Tag: pubmed

J Craniofac Surg. 2025 Apr 18. doi: 10.1097/SCS.0000000000011374. Online ahead of print.

ABSTRACT

OBJECTIVE: To compare the dentofacial morphology of patients with complete bilateral cleft lip, alveolus, and palate (CBCLAP) treated at 2 European centers participating in the ERN CRANIO Network, employing different protocols for alveolar cleft closure. The Milano Cleft Center (Center M) utilizes early secondary gingivo-periosteo-plasty (esGPP) at the time of hard palate closure, while the Nijmegen Cleft Center (Center N) employs early secondary alveolar bone grafting with or without premaxillary osteotomy at 9 to 11 years of age.

MATERIALS AND METHODS: This retrospective long-term outcome study included 95 patients (44 from Center M; 51 from Center N), treated consecutively from birth on in the same center, and evaluated at the end of growth (mean age 18 y). Lateral cephalograms were used to analyze skeletal and dentoalveolar variables. Measurements were conducted by an independent hospital to ensure objectivity. Statistical analyses included t tests and multiple linear regression to assess the effects of center, protocol, age, and sex.

RESULTS: The skeletal variable SN-ML (mandibular plane angle) indicated a more hyperdivergent pattern in Center N (mean 37.65 degrees, SD=6.62) compared with Center M (mean 34.55°, SD 7.52; P=0.037). Among dentoalveolar variables, only the upper incisor inclination (ILs-NL) was significantly larger in Center M (4.06 degrees, P=0.05). Regression analysis revealed minimal effects of center, protocol, age, or sex on skeletal and dentoalveolar variables.

CONCLUSION: Patients from both centers achieved acceptable maxillofacial outcomes independent of the type of alveolar cleft closure. Further research should consider patient-reported outcomes to align clinical evaluations with patient perceptions.

PMID:40249594 | DOI:10.1097/SCS.0000000000011374

Avian Dis. 2025 Apr;68(S1):391-397. doi: 10.1637/aviandiseases-D-24-00022.

ABSTRACT

Infectious coryza (IC) is a poultry respiratory disease caused by the bacterium Avibacterium paragallinarum. IC has major economic impacts in both the layer and broiler industries because this disease can cause increased mortality in affected flocks, an increase in condemnations at the processing plants, and decreased egg production in laying hens. In 2017, an increased number of IC-positive cases was seen in the Turlock Branch of the California Animal Health and Food Safety (CAHFS) Laboratory System. The objective of this study was to assess the epidemiologic patterns of IC before, during, and after this outbreak, as the number of positive cases remained elevated from 2017 to the end of 2022. Necropsy cases positive for IC in the CAHFS electronic database were analyzed from January 1, 2016, a year prior to the outbreak, to December 31, 2022. Out of 1935 submissions tested for IC during this period, the 674 cases found to be positive were then categorized by age, poultry type, season, and spatial distribution to investigate risk factors for IC. Raw data trends showed that most of the cases were from backyard poultry flocks (50.5%, 341/674), which appeared to be mainly localized to Los Angeles County and San Joaquin County. Commercial layers made up the second greatest number of cases (31.4%, 212/674) and were mostly localized to Merced County and Stanislaus County. Last, most of the commercial broiler cases (18.0%, 121/674) were in Merced County. Statistical analysis of this raw data was also performed and demonstrated that poultry in commercial egg layer operations located in the Central Valley and southern regions of California were at an increased risk for IC (odds ratio = 1.3, 95% confidence interval: 1.01-1.65, p = 0.039). Ultimately, this study provides updated information on IC trends in California and the risk factors associated with the outbreak.

PMID:40249577 | DOI:10.1637/aviandiseases-D-24-00022

Eur Arch Paediatr Dent. 2025 Apr 18. doi: 10.1007/s40368-025-01043-4. Online ahead of print.

ABSTRACT

PURPOSE: The aims of this study were to identify proteomic profiles of dental pulp from deciduous teeth and compare the profiles of the two dentitions.

