (UA) after percutaneous coronary intervention (PCI), evaluating their predictive value for developing microcirculation disorders. A single-center retrospective study design was used, which included 553 patients with UA who underwent PCI. The angiographic microcirculatory resistance (AMR) index was calculated based on coronary angiography data. Patients were divided into two groups according to their post-PCI AMR values: a post-PCI AMR ≤2.50 group and a post-PCI AMR >2.50 group. Variables were included in the multivariate regression model through univariate regression and variance inflation factor (VIF) screening. Subgroup analyses were conducted by sex to further evaluate the predictive value of selected variables in the overall sample. The total sample was randomly split into a 7:3 ratio for the training and validation sets. A nomogram based on the training sets was then constructed to visualize these predictions. The discrimination and calibration of the prediction model were evaluated using the receiver operating characteristic (ROC) curve and calibration curve. The post-PCI AMR >2.50 group had a higher percentage of females, increased incidence of diabetes, and elevated fasting blood glucose (FBG), glycated hemoglobin (HbA1c), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), and lipoprotein(a) (Lp(a)) levels (p < 0.05). Logistic regression analysis identified HbA1c, TG, LDL-C, and Lp(a) as independent predictors of elevated AMR post-PCI after adjusting for confounders. Subgroup analysis confirmed no significant interaction between the model and sex (p > 0.05). A nomogram was constructed based on the training set, with the area under the curve (AUC) for the ROC of 0.824 in the training set and 0.746 in the validation set. The calibration curves showed a good fit (training set: p = 0.219; validation set: p = 0.258). HbA1c, TG, LDL-C, and Lp(a) levels are independent risk factors for microcirculation disorders in patients with UA post-PCI. The constructed nomogram provides good predictive accuracy.

Relations between psychosis and emotion regulation in daily life: A systematic review and meta-analyses of studies using experience sampling methods.

Research has shown that heightened emotionality often precedes psychotic experiences. Understanding how individuals cope with these emotions is crucial for psychosis. While prior studies on emotion regulation (ER) and psychosis have mainly relied on self-report questionnaires, recent research has increasingly utilised daily life methods. This systematic review and meta-analysis seek to examine the relationship between daily ER and psychotic experiences. In total, 15 studies were included in the meta-analysis (21 in the systematic review). The results of Experience Sampling Methods (ESM) studies indicated that ER strategies of avoidance, reappraisal, repetitive thinking, and emotional suppression are not associated with psychotic experiences in daily life. The effect was not moderated by type of sample (clinical vs non-clinical), symptom (hallucination vs delusions), relation (contemporaneous vs perspective), or other methodological variables. Acceptance was found to be negatively associated with psychotic experiences, but the effect was small. These results indicate that ER in daily life might not impact directly on psychotic experiences. The results are discussed in light of conceptual and methodological limitations in the field.

Network-Wide Traffic Volume Estimation Based on Probe Vehicle Data

We propose a method for estimating traffic volumes on all roads of the network, which is relevant to various traffic use cases. By combining large-scale probe-vehicle data with stationary detector data, this method builds a model to estimate the probe-vehicle penetration rate at road level, which then allows for traffic volume estimation. Trained by penetration rates from only 60% of the stationary detectors in the Netherlands, our model is able to predict the probe-vehicle penetration rate with a mean absolute percentage error (MAPE) of 8.3%, which decreases to 6% on motorways. Increasing the proportion of detectors used for training did not significantly improve model performance. This suggests that our method can be used in countries with less-developed detector infrastructure as well, provided a minimum coverage of detectors is achieved for all regions and road classes of interest. Traffic volume estimation quality ([Formula: see text]) is constrained by the quality of the penetration rate estimation ([Formula: see text]) and the sampling error, denoted as [Formula: see text]. Using the estimated penetration rates, traffic volumes on motorways can be estimated with a [Formula: see text] of 25% within about 2 min of collecting probe-vehicle observations and 7% within a day. Thus, our traffic volume estimations can support real-time operations on motorways. For roads other than motorways, the maximum accuracy is limited to a [Formula: see text] of about 12%, and significantly longer observation periods are needed. Still, most offline use cases can be covered (e.g., annual average daily traffic).

Prevalence of FGFR2b Protein Overexpression in Advanced Gastric Cancers During Prescreening for the Phase III FORTITUDE-101 Trial.

