Tag: pubmed

Mem Cognit. 2026 Jan 19. doi: 10.3758/s13421-025-01841-7. Online ahead of print.

ABSTRACT

Event-files are a central concept in human action control as they integrate perception and action. The basic idea is that event-files comprise internal representations of stimuli, motor programs, and sensory effects of actions that are bound together, maintained for some time, and then ultimately decay. Yet the exact nature of the decay of event-files remains elusive. Most previous studies analyzed the decay only on a very coarse-grained scale typically with only two or three different response-stimulus intervals (RSI). Because available data are inconsistent, the decay function could as yet not be delineated, particularly whether it is linear or resembles a typical forgetting curve. In the present study, we analyzed the decay of event-files using nine different RSIs between 400 ms and 2,000 ms in a large sample (N = 156) of neurotypical participants. If event-file decay mimics the typical forgetting curve, the concept of event-files can be better connected to the memory literature. Using unsupervised cluster analysis, the results showed, however, a more complex pattern. In fact, there were different decay functions across participants with only a subset showing a forgetting curve suggesting that (i) event-file decay functions are modulated by interindividual differences and (ii) that the previously observed power function might be a statistical artifact due to averaging. At least in the time range investigated in this study, event-files decay only for a subset of participants. Possible underlying processes are discussed.

PMID:41555140 | DOI:10.3758/s13421-025-01841-7

Updates Surg. 2026 Jan 19. doi: 10.1007/s13304-026-02532-x. Online ahead of print.

ABSTRACT

BACKGROUND AND AIM: Statistical cure occurs when the mortality of a specific population returns to mortality values of the general population. Whether antiviral therapy is associated with achievement of statistical cure of patients with hepatocellular carcinoma (HCC) undergoing liver resection (LR) remains unknown. We aimed to clarify this issue.

METHODS: We enrolled 1004 patients with HCC who underwent LR. We evaluated overall survival (OS) as the primary survival measure for estimating cure fractions through a mixture model.

RESULTS: The probability of resection enabling patients with HCC to achieve the same life expectancy as that of the general population was 48.6%. The multivariable cure model revealed that age ≤ 65 years, 7th edition pathological American Joint Committee on Cancer (AJCC) stage 1, no cirrhosis, receipt of antiviral therapy, and a Model for End-Stage Liver Disease (MELD) score of ≤ 9 were predictors of cure after LR.

CONCLUSIONS: About five in 10 patients could be considered cured after LR for HCC. In addition to tumor- and liver-related variables, receipt of antiviral therapy was associated with achievement of statistical cure. We highlight the importance of antiviral therapy for maximizing outcomes of resection for HCC.

PMID:41555128 | DOI:10.1007/s13304-026-02532-x

Saudi Dent J. 2026 Jan 20;38(2):6. doi: 10.1007/s44445-025-00098-9.

ABSTRACT

This study aimed to evaluate the effects of photobiomodulation therapy (PBM) on post-operative pain and wound healing following the extraction of mandibular third molars. A split-mouth, double-blind, placebo-controlled, randomized clinical trial was conducted at the Faculty of Dentistry, José Antonio Páez University. Patients requiring bilateral surgical extraction of impacted and retained mandibular third molars in comparable anatomical positions were enrolled. Individuals with soft or hard tissue pathologies or systemic inflammatory conditions that could interfere with healing were excluded. One side of the jaw was randomly assigned as the treatment side, whereas the contralateral side served as the control. The treatment side received photobiomodulation (PBM) via a 980 nm diode laser (SOLASE PRO LASER, LAZON®) immediately after surgery and at 24, 48, and 72 h postoperatively. The primary outcome variables were wound healing and pain intensity. Pain was assessed via a visual analog scale (VAS) at 24, 48, 72, and 168 h. Wound healing was clinically evaluated on postoperative days 3, 7, and 14. Comparative analysis was performed via two-way ANOVA and repeated-measures ANOVA. A p value < 0.05 was considered statistically significant. Thirty-seven patients (32.4% male and 67.6% female) aged 22.8 ± 3.6 years participated in this study. VAS results revealed that PBM controlled pain at 24 h (4 ± 3) in comparison with the control group (6 ± 3) (p = 0.002), at 48 h (4 ± 3 and 5 ± 2, respectively) (p = 0.003), and at 72 h (2 ± 2 and 4 ± 2, respectively) (p = 0.004). Wound healing was significantly better on the PBM-treated side on day 3 (2,1 ± 0,2) than on the control side (3,2 ± 0,6) (p = 0.00), at 7 days (1,5 ± 0,5 and 2,5 ± 0,8, respectively) (p = 0.00), and at 14 days (1 ± 0 and 1,8 ± 0,8), respectively (p = 0.00). No adverse effects were reported. PBM with a 980 nm diode laser significantly reduced postoperative pain and enhanced wound healing compared to the contralateral control side, supporting its use as a safe and effective adjunct in third molar surgery.