METHODS: Teeth that were caries-free and had normal pulp conditions were collected from twelve healthy individuals. The obtained teeth consisted of deciduous teeth (n = 6) and permanent teeth (n = 6). Proteins were extracted from pulp tissue and then analysed using liquid chromatography-tandem mass spectrometry. MaxQuant was used to identify and quantify proteins from raw mass spectrometry data of the collected deciduous and previously analysed permanent dental pulp. Differentially expressed proteins (DEPs) between the dental pulp of the two dentitions were identified by a statistical analysis conducted using Metaboanalyst with criteria P-value < 0.05 and fold change > 2.

RESULTS: A total of 3,636 proteins were identified in the dental pulp of deciduous teeth. The biological process functional classifications of these proteins were primarily concerned with cellular process, biological regulation, metabolic process and response to stimulus. Dental pulp protein profiles differed significantly between deciduous and permanent teeth, with 736 proteins being differentially expressed, the majority of which were highly expressed in the pulp of deciduous teeth. Pathway analysis indicated DEPs to be involved in tumour necrosis factor (TNF) signalling, nuclear factor kappa B signalling, and odontoclast/osteoclast differentiation.

CONCLUSION: While the dental pulp of deciduous and permanent teeth shares some characteristics, there are also significant differences in protein expression, with the TNF signalling pathway and odontoclast/osteoclast differentiation being promoted in the dental pulp of deciduous teeth.

PMID:40249556 | DOI:10.1007/s40368-025-01043-4

Ann Med. 2025 Dec;57(1):2490830. doi: 10.1080/07853890.2025.2490830. Epub 2025 Apr 18.

ABSTRACT

BACKGROUND: Renal cell carcinoma (RCC) is a heterogeneous malignancy with diverse gene expression patterns, molecular landscapes, and differentiation characteristics of tumor cells. It is imperative to develop molecular RCC classification based on tumor cell differentiation for precise risk stratification and personalized therapy.

METHODS: We obtained scRNA-seq profiles from GSE159115 and bulk RNA-seq profiles from TCGA-KIRC cohort. We then performed scRNA-seq cluster analysis, monocle2 pseudotime analysis, and prognostic analysis to obtain tumor cell differentiation-related prognostic genes (TCDGs). Subsequently, we conducted consensus clustering to construct the RCC tumor cell differentiation-related prognostic classification (RCC-TCDC) and implemented prognostic and multi-omics analyses. Moreover, we utilized Lasso regression to help develop a multivariable prognostic model. In addition, we performed correlation analysis and Cmap algorithm for regulatory network establishment and candidate inhibitor prediction. We eventually included 370 kidney neoplasm patients in Xinhua cohort to undergo immunohistochemical staining and scoring for classification and comprehensive statistical analyses, including Chi-square tests, Kaplan-Meier survival analyses, and multivariable Cox regression analysis .

RESULTS: 32 TCDGs were identifiedand RCC-TCDC was constructed to classify TCGA-KIRC patients into RCC-low differentiation (RCC-LD) (S100A11+ SH3BGRL3+, high risk), RCC-moderate differentiation (TSPAN7+, medium risk), and RCC-high differentiation (RCC-HD) (AQP1+ NPR3+, low risk). Notably, RCC-LD was validated as anindependent risk factor for both OS (p = 0.015, HR = 14.0, 95%CI = 1.67-117.8) and PFS (p = 0.010, HR = 4.0, 95%CI = 1.39-11.7) of RCC patients in Xinhua cohort, taking RCC-HD as reference.

CONCLUSIONS: We constructed and validated a robust molecular classification system, RCC-TCDC, elucidating three distinct RCC subtypes.

PMID:40248945 | DOI:10.1080/07853890.2025.2490830

Curr Med Imaging. 2025 Apr 17. doi: 10.2174/0115734056343819250307040956. Online ahead of print.

ABSTRACT

AIM: The study investigates the creation and assessment of Machine Learning (ML) models using different classifiers such as SVM (Support Vector Machine), logistic regression, decision tree, k-nearest neighbour (kNN), and Artificial Neural Network (ANN) for the automated identification of tuberculosis (TB) from chest X-ray(CXR) images.