Fibroblast growth factor receptor 2 isoform IIIb (FGFR2b) protein overexpression is an emerging biomarker in gastric cancer and gastroesophageal junction cancer (GC). We assessed FGFR2b protein overexpression prevalence in nearly 3,800 tumor samples as part of the prescreening process for a global phase III study in patients with newly diagnosed advanced or metastatic GC. As of June 28, 2024, 3,782 tumor samples from prescreened patients from 37 countries for the phase III FORTITUDE-101 trial (ClinicalTrials.gov identifier: NCT05052801) were centrally tested for FGFR2b protein overexpression by immunohistochemistry (IHC) and had evaluable results. FGFR2b positivity was defined as both any % tumor cells (TC) and ≥10% TC exhibiting moderate-to-strong (2+/3+) membranous FGFR2b staining. Prevalence was analyzed across patient and sample characteristics. FGFR2b protein overexpression at any % and ≥10%, 2+/3+ TC positivity was 37.8% (1,428/3,782 [95% CI, 36.2 to 39.3]) and 16.2% (612/3,782 [95% CI, 15 to 17.4]), respectively. Of any %, 2+/3+ TC-positive tumors, 42.9% (612/1,428 [95% CI, 40.3 to 45.4]) were FGFR2b ≥10%, 2+/3+ TC positive. FGFR2b prevalence was not notably different within multiple patient and sample characteristics examined (age, sex, collection method [biopsy v resection], collection site, location of primary tumor, and geographic region). As of the data cutoff date, we report the largest prevalence assessment of FGFR2b protein overexpression in GC with more than one third (37.8%) of patients with GC exhibiting FGFR2b protein overexpression (any % TC, 2+/3+) by a validated IHC assay. Approximately 16% of patients had FGFR2b protein overexpression in ≥10% of TC. FGFR2b prevalence was similar across geographic regions and within defined patient and sample variables regardless of the level of expression.

A novel importance scores based variable selection approach and validation using a MIR and NIR dataset.

Variable selection is important in spectral analysis for improving interpretation quality and accuracy. This study introduces a novel variable selection process, named "VMHBSC", which consists of six steps, with each letter representing one step. To demonstrate its process and advantages, two datasets were employed, a mid-infrared spectral (MIR) dataset (234×7468, sample number×variables) of Chenpi samples (a traditional Chinese medicinal material derived from the dried peel of mature tangerines) and a near-infrared spectral (NIR) dataset (16000×256) for modeling competition. In the MIR dataset, VMHBSC selected 3 important variables from all 7468 variables, and models established using Decision Trees (DT), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) achieved higher accuracy compared to models using other variable selection methods. For the NIR dataset, VMHBSC selected 24 important variables from all 256 variables. Based on these 24 common variables, three hybrid models (VMHBSC-DT, VMHBSC-GBDT and VMHBSC-XGBoost) were also established and shown stable performance. These findings indicate the effectiveness of the VMHBSC process in enhancing model performance and robustness.

MINIMAL CHANGE DISEASE IN PEOPLE LIVING WITH HIV: A CASE REPORT AND REVIEW OF THE LITERATURE.

Various glomerular diseases are associated with human immunodeficiency virus (HIV) infection. However, the incidence of minimal change nephrotic syndrome has scarcely been reported. We describe a patient with a stage 4 HIV infection complaining of swelling in his face, hands, feet, genitals and an enlarged abdomen. Urinalysis revealed +3 proteins with normal urine sediment, and 2.1 g protein was found in the 24-hour analysis. Renal ultrasound showed bilateral glomerulopathies with hyperechoic and cortical thickening, a normal kidney size, and ascites. Kidney biopsy revealed acute tubular injury without HIV-associated nephropathy (HIVAN) features. The patient was treated with salt restriction, diuretics, captopril, methylprednisolone, and combined ART for 2 weeks and showed clinical improvement. Two weeks after the remission, the patient came to the outpatient department with a history of a 3-day cough with rust-colored sputum, fever, malaise, and shortness of breath. The lung auscultation revealed bilateral rhonchi and the chest x-ray result suggesting pneumonia. The patient was diagnosed with sepsis associated with healthcare-associated pneumonia but was not willing to be hospitalized and passed away at home. This study is limited to single-case nature and the possibility of sampling error. However, this case encourages further study in the field of HIV-associated renal diseases in providing clear recommendation in the management in special population.

Value of Mac-2 Binding Protein Glycosylation Isomer (M2BPGi) in Assessing Liver Fibrosis in Metabolic Dysfunction-Associated Liver Disease: A Comprehensive Review of its Serum Biomarker Role.

Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) is a broad condition characterized by lipid accumulation in the liver tissue, which can progress to fibrosis and cirrhosis if left untreated. Traditionally, liver biopsy is the gold standard for evaluating fibrosis. However, non-invasive biomarkers of liver fibrosis are developed to assess the fibrosis without the risk of biopsy complications. Novel serum biomarkers have emerged as a promising tool for non-invasive assessment of liver fibrosis in MAFLD patients. Several studies have shown that elevated levels of Mac-2 binding protein glycosylation isomer (M2BPGi) are associated with increased liver fibrosis severity in MAFLD patients. This suggests that M2BPGi could serve as a reliable marker for identifying individuals at higher risk of disease progression. Furthermore, the use of M2BPGi offers a non-invasive alternative to liver biopsy, which is invasive and prone to sampling errors. Overall, the usage of M2BPGi in assessing liver fibrosis in MAFLD holds great promise for improving risk stratification and monitoring disease progression in affected individuals. Further research is needed to validate its utility in clinical practice and establish standardized protocols for its implementation.