PMID:41555111 | DOI:10.1007/s44445-025-00098-9

Int Urol Nephrol. 2026 Jan 19. doi: 10.1007/s11255-026-05001-x. Online ahead of print.

ABSTRACT

BACKGROUND: Globally, Chronic kidney disease (CKD) has emerged as one of the major public health concerns impacting 10 to 15% of the world population. However, relatively very few studies have presented the comprehensive data on burden of disease particularly in developing countries like Nepal. Chronic kidney disease leads to declining kidney function, often progressing to end stage renal disease or death, imposing significant economic burdens. We aim to describe the ethology of end stage renal disease patients undergoing maintenance haemodialysis.

METHODOLOGY: A cross-sectional, descriptive study was conducted at a dedicated transplant centre in Nepal to assess a comprehensive understanding of the causes of chronic kidney disease. Data was collected from the individual’s undergoing haemodialysis at the Shahid Dharma Bhakta National Transplant Centre, approximately 175 individuals meeting the inclusion criteria. SPSS was used for descriptive and inferential statistics.

RESULTS: Among 175 patients, the mean age was 47.2 years (SD ± 14.97), with 115 (65.7%) males, and the majority were aged between 40 and 64 years. Hypertensive nephropathy was the most common cause, accounting for 39.4%, followed by both hypertension and diabetes mellitus in 12.6% of cases, while isolated diabetes mellitus accounted for 10.9%. Chronic glomerulonephritis (9.1%), chronic pyelonephritis (8%), and obstructive uropathy (7.4%) were also major contributors, with smaller proportions linked to conditions like urate nephropathy, renal tuberculosis, systemic lupus erythematosus, preeclampsia, and solitary kidney.

CONCLUSION: This study identified the primary causes of end stage renal disease in Nepal, where chronic kidney disease prevalence is increasing but under-researched. Hypertension and diabetes mellitus are key risk factors for chronic kidney disease, yet awareness of these conditions and their complications is often limited in Nepal. The etiological spectrum data is crucial for targeted screening programs for the general population and high-risk groups, follow-up and treatment, improving patient outcomes and reducing the economic burden of end stage renal disease.

PMID:41555102 | DOI:10.1007/s11255-026-05001-x

Eur Arch Paediatr Dent. 2026 Jan 19. doi: 10.1007/s40368-025-01156-w. Online ahead of print.

ABSTRACT

PURPOSE: To examine the dose-response of various fruit extracts on the growth of S. mutans as a primary cariogenic bacterium in the oral microbiota and to determine the minimal effective concentration that inhibits its growth.

METHODS: Fruit extracts of dried cranberries, cherries and raisins, as well as fresh pomegranate peel extract and pomegranate juice, were prepared and serially diluted with sterile deionized water to known concentrations of 1, 5, and 20 μg/μl. The effect of each fruit extract on the growth inhibition of S. mutans over 6 h was determined. Sterile deionized water served as a negative control and chlorhexidine (0.2%) served as a positive control.

RESULTS: Extracts of raisins, cranberries, and cherries, as well as pomegranate peel extract and juice, inhibited S. mutans growth in a dose-dependent manner. Significant inhibition at 6 h was observed for raisin, cranberry and cherry extracts at 5 and 20 μg/μl, as well as for pomegranate peel extract and juice at 1 – 20 μg/μl (all p < 0.05 vs. negative control). Among the highest concentrations tested, pomegranate peel extract (20 μg/μl) demonstrated the strongest inhibition, statistically significantly surpassing raisin, cranberry and cherry extracts, and pomegranate juice. Notably, the inhibitory effect of pomegranate peel extract did not differ from chlorhexidine, indicating that at this concentration it is as effective as the positive control.

CONCLUSIONS: Among fruit extracts, pomegranate peel extract (20 μg/μl), was the most effective inhibitor of S. mutans in vitro, matching the activity of chlorhexidine (0.2%), and could be further developed as a natural alternative for oral health applications.

