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The precise and efficient diagnosis of an individual’s skeletal class is necessary in orthodontics to ensure correct and stable treatment planning. However, it is difficult to efficiently determine the true skeletal class due to several correlations between various anatomic structures. The primary outcome of this prospective cross-sectional study was developing a machine learning model for classifying patients as skeletal class I and III. Furthermore, the investigation intended to compare cephalometric variables between skeletal class I and III as well as between age and sex-specific subgroups to analyse correlations between cephalometric parameters and to perform Principal Component Analysis (PCA) to identify the most important variables contributing to skeletal class I and III variances. This study was based on the pre-treatment lateral cephalograms of 509 German orthodontic patients diagnosed as skeletal class I (n = 341) or III (n = 168) according to the individualised ANB of Panagiotidis and Witt, following descriptive analyses of cephalometric parameters, correlation analyses followed by Principal Component Analysis (PCA) to identify key cephalometric variables. Machine learning models, including Random Forest (RF), Classification and Regression Trees (CART), k-nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Generalized Linear Model (GLM), were evaluated for accuracy. Within the same skeletal class, age influenced cephalometric parameters: in skeletal class I, adolescents presented a more horizontal pattern (PFH/AFH, Gonial angle, NL-ML) and prominent mandible (SNB, SN-Pg) than children. In skeletal class III, the degree of sagittal discrepancy between jaw bases was most notable in adults (ANB: III_Age > 21-III _14 < Age < 20 − 1.78°). Comparing skeletal class I and III, the latter had more prognathic mandibles (SNB) and compensated incisors’ inclination (proclination of the upper (+ 1/NA: 9.01°), retroinclination of the lower incisors (− 1/ML: 8.99°). Among others, a correlation was found between the sagittal (degree of prognathism, SNB) and vertical (inclination, ML-NSL) orientation of the mandible (skeletal class I: p < 0.001, ρ = − 0.742; skeletal class III: p < 0.001, ρ = − 0.665). PCA revealed that the first four principal components explain 93% of the variance in skeletal class I/III diagnosis and that these parameters had the most influence loading score on the first component-PFH/AFH ratio (0.35), SNB angle (0.35), SN-Pg (0.37), and ML-NSL (− 0.35). Evaluating machine learning models, the general model, including all cephalometric parameters, age, and sex, resulted in perfect (1.00) accuracy and kappa scores compared to the gold standard Calculated_ANB with the model’s RF and CART. In model 2 the amount of input variables was reduced (Wits, SNB only), but the accuracy (0.88), and kappa (0.73) were still good in the KNN model. In the last section of this study, we applied different machine learning classification models. We examined the ability of the parameters—SNA, SNB, and ML-NSL angles to predict the classification as skeletal class I or III. The results demonstrated that the GLM model gained an accuracy of 0.99 (Accuracy = 0.99, Kappa = 0.97). The precise diagnosis of skeletal class I/III can be simplified by applying the machine learning model GLM with the input variables SNA, SNB, and ML-NSL only. This stresses the importance of their correct identification. However, considering all skeletal classes, a larger population is needed to validate and generalize this approach.

