Vancomycin is a widely used antibiotic with a narrow therapeutic window and considerable pharmacokinetic variability, necessitating accurate and precise dosing. Population pharmacokinetics (popPK) models have become essential for facilitating model-informed precision dosing (MIPD) of vancomycin. We aimed to summarise and compare popPK models of vancomycin and evaluate MIPD software modules incorporating these models. We systematically searched PubMed, EMBASE, and reference lists of relevant articles from inception through 01 January 2026 to identify articles describing the development of compartmental, one-stage parametric popPK models based on data from adult patients (aged ≥ 18 years) receiving intravenous vancomycin. We extracted and summarised key information on study design, patients, vancomycin dosing regimens, sampling strategies, quantification methods, modelling techniques, and covariates. We contacted providers of MIPD software tools and invited them to complete an online questionnaire assessing the features and clinical integration of their vancomycin module. We evaluated the incorporated models and their clinical applicability. We identified 99 adult-applicable vancomycin popPK models across 97 articles: 48 (48.5%) were one-compartment, 47 (47.5%) two-compartment models, and 4 (4.0%) three-compartment models. Kidney function estimators and body weight metrics were the most commonly retained covariates on clearance and volume of distribution, respectively. Of 18 identified MIPD software tool providers, 13 (72.2%) completed the questionnaire, confirming the inclusion of vancomycin modules. These tools incorporated a total of 101 vancomycin models, of which 48 were intended for adults. Three tools had been evaluated in prospective non-interventional studies, and two in a prospective interventional trial. Five tools were certified as conforming to European Union (EU) regulatory standards under the Medical Devices Directive and were in the process of obtaining EU conformity under the Medical Device Regulation. Mapping published models to tool implementations revealed partial overlap, limited transparency on model selection and lack of model‑level external validation, underscoring the need for structured evaluation before routine clinical adoption. This review presents a comprehensive overview of vancomycin popPK models and MIPD software modules for adult patients. Our findings highlight the diversity among popPK models and the need for standardised reporting, transparent model selection, and prospective evaluation to support clinical implementation of MIPD.
Glucosylated-sterols can be synthetized endogenously, absorbed through the diet or derive from bacterial infection. Their clinical relevance is currently underestimated, even though their imbalance has been associated with an increased risk of neurodegeneration over the lifespan. We studied the detrimental effects elicited by dietary consumption of the plant-derived β-sitosterol β-D-glucoside (BSSG), known to be associated with the occurrence of ALS-PDC, to elucidate its potential mechanism of action. Zebrafish larvae and adults, as well as mice, were treated with BSSG administered directly in the water or via customized food pellet, respectively. Since the intestine was identified as the primary target tissue, its morphological and functional characteristics were assessed, together with transcriptional profiling and gut microbiota sequencing. Ex vivo analysis of zebrafish gut contractility was applied to evaluate intestinal neuromuscular responses. Mutant and transgenic zebrafish lines were used to explore a potential BSSG mechanism of action. BSSG induced intestinal inflammation in both zebrafish and mouse models. This previously unknown effect was evidenced by gut dysmotility and inflammatory response. Transcriptomic analyses revealed increased expression of inflammation-related genes in the intestine of both zebrafish and mice, while preliminary gut microbiota analyses suggested the onset of dysbiosis. Transgenic and mutant zebrafish lines, depleted of genes involved in glucocorticoids synthesis and activity, evidenced that BSSG likely interacts with the glucocorticoid receptor, potentially impairing its canonical anti-inflammatory activity. We identified novel pathways altered by dietary BSSG exposure. This molecule appears to initially induce gut inflammation, leading to changes in intestinal morphology and function, and may contribute to neurodegeneration through disruption of the well-known gut-brain axis.
