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Dietitian-led intervention to manage constipation in Parkinson’s disease: Study protocol for a parallel-group randomized controlled trial (NUTRI-GUT-PD)
Background Constipation is a frequent non-motor symptom in Parkinson’s Disease (PD). Standard care includes dietary and lifestyle guidance or the use of laxatives. Diet represents a promising non-pharmacological approach, but its management in PD is complex and goes beyond simple dietary recommendations. This study protocol aims to examine whether nutritional counseling delivered by a dietitian is more effective than usual care in reducing constipation symptoms in PD patients. Methods This 90-day randomized controlled superiority trial uses a parallel-group design. A total of 54 outpatients with PD without dementia and fulfilling the Rome IV criteria for functional constipation will be included. Baseline assessments comprise demographics, nutritional evaluation (anthropometric measurements and sarcopenia indicators), neurological assessment, and the Constipation Scoring System (CSS) scores. Participants will perform stool sample collection for microbiota analysis and will be requested to complete three 24-hour dietary recalls over the subsequent week. After baseline, participants will be randomly allocated to either the control group (usual care) or the intervention group (nutritional counseling delivered by a dietitian). At the first study visit, participants will deliver the stool sample and dietary recalls. The intervention will last for 90 days and will include one in person consultations and bi-weekly follow-up phone calls. Nutritional counseling will address four main topics: healthy eating, food processing, fiber and fluid intake, and levodopa interactions with diet. In addition, participants in the intervention group will receive an individualized diet plan. All baseline assessments will be repeated at the 90-day endpoint. The primary outcome is the change in weekly bowel movement frequency from baseline to day 90, measured through the stool diary. Secondary outcomes include CSS, changes in fecal gut microbiota, macronutrient distribution, diet quality, body composition and sarcopenia indicators. Trial registration number: NCT07213856.
A systems biology approach to find representative genes in Acute Myeloid Leukemia
In this study, we modeled gene expression profile data from Acute Myeloid Leukemia (AML) and healthy cases. At first, the GEO-GSE9476 dataset was processed, and a total of 341 genes were identified as differentially expressed genes (DEGs) in patients, and 599 DEGs in healthy individuals. Gene Ontology and pathway analysis on DEGs led to the identification of 5 Transcription Factors for patients and 3 for healthy cases. Analysis of the respective metabolic pathways revealed a common region in the metabolic pathway between AML and Tuberculosis (TB) that confirmed the validity of our procedure due to the consistency with similar reports. Upon PPI network analysis, Hub genes and three modules containing 41 up-regulated and down-regulated genes in AML patients were identified. Survival analysis on these genes results in reducing the number of identified effective genes into 3 upregulated ( ITGAM , ITGAL and CD163 ) and 5 downregulated genes ( MCM2 , MCM3 , RFC4 , RFC5 and FEN1 ). Finally, drug sensitivity analysis was performed on these genes demonstrating complexity in drug-resistance due to the pattern of gene expression. This knowledge could potentially enable personalized treatment approaches based on individual patient responses due to the epigenetics and life style which affect gene expression pattern.
Choroidal metastasis: Impact of primary tumors and age on survival - a single center analysis
Purpose The purpose of this study was to determine whether patient’s age and their primary tumor type act as independent predictors of their survival after a diagnosis of choroidal metastasis. Methods This retrospective single-center study (August 2013 – August 2025) included 70 patients with choroidal metastases. Clinical data and multimodal imaging were extracted from medical records. Tumor volume was calculated from ultrasound measurements. Patients were grouped according to primary tumor origin (lung, breast, or other primaries). Survival was compared between these three primary tumor groups and according to age. Group differences in continuous variables were assessed using ANOVA/Kruskal-Wallis testing, and survival distributions were analyzed with Kaplan-Meier curves. Patients were categorized into younger (<59.6 years) and older (≥59.6 years) groups for Kaplan-Meier survival analysis. Univariate and multivariate Cox regression models were performed to identify independent predictors of overall survival. Results Median overall survival was 71.6 weeks. Statistically significantly longer survival was found in patients <59.6 years (median 88.3 weeks; estimated 3-year survival 33.2%) compared with those ≥59.6 years (median 41.9 weeks; 3-year survival 16.8%; p = 0.037. Primary tumor group analysis showed a median survival of 94.4 weeks for breast cancer (estimated 3-year survival 38.3%), 52.7 weeks for lung cancer (9,5%), and 40.6 weeks for other primaries (19.7%). However, this difference was not statistically significant (p = 0.269). In multivariate Cox regression analysis, age ≥ 59.6 years (HR 2.10; p = 0.016) and >1 extraocular metastases elsewhere in the body (HR 2.53; p = 0.029) were independent predictors of mortality. Conclusion Survival in choroidal metastases is strongly driven by age and the number of extraocular metastatic sites at presentation. Our findings suggest that younger age and a lower metastatic burden are most reliable indicators for a more favorable prognosis.