BACKGROUND: As a persistent worldwide health concern, TB requires early detection for effective treatment and control of the infection. The differential diagnosis of TB is a challenge, even for experienced radiologists. With the use of automated processing of CXR images which are reasonable and frequently used for TB diagnosis, employing Artificial Intelligence (AI) techniques provides novel possibilities.

OBJECTIVE: The objective of the study was to identify respiratory disorders, radiologists devote a lot of time reviewing each of the CXR images. As such, they can identify the type of disease using automated methods based on AI algorithms. This work advances the diagnosis of TB via machine learning, which may result in early treatment options and enhanced outcomes for patients.

METHOD: The disease was classified using distinct parameters like edge, shape, and Gray Level Difference Statistics (GLDS) on splitting of the dataset at 70:30 and 80:20.

RESULTS: It was observed that authors attained 93.5% accuracy using SVM with linear kernel for a 70:30 data split considering hybrid parameters. The comparison was made considering different feature extraction techniques, different dataset splitting, existing work, and another dataset.

CONCLUSION: The designed model using SVM, decision tree, kNN, ANN, and logistic regression was compared using other state-of-the-art techniques, other datasets, different feature extraction techniques, and different splitting of data. AI has great promise for enhancing tuberculosis detection, which will ultimately lead to an earlier diagnosis and improved disease management.

PMID:40248942 | DOI:10.2174/0115734056343819250307040956

Curr Med Imaging. 2025 Apr 17. doi: 10.2174/0115734056371884250324151755. Online ahead of print.

ABSTRACT

OBJECTIVE: This study evaluated the impact of Low-Intensity Pulsed Ultrasound (LIPUS) on bone radio density in patients undergoing orthodontic treatment with clear aligners, aiming to enhance bone remodeling and improve treatment stability.

METHODS: This retrospective study included 68 participants divided into two groups: 34 treated with LIPUS and 34 in a control group. Bone radio density was measured using Hounsfield units from CBCT scans before and after treatment. Statistical analyses included Mann-Whitney U tests and paired ttests.

RESULTS: The average age was 29.85 ± 14.85 years in the control group and 36.29 ± 12.78 years in the LIPUS group. Bone radio density in the upper arch of the LIPUS group significantly increased from 444.6 HU to 751.3 HU (p < 0.001), while the control group showed a slight decrease in the upper arch (657.4 HU to 650.5 HU, p = 0.86). In the lower arch, a similar trend was observed in the LIPUS group, with an increase from 767.7 HU to 823.4 HU (p = 0.17), though not statistically significant. There were no significant differences in post-treatment ABO DI scores between groups, suggesting equivalent effectiveness in achieving orthodontic outcomes.

CONCLUSION: LIPUS with clear aligners seems promising in enhancing bone radio density, indicating an improved bone remodeling effect. This highlights LIPUS’s potential as a beneficial adjunct in orthodontic treatments.

PMID:40248938 | DOI:10.2174/0115734056371884250324151755

Ann Med. 2025 Dec;57(1):2486585. doi: 10.1080/07853890.2025.2486585. Epub 2025 Apr 18.

ABSTRACT

INTRODUCTION: Perinatal opioid use disorder (OUD) remains a public health epidemic. Stress, anxiety and depression are disproportionately high among this population and are associated with poor recovery outcomes. Mindfulness interventions show promise for supporting recovery for women. This paper reports results of a pilot study to evaluate initial efficacy and acceptability of digitally delivered mindfulness meditation videos to reduce stress and promote mindfulness among women in recovery.