Differentiating between spinal tuberculosis, pyogenic (bacterial) spondylitis and spinal metastasis remains a major diagnostic challenge because their radiological features often overlap. Delayed or incorrect diagnosis may lead to inappropriate treatment, permanent disability or death. To develop and evaluate deep learning models for automated classification of spinal tuberculosis, pyogenic infection, and spinal metastasis using magnetic resonance imaging (MRI). T2-weighted sagittal MRI scans from 120 patients (40 per disease group) with pathologically or microbiologically confirmed diagnoses between 2014 and 2019 were retrospectively analyzed. Lesion regions were manually annotated by radiologists, and data were split into 80% training and 20% testing sets at the patient level. Extensive data augmentation (rotation ± 5°, zoom 1.1-1.2×, shearing ± 5°, grid distortion 2 × 2) was applied to mitigate overfitting. Three models were trained and compared: (1) a single-layer perceptron baseline, (2) a custom dense neural network (2 × 1024 neurons), and (3) pre-trained convolutional neural networks (ResNet50, VGG16, InceptionV3). Model performance was evaluated using accuracy, precision, recall, and F1-score on both whole and segmented images. After augmentation, 1,000 synthetic samples were generated per class. The baseline model achieved 27-33% accuracy, whereas the dense and pre-trained models achieved 98-100% accuracy on the test set. Although pre-trained networks demonstrated marginally higher performance, the difference compared with the dense model was not statistically significant. Activation heatmaps revealed inconsistent localization of attention regions, suggesting potential overfitting and limitations in visualization interpretability. Deep learning models demonstrated strong potential in distinguishing between spinal tuberculosis, bacterial spondylitis, and spinal metastasis on MRI. However, the near-perfect performance likely reflects dataset homogeneity and augmentation effects rather than full generalization. External, multi-center validation and improved interpretability methods (e.g., Grad-CAM) are warranted to confirm clinical applicability and ensure reliable decision support for radiologists.
Nursing is intrinsically demanding, exposing practitioners to substantial workloads, emotional labour, and systemic healthcare challenges that contribute to significant psychological distress. In Ghana, the compounding effects of structural deficiencies within the healthcare system make nurses particularly vulnerable. However, research on culturally informed coping strategies within this population remains limited. This study examined the association between psychological distress and Africentric coping strategies among 248 nurses in public healthcare facilities in the Central and Greater Accra regions of Ghana. Data were collected using the Hospital Anxiety and Depression Scale (HADS) and the Africultural Coping Systems Inventory (ACSI). Psychological distress was operationalised as the summed HADS total score (range: 0-42). Descriptive statistics, Pearson correlation analysis, and multiple linear regression models were used to examine associations. The mean HADS anxiety score was 12.22 (SD = 3.14) and mean depression score was 11.27 (SD = 3.06), indicating elevated anxiety and moderate depressive symptoms. Mean ACSI subscale scores were: Cognitive/Emotional Debriefing M = 2.11 (SD = 0.61), Spiritual-Centred Coping M = 2.18 (SD = 0.73), Collective Coping M = 1.96 (SD = 0.58), and Ritual-Centred Coping M = 0.75 (SD = 0.34). Spiritual (r = - .268, p < .05) and collective coping (r = - .587, p < .05) were significantly and negatively associated with psychological distress; cognitive and ritual coping were not. In regression models, only spiritual (β = -2.681) and collective (β = -0.811) coping demonstrated significant negative associations with distress. Demographic factors including gender, marital status, and professional rank were significant predictors of distress. These findings highlight the importance of culturally and contextually tailored mental health support that incorporates spiritual resources and peer support networks. The study provides empirical evidence to guide healthcare policy, workplace mental health programming, and culturally responsive nursing practice in sub-Saharan Africa. Not applicable.
Accurate modelling of airflow and aerosol/particle dynamics within the human respiratory system is essential for improving inhalation-based drug delivery strategies and for evaluating the health risks associated with hazardous particulates. Owing to the complex geometry of the human airways, inter-individual anatomical variations, and variable breathing patterns, this process constitutes a highly complex multiphase flow problem. To address the constraints associated with in vivo and in vitro techniques, in silico approaches based on computational fluid dynamics (CFD) have been extensively utilized to examine respiratory airflow and aerosol dynamics at microscopic scales. The aim of this study is to review recent CFD-based approaches for modeling airflow and aerosol behavior in the human respiratory system, summarize key modeling strategies and influential parameters, and identify future research directions. Recent studies indicate a transition of respiratory tract models toward more physiologically realistic and whole-lung representations. These studies demonstrate that coupling CFD with particle models enables reliable prediction of aerosol transport and deposition by accounting for the effects of geometric variations, breathing conditions, turbulence characteristics, and particle physical and chemical properties. CFD-based modeling, particularly when integrated with particle dynamics, provides a powerful and reliable framework for investigating airflow and aerosol behavior in the human respiratory system. Continued advancements toward realistic whole-lung models and improved representation of physiological and particle-related parameters are expected to further enhance predictive accuracy and support both clinical and environmental health applications.