The economic burden of Type 2 Diabetes by social determinants of health: A systematic review
Background The unequal distribution of resources in society generates social gradients that translate into health inequalities and differential use of health care resources and their costs. Non-medical factors such as employment, income, ethnicity and education impact the prevalence and treatment outcomes of patients with type 2 diabetes mellitus (T2DM); however, there is a scarcity of articles assessing the relationship between health inequalities and the economic costs of treatment. Therefore, we conducted a systematic review of published studies examining the cost differences of treating T2DM across social determinants of health (SDH). Methods We systematically searched MEDLINE, Embase, PsycINFO, EconLit, and NHS EED for original peer-reviewed articles that provided cost differences of treating T2DM by SDH: education, income, employment, residency and ethnicity. We grouped the studies by each SDH and calculated the percentage differences where possible between the lowest and highest ends of the gradient (education, income and employment). Residency was categorised as rural vs. urban and ethnicity as white or general population vs other ethnic minorities. Results We included 19 articles retrieved internationally from varying healthcare systems. Results were contextualised given the healthcare financing model. In countries with high out-of-pocket expenses, Black and Hispanic ethnic backgrounds and rural residence were associated with lower direct health care and costs likely to be determined by ability to pay rather than clinical need. Indirect costs such as lost productivity due to absenteeism were also lower in unemployed, and lower income groups. Conclusions There are evident health disparities in the direct and indirect economic consequences of T2DM. The effect of decreased healthcare use and costs on treatment outcomes needs to be further explored to inform policies to ensure healthcare delivery is based on clinical need rather than socio-economic factors.
Prevalence, risk factors, and management practices of premenstrual syndrome among female university students in Lebanon: An observational cross-sectional study
Background Premenstrual syndrome (PMS) is common among women of reproductive age and may impair quality of life, academic performance, and social functioning. In Lebanon, data on PMS and its management among female university students remain limited. This study aimed to estimate the prevalence of PMS among female university students in Lebanon, identify risk factors, and recognize adopted management practices and their perceived effectiveness. Methods An observational cross-sectional study was conducted among female students at Beirut Arab University, Lebanon, between April 20 and May 8, 2026. Data were collected using a self-administered questionnaire assessing sociodemographic, menstrual, lifestyle, behavioral, psychosocial, and management-related factors. Premenstrual symptoms, social media addiction, and perceived stress were assessed using the Premenstrual Symptoms Screening Tool, Bergen Social Media Addiction Scale, and Perceived Stress Scale-4, respectively. Binary logistic regression was used to identify factors associated with moderate-to-severe PMS and premenstrual dysphoric disorder (PMDD). Results Among 1,062 participants, 497 participants screened positive for moderate-to-severe PMS (46.8%), and 175 had symptoms consistent with PMDD based on the PSST (16.5%). The most frequently reported moderate-to-severe symptoms were physical symptoms (69.7%), fatigue or lack of energy (68.8%), depressed mood or hopelessness (67.8%), and overeating or food cravings (66.2%). Academic absenteeism was reported by 37.2%. More than one-third of the participants with moderate-to-severe PMS and PMDD (39%) reported using pharmacological management practices, mainly non-steroidal anti-inflammatory drugs. However, less than half of medication users perceived these treatments as being very effective (45.8%). Higher odds of moderate-to-severe PMS were observed among current smokers (AOR = 1.61, P = 0.003), those with heavy menstrual bleeding (AOR = 1.58, P = 0.03), high meal skipping (AOR = 1.38, P = 0.03), high fast-food consumption (AOR = 1.34, P = 0.04), higher social media addiction scores (AOR = 1.31, P < 0.001), and higher perceived stress scores (AOR = 1.48, P < 0.001). Conclusion PMS was common among female university students in Lebanon and a considerable proportion experienced PMDD. In fact, lifestyle, behavioral, and psychosocial factors were associated with moderate-to-severe symptoms, highlighting the need for university-based awareness, screening, and counseling strategies that address modifiable risk factors and support appropriate management of premenstrual symptoms.