PMID:41555095 | DOI:10.1007/s40368-025-01156-w

Eur Spine J. 2026 Jan 20. doi: 10.1007/s00586-025-09728-6. Online ahead of print.

ABSTRACT

PURPOSE: Mental health disorders have been shown to influence surgical outcomes, yet their effects on cervical spine surgery remain incompletely defined. This study evaluated the impact of depression and psychotic disorders on (1) perioperative complications, (2) discharge disposition, and (3) hospital resource utilization following anterior cervical discectomy and fusion (ACDF).

METHODS: The National Inpatient Sample (NIS) was queried for adult ACDF admissions from 2016 to 2022. Patients were classified into those with either no mental illness, depression, or psychotic disorder. Medical/surgical complications, dysphagia, and overall adverse events were extracted using ICD-10 diagnosis codes. Discharge status, inpatient mortality, costs, and length of stay were also evaluated across all cohorts. Subsequent analyses were adjusted for demographics, Elixhauser comorbidity index, hospital characteristics, and levels fused. Fusion level was included as a categorical covariate (single-level vs. multilevel) in all adjusted regression models. Multivariable logistic regression estimated adjusted odds ratios for complications/discharge outcomes, while weighted linear models compared/contrasted healthcare utilization with statistical significance set at P < 0.05.

RESULTS: 376,130 inpatient ACDFs were identified (64,020 with depression, 11,255 with psychotic disorders). Depression was associated with increased cardiovascular complications (OR 1.28, 95% CI 1.16-1.41), dysphagia (OR 1.09, 95% CI 1.02-1.18), and non-routine discharge (OR 1.15, 95% CI 1.09-1.21). Psychotic disorders were associated with increased rates of cardiovascular events (OR 1.29, 95% CI 1.04-1.59), mechanical complications (OR 1.36, 95% CI 1.01-1.83), and non-routine discharge (OR 1.47, 95% CI 1.32-1.64). Both mean costs and length of stay were likewise higher in patients with mental disorders (P < 0.001).

CONCLUSION: Mental health disorders are associated with increased postoperative complications, non-routine discharge, and healthcare utilization following ACDF. Appropriate diagnosis, management, and preoperative optimization may improve outcomes in this patient population.

PMID:41555094 | DOI:10.1007/s00586-025-09728-6

Soc Psychiatry Psychiatr Epidemiol. 2026 Jan 19. doi: 10.1007/s00127-025-03044-6. Online ahead of print.

ABSTRACT

PURPOSE: Physical illness describes long-term physical health conditions such as asthma, diabetes, and epilepsy. Mental or neurodevelopmental disorder (MND) that co-occurs with physical illness in childhood is associated with poorer outcomes for children and their families. There is a need for contemporary estimates of physical-MND burden to inform resource allocation and reduce occurrence. This descriptive study estimated the prevalence of morbidity status and compared prevalence of MNDs among children with or without physical illness.

METHODS: Data come from the 2019 Canadian Health Survey on Children and Youth, a representative cross-sectional study conducted by Statistics Canada. Physical illnesses and MNDs were reported by the person most knowledgeable about the child.

RESULTS: The sample included children aged 5 to 17 years (n = 33,715). In total, 49.5% of children had at least one physical illness and 17.9% had at least one MND. Physical-MND multimorbidity was reported for 9.8% of children. Among children with any physical illness, MNDs were present in 19.9%. Among children with no physical illness, the prevalence of MNDs was 14.1%. Differences in prevalence of MNDs across types of physical illnesses were small in magnitude (h=-0.02 to 0.35).

CONCLUSION: Findings show that childhood physical-MND multimorbidity is common, highlighting the need for screening of MNDs among Canadian children with physical illness. Integrated care models are necessary to comprehensively address the physical and MND health needs of children. These estimates of morbidity snapshot the time immediately prior to the COVID-19 pandemic and have critical utility as baselines for future post-COVID-19 studies.

PMID:41555082 | DOI:10.1007/s00127-025-03044-6

Soc Psychiatry Psychiatr Epidemiol. 2026 Jan 19. doi: 10.1007/s00127-025-03036-6. Online ahead of print.

ABSTRACT

PURPOSE: To estimate how social disparities in child psychiatric symptoms might change following hypothetical interventions targeting sports, outdoor play, and screen time at age 10.