Routine use of surgical drains after abdominal operations has largely been abandoned over the past decades. Studies have failed to demonstrate benefits of routine drainage following liver, gallbladder, gastric, and colorectal surgeries. Until recently, intraoperative placement of abdominal drains was the gold standard in pancreatoduodenectomies (PDs) due to concerns about uncontrolled postoperative pancreatic fistula (POPF). A large randomized trial in 2014 reported increased mortality in patients without postoperative drain placement. However, as the study did not stratify participants based on their preoperative risk of developing a POPF, further research is needed. Limited evidence from a non-randomized cohort suggests that omitting drains may be safe in very low-risk settings. However, a larger comparative study, including a broader range of PD cases, is necessary to confirm these findings. This is a two-arm, randomized, controlled, non-blinded, multicenter trial comparing intra-abdominal drain placement with no drain placement during planned pancreatoduodenectomies (PDs). Eligible patients who meet the inclusion criteria will be assessed for their individual risk of postoperative pancreatic fistula (POPF) using a risk scoring system. They will then be randomized into either the drain placement or no drain placement group. The groups will be compared using the chi-square test for categorical variables and Fisher's exact test. Logistic regression models will be used to calculate odds ratios for morbidity. Univariable and multivariable models will assess the impact of drain placement on clinical outcomes. This trial aims to determine whether omitting routine intraoperative drain placement reduces the risk of complications in patients undergoing pancreatoduodenectomy (PD). It will provide level 1 evidence on the association between routine intra-abdominal drainage and postoperative complications in patients with a low to intermediate risk of developing a postoperative pancreatic fistula (POPF). The findings will contribute to future treatment guidelines by expanding the available knowledge on optimal drainage strategies. ClinicalTrials.gov Identifier: NCT05270564. Registered on February 16 2022.
To our knowledge, a systematic comparison of nutrients contribution to mortality in large scale cohort of middle-aged to elderly individuals has not yet been done. We aim to investigate the associations between most of the available nutrients and all-cause and disease-specific mortality, and explored their joint effect on mortality risk. A total of 208,312 participants from the UK Biobank (UKB) with baseline 24-hour dietary recall data were enrolled. Cox proportional hazards models were used for a nutrients-wide association analysis of all-cause mortality and disease-specific mortality. Mixed-effects analyses were further conducted to evaluate the combined effects of nutrients significantly associated with mortality risk by Bayesian kernel machine regression (BKMR) and Quantile G-Computation (Qgcomp) regression models. No significant associations were found between total energy, total protein, total lipid, or total carbohydrate intake and all-cause mortality risk. However, energy density was moderately and positively associated with all-cause mortality (HR=1.017, 95%CI: 1.004-1.030). Nutrient type and quality exhibited significant impacts: plant-derived protein (HR=0.995, 95%CI: 0.992-0.998), plant-derived lipids (HR=0.997, 95%CI: 0.995-0.999), were negatively associated with all-cause mortality. Among carbohydrates, starch, lactose, and intrinsic/milk sugars showed protective effects, while free sugars, non-milk extrinsic sugars, sucrose, and maltose were positively associated with increased mortality risk. For minerals and vitamins, copper, manganese, total iron, non-haem iron, vitamin E, riboflavin, biotin, and pantothenic acid exhibited inverse associations with all-cause mortality. Mixed-effects analyses revealed cumulative inverse trends of beneficial nutrients and positive trends of harmful nutrients on mortality risk, with manganese, maltose, biotin, and niacin being key contributors. Disease-specific analysis showed that energy density and certain sugars were positively associated with neoplasms mortality; multiple sugars were linked to nervous system disease mortality; and alcohol, maltose were positively associated with digestive system disease mortality, while most macronutrients, minerals, vitamins, and fibre had protective effects. Sodium and chloride were positively associated with circulatory system disease mortality. Total intake of major macronutrients was not significantly associated with mortality risk, but nutrient type and quality played critical roles. Plant-derived nutrients, specific minerals, vitamins, dietary fibre, and natural carbohydrates were protective against mortality, whereas refined sugars and high energy density were detrimental. These findings highlight the importance of dietary quality in reducing mortality risk and provide evidence for developing targeted dietary recommendations.