Vehicle routing optimization and algorithms for instant delivery under customer loss mechanism
In the field of instant delivery, the mismatch between delivery resources and customer demands has led to increasingly significant customer losses. To address this issue, this study introduces the customer loss mechanism and constructs an evaluation function to screen out resource-intensive customers, thereby clarifying the scope of delivery services. Based on this, this study establishes the vehicle routing optimization model under the customer loss mechanism with the objective of minimizing the sum of vehicle fixed costs, variable routing costs, and time window penalty costs. An improved genetic algorithm is employed to solve this model. Case study results demonstrate that the improved genetic algorithm outperforms traditional genetic algorithms and tabu search algorithms in convergence speed, optimization capabilities, and stability, reducing total delivery cost by 36.25% and 4.18%, respectively, with zero delivery violations. Regarding model performance, when proactively excluding 8.33% of customers, the total delivery cost is reduced by 17.18%, primarily driven by the reduction in fleet size. Furthermore, large-scale experiments reveal a pronounced leverage effect: excluding a mere 5% of marginal customers counter-intuitively reduces both fleet size and travel distance, while a 10% loss yields an 18.39% total delivery cost reduction with zero violations, proving that the mechanism precisely screens out inefficient nodes rather than arbitrarily rejecting them. Sensitivity analysis further confirms the model’s robustness across varying resource tightness, demonstrating that proactive customer loss is a feasible and effective strategy for improving resource utilization through precise resource focusing.
Exploring the mechanism of Shuangyu Granule in regulating immune-inflammatory responses in influenza through UPLC-Orbitrap-MS/MS, GC-MS, and network target analysis
Influenza, an acute respiratory infectious disease caused by the influenza virus, remains a significant challenge for prevention and treatment due to rapid viral mutation and high pathogenicity. Traditional Chinese Medicine (TCM), including Shuangyu Granule (SYKL), has demonstrated efficacy in managing influenza. This study aimed to systematically identify the chemical components of SYKL in vitro and its absorbed constituents in vivo, and to preliminarily explore its potential mechanism in regulating influenza-related immune inflammation. UPLC-Orbitrap-MS/MS and GC-MS were used to characterize SYKL’s chemical profile, identifying 148 in vitro components and 21 prototype absorbed blood components. Network target analysis, integrated with single-cell RNA sequencing (scRNA-seq) data from influenza patients, predicted that the absorbed components may target multiple immune-inflammatory regulatory genes across various immune cell types. Molecular docking suggested favorable predicted binding potential between these components and target proteins. Experimental validation using poly(I:C)-induced inflammatory models in both RAW264.7 macrophages and mouse bone marrow-derived macrophages (BMDMs) showed that the absorbed components—loganic acid, 8-epiloganic acid, calycosin, atractylodin, eucalyptol, secoxyloganin, and paeoniflorin—significantly reduced mRNA expression of immune-inflammatory genes (DUSP6, MAPKAPK2, NOD2) and inhibited secretion of TNF-α, IL-6, IL-8, and NO. These findings suggest that SYKL may alleviate influenza-associated inflammation through multi-component, multi-cell, and multi-target pathways, highlighting its potential in modulating excessive immune responses in influenza.