METHODS: We used data from 9,778 children of the Generation R Study, a prospective population-based cohort in Rotterdam, the Netherlands. Social inequality variables included sex, maternal education, and migration background. Primary caregivers filled out the validated Child Behavior Checklist to report on children’s internalizing and externalizing symptoms at the age of 13. The hypothetical interventions (i.e., outdoor play, sports participation, and screen time) were parent-reported at age 10. We used sequential G-estimation to estimate the inequality with and without the hypothetical intervention.

RESULTS: Children with migration backgrounds (46.3%) and low maternal education (53.3%) were associated with relatively more internalizing and externalizing symptoms than peers, with disparities of 0.125 and 0.177 standard deviations, respectively. Girls had more internalizing symptoms (0.106 SD), while boys had more externalizing symptoms (0.154 SD). Increasing sports participation reduced disparities in internalizing symptoms linked to maternal education (β = -0.014; 95% CI: -0.024, -0.003), while outdoor play and screen time interventions showed limited effects. None of the hypothetical interventions led to a statistically significant reduction in social disparities in externalizing symptoms.

CONCLUSIONS: This study underscores the persistence of sex, cultural, and socioeconomic disparities in youth mental health. While sports participation showed a potential effect in reducing disparities in internalizing symptoms, its impact on externalizing symptoms and other interventions was negligible. Future efforts should focus on identifying more effective strategies for addressing these disparities.

PMID:41555079 | DOI:10.1007/s00127-025-03036-6

Environ Monit Assess. 2026 Jan 19;198(2):147. doi: 10.1007/s10661-026-14981-3.

ABSTRACT

Assessing changes in mean sea level (MSL) has become increasingly critical due to the significance of climate changes. Soft computing techniques are now widely used to reduce the time and cost associated with traditional MSL estimation methods. Historical MSL data is frequently used to predict future values, yet the application of soft computing models to analyze climate change’s impact on MSL remains relatively unexplored. This study aims to develop and compare various soft computing techniques for modeling MSL fluctuations using meteorological data. Random forest (RF), support vector regression (SVR), K-nearest neighbors (KNN) regression, deep neural network (DNN), Gaussian process regression (GPR), and stacked ensemble methods are employed in this study. The newly developed models are statistically assessed for their effectiveness in modeling MSL at Damietta station, Egypt. Variables environmental data such as surface water temperature, pressure, air temperature (average air temperature, dewpoint, wet-bulb, and heat index), and humidity and wind attributes (speed and direction) are utilized and evaluated in modeling MSL. The results indicate that RF, KNN, and GP outperformed other proposed models in modeling MSL during both training and testing phases. The developed weighted stacked ensemble model, integrating RF, KNN, and GPR, outperformed the base models with a correlation coefficient (R) of 0.88 and normalized root mean square error (RMSE) of 0.056 m. MSL modeling at the study station was particularly sensitive to variations in water temperature, wind speed and direction, and atmospheric pressure. This methodology serves as a valuable framework for climate-driven MSL forecasting in developing coastal regions lacking long-term tide records, directly contributing to UNESCO’s Ocean Decade Challenge 5 on coastal resilience.

PMID:41555074 | DOI:10.1007/s10661-026-14981-3

Nat Protoc. 2026 Jan 19. doi: 10.1038/s41596-025-01300-2. Online ahead of print.

ABSTRACT

Neural activity data can be associated with behavioral and physiological variables by analyzing their changes in the temporal domain. However, such relationships are often difficult to quantify and test, requiring advanced computational modeling approaches. Here, we provide a protocol for the statistical analysis of brain dynamics and for testing their associations with behavioral, physiological and other non-imaging variables. The protocol is based on an open-source Python package built on a generalization of the hidden Markov model (HMM)-the Gaussian-linear HMM-and supports multiple experimental modalities, including task-based and resting-state studies, often used to explore a wide range of questions in neuroscience and mental health. Our toolbox is available as both a Python library and a graphical interface, so it can be used by researchers with or without programming experience. Statistical inference is performed by using permutation-based methods and structured Monte Carlo resampling, and the framework can easily handle confounding variables, multiple testing corrections and hierarchical relationships within the data, among other features. The package includes tools developed to facilitate the intuitive visualization of statistical results, along with comprehensive documentation and step-by-step tutorials for data interpretation. Overall, the protocol covers the full workflow for the statistical analysis of functional neural data and their temporal dynamics.

PMID:41555071 | DOI:10.1038/s41596-025-01300-2