Machine Learning (ML) models have achieved outstanding performance in predicting post-surgical survival. However, the "black-box" nature of ML models restricts their clinical application. This study aims to develop and validate a clinically feasible, machine learning-based online prediction system for predicting the 5-year survival status of patients with prostate cancer (PCa) after surgery. This study conducted a retrospective analysis of clinical data from 300 older adults with PCa aged ≥ 65 years. LASSO regression, random forest (RF), and recursive feature elimination (RFE) were employed to screen for clinical parameters. Subsequently, the cohort was split into a training set and a test set at a 7:3 ratio, and 25 machine learning approaches were utilized for comparative assessment to determine the optimal model. Calibration curves were applied to evaluate the performance of each model, while decision curve analysis (DCA) was adopted to assess their clinical usefulness. In addition, SHAP (Shapley Additive exPlanations) values were used to interpret the model features. Finally, the Shiny framework was employed to develop an online prediction system. The intersection of the three feature selection algorithms identified 13 clinical parameters: Age, ALB, BUN, CRE, HB, PLT, PT, PSA, GS, PNI, PSM, pT and pN. Comparing 25 machine learning algorithms, LightGBM gave the best results. Its performance metrics were: Accuracy 0.9328, Sensitivity 0.9074, Specificity 0.9538, Positive Predictive Value (PPV) 0.9418, Negative Predictive Value (NPV) 0.9118, AUC 0.9778, Recall 0.9074, F1 Score 0.9143. SHAP values revealed the contribution of each feature in the LightGBM model. This study successfully developed a 5-year postoperative survival prediction model for prostate cancer patients. The model demonstrated favorable predictive performance in the test set, which may provide a reference for clinical decision-making. Further multi-center external validation is required to clarify its clinical application value in the future.
High-dose methotrexate (HD-MTX)-induced nephrotoxicity remains a critical clinical challenge in primary central nervous system lymphoma (PCNSL) treatment, yet quantitative tools for individualized risk prediction are currently lacking. Here, a population pharmacokinetic/pharmacodynamic analysis was performed using 5,918 plasma concentration samples from 743 Chinese adult patients. Nonlinear mixed-effects modeling was employed to establish toxicodynamic structural models driven by MTX alone, 7-Hydroxy-MTX alone, or their combination. Although all models exhibited comparable predictive performance, the MTX linear model was selected as it offered the optimal balance between predictive performance and clinical feasibility. Covariate analysis identified hemoglobin as the most significant predictor, with higher levels correlating with reduced susceptibility to renal injury. To facilitate clinical translation, exposure-toxicity probability curves were constructed, enabling clinicians to identify dynamic concentration thresholds tailored to specific toxicity grades (Grade 1 vs. ≥2 nephrotoxicity) and individualized risk tolerance. Using a 10% risk probability for Grade ≥2 nephrotoxicity as the safety monitoring threshold, the corresponding MTX concentration thresholds at 24, 48, and 72 h were 9.71, 0.81, and 0.26 μmol/L, respectively. This quantitative framework serves as an exploratory tool to complement therapeutic drug monitoring, assisting in the early identification of MTX-induced renal injury among PCNSL patients with normal to mildly impaired renal function.