METHODS: Women with lived experience of OUD were recruited from three outpatient programs that provided care to pregnant and parenting women with a history of opioid use in rural northern New England (2 maternity care settings that offered buprenorphine as part of their service menu and 1 academic substance use treatment setting). In a pre-post study design, participants were randomly assigned to receive four of 16 short meditation videos, each delivered by email in a survey link over a 2-week period (2 per week) Videos were co-designed in earlier work with representative end-users, guided by evidence-based mindfulness interventions. Assessment included the Perceived Stress Scale and the Mindfulness Attention Awareness Scale. Participants rated each video on usefulness, enjoyability, ability to lower anxiety, and intention to use in the future. Participants also provided open-ended feedback about the videos. Data were analyzed using descriptive statistics, paired t-tests, and generalized linear modeling.

RESULTS: A total of 20 women, ages 24-36 years, completed the pilot study. Most participants (95%) were white and non-Hispanic, reflecting the rural region. Marginal mean perceived stress scores decreased significantly from 21.49 to 19.85 [p = 0.05, d = 0.43] and mean mindfulness scores increased significantly from 3.47 to 3.76 [p = 0.04, d = 0.45]. Overall, the meditation videos were rated as highly acceptable and useful and a majority (80%) indicated intention to use the meditations in the future.

CONCLUSION: Digitally delivered meditation videos were highly acceptable and useful to participants and the low dose intervention reduced stress and improved mindfulness. Findings inform directions for future research with larger samples to evaluate the effectiveness of this accessible digital intervention to support women in recovery and strategies for broadly implementing the intervention.

PMID:40248919 | DOI:10.1080/07853890.2025.2486585

Inquiry. 2025 Jan-Dec;62:469580251332057. doi: 10.1177/00469580251332057. Epub 2025 Apr 18.

ABSTRACT

Intimate Partner Violence (IPV) constitutes a significant public health crisis worldwide, with profound social and economic implications. This cross-sectional study explores the linkage between childhood adversities and IPV among married women in urban Bangladesh. From January to December 2018, the research involved 230 female participants from 3 urban sub-districts of Dhaka city. Utilizing the Adverse Childhood Experiences (ACE) questionnaire alongside an IPV questionnaire from the modified version of the Abuse Assessment Screen, this study examined the prevalence and severity of physical, sexual, and psychological abuse. Multiple linear regression analysis was employed to identify predictors of IPV. Findings reveal a strikingly high prevalence of IPV, with 91.7% of participants reporting experiences of some form of IPV. The frequency of IPV varied, with substantial portions experiencing multiple forms of abuse. The regression analysis indicated that higher ACE scores (B = 0.24, P < .05), inability to pay utility bills (B = 0.41, P < .05), and smoking at home (B = 0.53, P < .05) significantly contributed to increased IPV scores. The study underscores the profound impact of childhood adversities on the likelihood of experiencing IPV in adulthood, advocating for comprehensive public health strategies that address the prevention of violence across the lifespan. It emphasizes the need for targeted interventions that not only address the immediate factors contributing to IPV but also the broader socioeconomic issues that underpin such behaviors.

PMID:40248917 | DOI:10.1177/00469580251332057

J Chem Inf Model. 2025 Apr 18. doi: 10.1021/acs.jcim.4c02008. Online ahead of print.

ABSTRACT

Generative models have emerged as potentially powerful methods for molecular design, yet challenges persist in generating molecules that effectively bind to the intended target. The ability to control the design process and incorporate prior knowledge would be highly beneficial for better tailoring molecules to fit specific binding sites. In this paper, we introduce MolSnapper, a novel tool that is able to condition diffusion models for structure-based drug design by seamlessly integrating expert knowledge in the form of 3D pharmacophores. We demonstrate through comprehensive testing on both the CrossDocked and Binding MOAD data sets that our method generates molecules better tailored to fit a given binding site, achieving high structural and chemical similarity to the original molecules. Additionally, MolSnapper yields approximately twice as many valid molecules as alternative methods.

PMID:40248896 | DOI:10.1021/acs.jcim.4c02008

Euro Surveill. 2025 Apr;30(15). doi: 10.2807/1560-7917.ES.2025.30.15.2500247.

NO ABSTRACT

PMID:40248887 | DOI:10.2807/1560-7917.ES.2025.30.15.2500247