A standardized imaging and analysis workflow for quantitative evaluation of cutaneous neurofibromas in Nf1-KO mice
Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder in which cutaneous neurofibromas (cNFs) represent one of the most common and burdensome manifestations. No approved pharmacological treatment exists. Preclinical studies are essential to evaluate candidate therapies, but reliable outcome and endpoint measures for cNFs in animal models remain limited. We developed and validated a standardized methodology to assess drug efficacy in the Prss56Cre Nf1-KO mouse model which recapitulates key features of cNFs. In this model, Nf1 inactivation and tdTomato (Tom) reporter expression were specifically targeted to Schwann cells (SCs) responsible for cNF development. This approach enables real-time monitoring, isolation, and manipulation of tumor SCs at any time. We defined macroscopic (tumor count, total Tom + fluorescent surface area, fluorescence intensity) and microscopic (cell-type composition defined by immunolabeling with a panel of specific markers, area quantification) endpoints, developed dedicated ImageJ scripts for automated image analysis, and compared the results with those obtained using the conventional manual method. Both automated measurements showed excellent reproducibility (ICC = 1) and strong correlation with manual analysis (Spearman’s coefficient > 0.90), while significantly reducing analysis time (up to 100-fold faster). Bland–Altman analyses confirmed the absence of systematic bias compared with manual scoring. The standardized image naming and metadata integration further facilitated data consolidation and statistical analysis. This validated approach provides a reliable, reproducible, and time-efficient framework for evaluating drug effects on cNFs in preclinical studies. It establishes a foundation for robust efficacy testing of candidate therapies, facilitates cross-study comparability, and accelerates therapeutic development and clinical translation.
An automated approach to extracting head and brain circumference from MRI datasets
Head circumference is a fundamental biometric parameter for brain growth in both the clinical pediatric setting and in developmental neuroscience. However, the gold standard for obtaining head circumference by manual tape measurement is notoriously error-prone. Further, while it is known that the growth trajectories of head and brain differ over time, a systematic comparison of these two parameters as a function of age does not yet exist. We developed a new and automated algorithm for obtaining head and brain circumference from MRI data. The algorithm mimics manual head circumference measurement by placing a convex hull around axial slices which must intersect with predefined anatomical landmarks. Several differently-tilted iterations are run and results are combined. In addition to obtaining head circumference, the approach can also be applied to gray matter only, providing “brain circumference” (gray matter hull perimeter) at the same level as head circumference. To assess validity, we used T1-weighted 3D datasets (n = 153) with available, manually measured head circumference values (age range 0–226 months [0–18.8 years]). To assess test-retest reliability, a second dataset (n = 3 with 40 scans each) was used. When compared with the current gold standard (manual measure), high validity was demonstrated for the new approach, with no systematic bias. The algorithm also showed a very high reliability across multiple measurements. Developmental trajectories of both head and brain circumference were generated and compared. In summary, the algorithm represents a valid and reliable method for the automated determination of head as well as brain circumference. It offers an objective way to assess these parameters in retrospect and prospectively, and may shed light on specific clinical situations where they differ, such as in the presence of enlarged subarachnoid spaces.
Intrathecal pump refills at home or at the hospital: Protocol for a randomized controlled crossover trial—The IMPROVE study
Background Intrathecal drug delivery (IDD) offers a therapeutic option for patients suffering from refractory pain or severe spasticity. By allowing targeted and continuous infusion directly into the intrathecal space, IDD bypasses the blood-brain barrier and enhances therapeutic effectiveness of the drug. Following the implantation of an IDD pump, the most commonly performed postoperative maintenance procedure is the pump refill (at regular intervals). This process can be burdensome for patients, affects their comfort, and carries significant risks. The current aim of this study is to evaluate whether intrathecal pump refills performed at home provide a difference in patient comfort compared to refills conducted in the hospital. Methods The IMPROVE study is a monocentric, randomized controlled crossover trial, including 82 patients. For this study, each patient will undergo four intrathecal pump refill procedures (two at home and two in the outpatient clinic) allocated in a randomized order. The primary objective of this study is to determine whether at-home refills provide a difference in patient comfort compared to hospital-based refills. Secondary objectives include assessing differences in quality of life, pain, stress, anxiety, self-efficacy, caregiver burden, patient preferences, safety, and overall cost-effectiveness between the two settings. Patients will be followed over the course of four intrathecal pump refills, which is estimated to span approximately one year. Discussion Within the IMPROVE project, pump refills will be performed through hospital at home. If at-home intrathecal pump refills prove more comfortable for patients and cost-effective for society, this would strengthen the patient-centred care model and support adopting this approach as the new standard treatment for IDD patients. A graphical abstract is provided in the supplementary materials (S1 Fig). Trial registration Details on the study site can be found at ISRCTN with identifier: ISRCTN18031921; [href: https://doi.org/10.1186/ISRCTN18031921 ] https://doi.org/10.1186/ISRCTN18031921 . The trial was registered in the ISRCTN registry on 18 November 2025.