Polygenic risk scores (PRS) have emerged as important tools for quantifying inherited susceptibility to cancer, and are increasingly combined with environmental and lifestyle factors into composite risk scores (CRS). In this context, environmental inputs should be understood not as isolated covariates, but as components of the human exposome, encompassing cumulative, time-varying, and interacting exposures across the life course, which fundamentally shape cancer risk alongside inherited susceptibility. These approaches are often discussed as candidates for precision prevention and screening, yet their evidentiary basis spans heterogeneous study designs, outcomes, and methodological assumptions. Here, we provide an integrated review of genetic, environmental, and composite cancer risk models, explicitly distinguishing etiologic association from predictive performance and clinical translation. We synthesize evidence from large genome-wide association studies, cohort and case-control analyses, and recent CRS evaluations using both narrative assessment and structured quantitative summaries. Across cancer sites, PRS and CRS consistently stratify relative risk, with monotonic increases in odds ratios across score percentiles. However, gains in discrimination metrics such as the area under the curve or C-index are generally modest and heterogeneous, and calibration performance varies substantially across populations and settings. External validation and multi-ancestry evaluations remain limited, and methodological challenges, including overfitting, population stratification, and model transportability, are frequently under-reported. We argue that current evidence supports the use of PRS and CRS primarily as tools for risk stratification, prioritization, and risk-enriched research designs, rather than as stand-alone clinical decision systems. The most near-term translational value lies in targeted screening strategies, prevention trials, and population-level risk assessment, provided that calibration, governance, and equity considerations are explicitly addressed. We conclude by outlining key methodological and data requirements needed to advance CRS from exploratory models toward robust, population-appropriate tools in cancer prevention.
The global burden of injury is a key indicator for assessing public health and medical needs. During the COVID-19 pandemic, this burden was impacted. This study aims to explore how the pandemic influenced the injury burden globally and regionally, and provide recommendations to relieve this burden. The burden of injury-related data is derived from the Global Burden of Disease (GBD) 2021 Study. Autoregressive integrated moving average (ARIMA) and ARIMA-Long short-Term Memory (LSTM) models were adopted for counterfactual inference to predict the scenario without the pandemic. During the COVID-19 pandemic, the observed global age-standardized incidence rate (ASIR) of injury exceeded the predicted value by 107.31 per 100,000, and the observed age-standardized prevalence rate (ASPR) was higher than the predicted value by 102.81 per 100,000. Self-harm and interpersonal violence saw the largest deviations above predicted values in Europe and parts of Asia. Specifically, Armenia's ASIR was 7,829.33 per 100,000 higher than predicted, and its ASDR exceeded projections by 5,186.32 per 100,000. Besides, traffic injuries exceeded predicted levels most significantly in Southeast Asia, with Indonesia's ASIR 25.48 per 100,000 higher than projected. And the observed ASIR of unintentional injuries in China was 379.61 per 100,000 higher than the predicted value. During the COVID-19 pandemic, the global burden of injuries surpassed the predicted levels for a scenario without the pandemic in 2020-2021, especially in Europe and Asia. In addressing an epidemic, prevention and emergency measures for high-burden injury types and key populations should be strengthened based on local socio-cultural contexts.
Fetuin-A represents a novel molecular target involved in the complex pathogenesis of metabolic disorders. This study aimed to evaluate its association with obesity, type 2 (T2DM) and type 1 (T1DM) diabetes and its correlation with non-invasive liver assessment. 105 patients (38 with obesity without T2DM, 30 with T2DM, and 37 with T1DM) and 13 controls were included. All participants underwent transient elastography (TE) with controlled attenuation parameter (CAP), and liver stiffness measurement (LSM), clinical and biochemical data (including fetuin-A). Fetuin-A was significantly higher in all clinical groups than controls, with the greatest increase observed in obesity without T2DM. Fetuin-A correlated positively with measures of adiposity (BMI, waist circumference, waist to height ratio), triglycerides, and non-invasive indicators of liver steatosis (Fatty Liver Index and CAP), while showing no association with fibrosis (defined by LSM ≥ 7.9 kPa). In age- and sex-adjusted models, fetuin-A remained independently associated with obesity and T1DM, whereas T2DM showed a negative but not significant association. Fetuin-A was significantly higher in steatosis, with good discriminatory ability (AUC 0.84, 95% CI 0.77-0.91) and a sensitivity of 0.75 and specificity of 0.74 at the optimal Youden threshold. Negative association with male sex and positive association with age was also observed. Results confirm fetuin-A as a molecular signature of metabolic disorders, mediating the cross-talk between liver and adipose tissue. Further studies are needed to validate it as a useful biomarker for the early diagnosis and monitoring of liver and metabolic disease, including obesity, T2DM, T1DM, and MASLD, and to reach a consensus on reference values definition.
Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β (Aβ) peptides as plaques in the brain parenchyma and as deposits in the cerebral vasculature. Early detection of amyloid plaques and deposits is imperative for diagnosing AD before the onset of cognitive decline. Magnetic resonance (MR) imaging using Gd (III)-based agents for contrast enhancement and plaque targeting provides a promising avenue. However, there remains a challenge due to the limited permeability of these contrast agents across the blood-brain barrier (BBB), which restricts its delivery. Furthermore, clearance mechanisms in the brain also reduce retention of contrast agents. To identify mechanisms that limit the success of contrast agents, we investigated the pharmacokinetics and the brain distribution of contrast agent, Gd[N-4ab/Q-4ab]Aβ30, using AD transgenic mouse models and compartmental modeling. Our results demonstrate that the contrast agent is internalized by parenchymal cells, which limits its availability to bind to extracellular plaques. Sensitivity analysis conducted on the compartmental model identified systemic clearance and plasma-to-brain influx as key parameters that limit the delivery of the contrast agent to the brain. The analysis also highlights the BBB as a formidable barrier for delivery and the importance of improving BBB permeability to increase the accumulation of the contrast agent in the brain. Furthermore, model simulations revealed that glymphatic drainage contributes to the poor retention and rapid elimination of the contrast agent from the brain. By elucidating the role of these biological processes and parameters, this study contributes to understanding factors limiting contrast agent efficacy in amyloid plaque imaging in the AD brain. These findings also reveal important targets for optimizing contrast agent design to improve its brain delivery.
The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines aim to improve the reporting of observational studies, including cohort, case-control, and cross-sectional designs, relevant to assessing associations, risks, and outcomes in real-world clinical settings. They are conceived to optimize the meaningfulness of epidemiological and clinical studies, aligning them with the Aristotelian "mesotes" curve, namely, the principle of achieving balance and avoiding extremes to best reflect the most truthful approximation of reality. This commentary addresses situations where strict adherence to STROBE guidelines may be impossible or inappropriate, potentially distorting results, and shows how statistical tools can mitigate these issues. Specific challenges arise in real-life randomization scenarios, situations lacking placebo arms, and in correcting for multiple comparisons. We discuss these challenges, examine the role of Bonferroni and similar corrections, and propose alternative approaches such as false discovery rate (FDR) and Bayesian hierarchical models. We illustrate these points with examples from the literature and a simulation study evaluating p-value adjustments in multiple hypothesis testing. This work provides a framework for researchers to navigate STROBE guidelines thoughtfully, ensuring that observational studies are both rigorous and relevant.
No epidemiological studies have systematically evaluated the associations between prenatal exposure to organophosphate esters and polychlorinated biphenyls and the risk of congenital heart disease (CHD) in offspring. Moreover, the potential modifying role of maternal B-vitamin status in persistent organic pollutants (POPs)-CHD associations has not been examined. We therefore investigated the cardiotoxic effects of prenatal POPs exposure and evaluated effect modification by maternal B-vitamin levels. A multicenter case-control study was conducted in China from 2016 to 2021, including 425 participants. Thirty POPs and seven plasma B vitamins were quantified using high-resolution mass spectrometry. Single-exposure associations were examined by logistic regression, while multipollutant effects were assessed through Bayesian kernel machine regression (BKMR) and Weighted Quantile Sum (WQS) models. Potential effect modification by B vitamins was systematically evaluated. Prenatal exposure to p-cresyl diphenyl phosphate was associated with an increased risk of CHD (odds ratio [OR] = 1.36, 95% confidence interval [CI]: 1.08, 1.71). Mixture analyses consistently showed an increasing trend in CHD risk with higher exposure to the POPs mixture, with the WQS model yielding a statistically significant association (OR = 1.20, 95% CI: 1.03, 1.40). Higher concentrations of pyridoxamine, pyridoxal, and vitamin B12 were inversely associated with CHD risk, and mixture analyses using both BKMR and WQS regression further demonstrated a significant negative association between the overall B-vitamin mixture and CHD risk. In the interaction analysis, B vitamins significantly modified the association between prenatal POPs exposure and CHD risk (OR = 0.14, 95% CI: 0.04, 0.52). This study provides the first population evidence that prenatal POPs exposure is associated with increased CHD risk and adequate maternal B-vitamin levels may attenuate the developmental cardiotoxicity of emerging flame retardants, highlighting the importance of maternal nutritional status in modifying environmental risk factors for CHD.