Editorial Note: A risk-averse sustainable perishable food supply chain considering production and delivery times with real-world application
Fetal sex programs immune architecture and cellular differentiation at the maternal–fetal interface in early human pregnancy
Enhancing missense variant classification in predicted intrinsically disordered regions
Classifying disease-causing missense variants in intrinsically disordered regions (IDRs) remains a significant challenge, with over 25% of known deleterious variants occurring in these regions. Existing in silico missense variant predictors that predict variant classification generally perform better in ordered regions of the protein, limiting their effectiveness. To address this, we developed a machine learning methodology that integrates global IDR conformation (gIDRc) features from ALBATROSS, phase separation (PS) features from BioPython, and 1024-dimensional protein embeddings from ProtTransBertBFD generated for both wild-type (WT) and mutant IDR sequences. IDR boundaries were defined using the AlphaFold-RSA predictions, which identifies disordered regions based on AlphaFold2 pLDDT scores and relative solvent accessibility. Using ClinVar variant classifications as ground truth, AlphaMissense, EVE, and ESM1b were the highest scoring unsupervised in silico missense predictors for IDR variants. Our baseline model, using only IDR-specific features achieved competitive performance on the hold-out test set with a PR-AUC of 0.817. Critically, when these IDR features were combined with these methods we saw significant overall improvement. The AlphaMissense-Enhanced model increased its PR-AUC from 0.807 to 0.919. Similarly, ESM1b-Enhanced improved PR-AUC from 0.679 to 0.845 and EVE increased from 0.591 to 0.910. These results demonstrate the effectiveness of our enhancements for classifying missense variants in IDRs and highlight its ability to complement existing in silico missense predictors.
Effectiveness of liquorice gel as an adjunct to non-surgical periodontal therapy in chronic periodontitis: a randomized controlled, clinical and microbiological trial
Identifying cluster profiles based on barriers and facilitators to physical activity during COVID-19 confinement: A cross-sectional study using machine learning analysis
Social restrictions, such as confinement periods, tend to reduce physical activity (PA) levels. However, sociodemographic factors may influence specific barriers and facilitators to PA during such periods. This study aimed to identify cluster profiles of individuals based on barriers and facilitators to physical activity (PA) during COVID-19 confinement. Brazilian adults participated in a cross-sectional online survey. The questionnaire collected demographic data, PA levels, sedentary behavior (SB), and perceived barriers and facilitators for PA. During data preprocessing, correlated barriers and facilitators related to a similar topic were aggregated. Using machine learning analysis, the K-modes evaluated by the Silhouette Score were used for barriers and the ROCK evaluated by the Silhouette Score was used for facilitators. The barriers model produced well-defined profiles, whereas the facilitators model did not. The facilitator model generated clusters with multiple negative silhouette coefficients and exhibited a significantly less cohesive cluster structure. Therefore, only the barriers-based model was used for further analysis. The best model generated eight clusters, each named according to the most frequent barriers in the group, such as “Inactive depressive women”, “Active depressive women” and “Super active”. The depressive clusters presented more barriers to PA, three barriers each one. Significant differences in PA and SB were observed across clusters. This work highlights the novelty of using unsupervised machine learning to uncover latent subgroups based on multiple concurrent barriers. In conclusion, tailored home-based and outdoor strategies should be developed, particularly targeting individuals with depressive symptoms and those facing significant time constraints.