Congenital cytomegalovirus (cCMV) infection is a leading cause of non-genetic sensorineural hearing loss and neurodevelopmental impairment. This study aimed to systematically evaluate and compare the diagnostic accuracy of saliva polymerase chain reaction (PCR) and dried blood spot (DBS) PCR for detecting cCMV in newborns. A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines. PubMed, Cochrane Library, ProQuest, and Google Scholar were searched for studies published between 2010 and 2025. Eligible studies reported sufficient data to construct 2 × 2 contingency tables and used urine PCR or culture as reference standards. Pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratios, and summary receiver operating characteristic (SROC) curves were estimated using random-effects and bivariate models. Nineteen studies comprising 103,669 neonates were included. Saliva PCR demonstrated high pooled sensitivity (0.95, 95% CI: 0.91-0.97) and perfect specificity (1.00, 95% CI: 0.99-1.00). In contrast, DBS PCR showed lower pooled sensitivity (0.72, 95% CI: 0.45-0.89) while maintaining high specificity (0.99, 95% CI: 0.92-1.00). Subgroup analysis revealed significantly higher sensitivity for saliva PCR compared with DBS PCR (p = 0.004). SROC analysis indicated superior overall diagnostic performance for saliva PCR (AUC = 0.72) relative to DBS PCR (AUC = 0.56). Substantial heterogeneity was observed, particularly among DBS studies. Saliva PCR demonstrates superior sensitivity and overall diagnostic accuracy for early detection of cCMV compared with DBS PCR, while both methods show high specificity. These findings support saliva PCR as the preferred modality for newborn screening where feasible, while DBS PCR may serve as a complementary or alternative approach in large-scale or resource-limited settings. Not applicable.
There is a clear need for more effective screening tools for patients undergoing hemodialysis, especially considering the wide variation in depression screening options and the lack of a gold standard assessment tool for this specific population. This study aims to examine the validity and reliability of the Turkish version of the Depression Inventory for Maintenance Hemodialysis Patients (DI-MHD). This was a methodological study of the translation, cultural adaptation, and psychometric validation of the DI-MHD. Two hundred eighty-three patients from two hemodialysis units in Turkey were recruited for the study using convenience sampling. The following data were collected from the participants: descriptive characteristics, DI-MHD scores, Beck Depression Inventory (BDI) scores, and inflammatory biomarkers. The BDI was used as a reference instrument to evaluate the concurrent validity of the DI-MHD. Exploratory and confirmatory factor analyses were implemented to test the construct validity. The test‒retest method was used to test the reliability and consistency of the scale over time. IBM SPSS version 22.0 and AMOS 24.0 were used for analysis. Exploratory factor analysis revealed a dominant single factor explaining 37.21% of the total variance (extracted variance = 33.52%). Although confirmatory factor analysis demonstrated acceptable fit for the correlated four-factor and second-order models, high latent factor correlations (φ = 0.677-1.052) and strong higher-order loadings (0.708-1.041) indicated limited discriminant validity among dimensions. Therefore, the one-factor model was retained as the most parsimonious and theoretically coherent solution. The Turkish version of the DI-MHD is a valid and reliable tool that can be used to evaluate and classify the depression levels of patients in hemodialysis units. ClinicalTrials.gov NCT. First registration date: 2025-02-20.