Experimental study on crack initiation mechanism of loess containing non-penetrating fissures
Age-stratified prognostic performance of hematologic inflammatory indices for 30-day mortality in emergency department patients with PCR-confirmed COVID-19: A cohort study from the pre-vaccination pandemic era
Background During the pandemic era, rapid and accessible prognostic tools were essential to support clinical decision-making for emergency department (ED) patients presenting with acute infectious symptoms. Hematologic inflammatory indices derived from complete blood count (CBC) parameters, such as the systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and pan-immune-inflammation value (PIV), have been increasingly investigated for risk stratification. This retrospective cohort study evaluated the age-stratified prognostic performance of these indices for 30-day mortality in ED patients during the pandemic period. Methods This retrospective cohort study included adults presenting to a tertiary-care ED between March 1 and May 31, 2020. All included patients were retrospectively confirmed to have SARS-CoV-2 infection by RT-PCR. CBC-derived inflammatory markers (SII, SIRI, and PIV) were calculated at admission. The primary outcome was 30-day mortality; the secondary outcome was ICU admission. Age-stratified analyses (<65 and ≥65 years) were performed. Receiver operating characteristic (ROC) analyses, area under the curve (AUC) values, optimal cut-offs, and negative predictive values (NPVs) were determined; logistic regression models assessed independent associations with mortality. Results A total of 2,778 PCR-confirmed patients were included (mean age 47.8 ± 16.2; 58.7% male). Thirty-day mortality was 6.2%. In the overall cohort, SII, SIRI, and PIV demonstrated modest prognostic performance for mortality (AUCs: 0.663, 0.659, and 0.649, respectively). In patients <65 years, performance improved particularly for SII (AUC 0.727), with SIRI and PIV yielding AUCs of 0.676 and 0.677, respectively. Among patients ≥65 years, discrimination was lower (SII: 0.570; SIRI: 0.604; PIV: 0.588). Formal DeLong testing confirmed statistically significant age-related attenuation for SII (ΔAUC = 0.159; P = 0.0055), with non-significant trends for SIRI and PIV. As an exploratory secondary outcome, direct ED-to-ICU admission occurred in 2.9% of patients; this endpoint primarily reflects the institutional pandemic-era pathway of low-threshold ward admission with subsequent ICU escalation upon clinical deterioration. All indices demonstrated high negative predictive values, particularly in younger patients, indicating potential utility for identifying lower-risk individuals during high-volume pandemic ED operations. Conclusions Hematologic inflammatory indices obtained at ED presentation demonstrated age-dependent prognostic performance for 30-day mortality, with SII showing good discrimination and high negative predictive value (98.9%) in patients younger than 65 years and reduced discriminatory performance in elderly patients. These readily available and cost-effective parameters may support rule-out decisions for younger adults in emergency settings, while in elderly patients clinical assessment and comorbidity profiling should be prioritized over inflammatory marker interpretation.
Integrated value iteration and simple games enable calibrated strategic coalition formation in social networks
Understanding support needs of African and African-Caribbean people living with dementia, their care partners and families, and impacts of delayed support: Identifying inclusive strategies to facilitate social care support: A study protocol
African and African-Caribbean (AAC) people living with dementia (PLWD) are a population at high risk of inequitable access to health and social care services, and have poor health and wellbeing outcomes. Research suggests they are likely to experience failings in care, be recognised by services late and at points of crisis, and are at increased risk of institutionalisation. This research aims to examine the experiences of AAC PLWD, their care partners and families of accessing social care services and support. Employing an intersectionality theoretical framework, this study will use a multi-method, flexible exploratory sequential design. An evidence synthesis using the JBI meta-aggregation approach, co-produced with AAC PLWD, their care partners, and families, will be conducted to synthesise existing evidence on the experiences of AAC PLWD, their care partners’ and families’ of support seeking in the UK. Narrative interviews will involve 3 sequential interviews conducted with 40 AAC PLWD (n = 10 per site), with opportunities for dyadic interviews with care partners and families. Ethnographic fieldwork will be conducted across 4 Local Authority sites within Adult Social Care teams (n = 30 days per site) to provide insight into institutional and organisational processes and cultures, and staff practices and engagement with PLWD. Artistic art workshops will be conducted to facilitate a diverse range of participant voices and support meaningful engagement for AAC PLWD at increased risk of isolation. Ethical approval has been obtained. Dissemination will include peer reviewed publications, conference presentations and free publicly available resources for health and social care professionals, third-sector partners, PLWD and their carer partners.