Perinatal depression is a common mental health problem, affecting about 10-15% of women during pregnancy and after childbirth. The condition leads to detrimental health impacts on mothers and their children. Despite high prevalence and impact, diagnosis of depression among pregnant women and women with young children is not done routinely in most resource-limited settings. Thus, the scope of perinatal depression remains underestimated. This study assessed perinatal depression and associated factors in pregnant women and mothers of children under 2 years old in rural Rwanda. This cross-sectional quantitative study was conducted in four sectors in Burera district in Northern Rwanda. It involved all pregnant women in their second and third trimesters and mothers with children below 2 years old. A questionnaire consisting of the Edinburgh Postnatal Depression Scale, Household Hunger Scale, Parental Sense of Competence - Efficacy Scale, modified Medical Outcomes Study Social Support Survey, and questions about intimate partner violence experience was used. Bivariate and multivariate regression models were used to identify the factors influencing depression status. This study surveyed 1464 participants. Perinatal depression symptoms were found in 24.5% of participants. Being single (OR 4.8; 95%CI 1.5-14.7), having more than 6 children (OR 2.1; 95%CI 1.0-4.4), experiencing verbal abuse (OR 1.47; 95%CI 1.0 -2.1), sexual abuse (OR 1.87; 95%CI 1.3-2.7) and physical abuse (OR1.5; 95%CI 1.1-2.4) from an intimate partner, and having severe household food insecurity (OR 3.0; 95%CI 1.9-4.9) were found to significantly influence the odds of having perinatal depression. Perinatal depression is a highly prevalent and multifactorial issue in Burera district. Lack of social support, intimate partner violence, and food insecurity predispose mothers to perinatal depression. Addressing these elements is crucial for the effective prevention and management of perinatal depression.
Cognitive diagnostic assessment (CDA) enables direct exploration of participants' cognitive structures or psychological latent traits (referred to as attributes), offering unique advantages within psychological methodologies. The Q-matrix, which delineates the relationship between items and attributes in CDA, is crucial for accurate diagnosis. However, ensuring the accuracy of the Q-matrix in practical applications is often challenging. Constructing a Q-matrix typically requires extensive calibration efforts from both test developers and domain experts, and even then, issues of accuracy and subjectivity remain. Although various Q-matrix validation methods have been developed to improve its quality, their implementation often presents a steep technical barrier for typical psychological researchers. These challenges have limited the broader application of CDA in psychological research. This paper provides a systematic review of Q-matrix validation methods under saturated cognitive diagnosis models (CDMs) and introduces Qval, a user-friendly and powerful R package that offers a one-stop solution for implementing a wide range of state-of-the-art validation procedures, including parameter estimation, validation methods, iterative procedures, and search algorithms. The Qval package leverages C++ code and parallel computing to improve computational efficiency. Additionally, this paper provides detailed guidance on how to implement Q-matrix validation procedures effectively.
Falls are a leading cause of injury and disability among older adults worldwide, yet data from sub-Saharan Africa remain scarce. This study aimed to estimate the prevalence of fallers, recurrent fallers, and individuals at high risk of falls; identify associated sociodemographic and health-related factors; and examine the relationship between functional fitness and fall outcomes among older adults in Ghana, a country representative of the broader sub-Saharan African context. We conducted a population-based cross-sectional study among 639 community-dwelling adults aged ≥ 60 years in urban and rural Ghana, using multi-stage sampling. Fall history over the past 12 months was assessed through self-report, and fall risk was evaluated using the 12-item Fall Risk Questionnaire. Functional fitness was measured using eight performance tests adapted from the Senior Fitness Test and Short Physical Performance Battery. Associations between fall outcomes, sociodemographic and health factors, and fitness measures were analysed using Poisson regression with robust variance and negative binomial regression models. The prevalence of fallers, recurrent fallers, and high fall risk was 24.6%, 10.3%, and 30.2%, respectively. Female sex was strongly associated with both fallers and recurrent fallers. Rural residence was significantly associated with recurrent fallers. Moderate pain and high sedentary behaviour, were also linked to fallers. Fall risk scores were strongly associated with reported fall events, with psychological and strength-related items contributing most to fall outcomes. Poor lower limb strength, assessed using the five-times sit-to-stand test, was significantly associated with recurrent fallers (PR = 1.08; 95% CI: 1.04-1.13), while declines in any fitness domain were associated with elevated fall risk. Falls are common among older adults in Ghana, with risk influenced by sex, residence, and functional status. Functional fitness plays a central role in fall risk, underscoring the value of targeted interventions to promote safe and healthy ageing in sub-Saharan Africa.