The rising incidence of dementia and associated neurodegenerative disorders poses a growing public health challenge. These conditions have traditionally been studied as isolated central nervous system disorders; however, emerging evidence suggests that broader systemic factors, including chronic inflammation, immune dysregulation, metabolic dysfunction, and genetic susceptibility, may also play a role. This review examines the interconnection between autoimmune diseases and metabolic syndromes in the pathogenesis and exacerbation of neurodegeneration. Conditions such as rheumatoid arthritis, systemic lupus erythematosus, and type 1 diabetes mellitus have been associated with a heightened risk of developing dementia through chronic immune activation, blood–brain barrier disruption, and neuroinflammatory signaling. Similarly, metabolic disorders such as diabesity promote insulin resistance and oxidative stress, accelerating cognitive decline. The review also discusses glaucoma as a neurodegenerative condition with autoimmune features, underscoring the need for expanded classification and treatment strategies. A key focus is the utilization of the Collaborative Cross (CC) mouse model, which enables the study of gene–environment interactions across genetically diverse backgrounds. Findings from CC mice reveal strain‐dependent susceptibility to inflammation, cognitive impairment, and gut–brain axis dysfunction, providing a translational bridge to human variability. This review highlights the importance of integrating precision‐based approaches to dementia research that consider systemic influences. Advancing our understanding of these multiorgan interactions holds potential for designing precision‐based therapeutic approaches to postpone the onset or reduce the incidence of neurodegenerative conditions. The increasing prevalence of dementia and related neurodegenerative diseases—including Alzheimer's disease, Parkinson's disease, multiple sclerosis, and amyotrophic lateral sclerosis—poses a growing public health challenge. These conditions have traditionally been studied as isolated central nervous system disorders, but emerging evidence points to broader systemic factors, including chronic inflammation, immune dysregulation, metabolic dysfunction, and genetic susceptibility. Diseases such as rheumatoid arthritis, systemic lupus erythematosus, and type 1 diabetes mellitus have been linked to increased risk of dementia through chronic immune activation, blood–brain barrier disruption, and neuroinflammatory signaling. Similarly, metabolic disorders such as diabesity promote insulin resistance and oxidative stress, accelerating cognitive decline. A key focus is the application of the Collaborative Cross (CC) mouse model, which enables the study of gene–environment interactions across genetically diverse backgrounds. Findings from CC mice reveal strain‐dependent susceptibility to inflammation, cognitive impairment, and gut–brain axis dysfunction, providing a translational bridge to human variability. This review emphasizes the importance of integrated, precision‐based approaches to dementia research that account for systemic influences.

Background Classification is one of the most common tasks in artificial intelligence (AI) driven fields in dentistry and orthodontics. The AI abilities can significantly improve the orthodontist’s critical mission to diagnose and treat patients precisely, promptly, and efficiently. Therefore, this study aims to develop a machine-learning model to classify German orthodontic patients as skeletal class I or II based on minimal cephalometric parameters. Eventually, clustering analysis was done to understand the differences between clusters within the same or different skeletal classes. Methods A total of 556 German orthodontic patients were classified into skeletal class I ( n  = 210) and II ( n  = 346) using the individualized ANB. Hierarchical clustering analysis used the Euclidean distances between data points and Ward’s minimum variance method. Six machine learning models (random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), linear discriminant analysis (LDA), classification and regression trees (CART), and General Linear Model (GLM)) were evaluated considering their accuracy, reliability, sensitivity, and specificity in diagnosing skeletal class I and II. Results The clustering analysis results showed the power of this tool to cluster the results into two–three clusters that interestingly varied significantly in many cephalometric parameters, including NL-ML angle, NL-NSL angle, PFH/AFH ratio, gonial angle, SNB, Go-Me (mm), Wits appraisal, ML-NSL, and part of the dental parameters. The CART model achieved 100% accuracy by considering all cephalometric and demographic variables, while the KNN model performed well with three input parameters (ANB, Wits, SNB) only. Conclusions The KNN model with three key variables demonstrated sufficient accuracy for classifying skeletal classes I and II, supporting efficient and still personalized orthodontic diagnostics and treatment planning. Further studies with balanced sample sizes are needed for validation.

Head and neck squamous cell cancer (HNSCC) is a leading global malignancy. Every year, More than 830 000 people are diagnosed with HNSCC globally, with more than 430 000 fatalities. HNSCC is a deadly diverse malignancy with many tumor locations and biological characteristics. It originates from the squamous epithelium of the oral cavity, oropharynx, nasopharynx, larynx, and hypopharynx. The most frequently impacted regions are the tongue and larynx. Previous investigations have demonstrated the critical role of host genetic susceptibility in the progression of HNSCC. Despite the advances in our knowledge, the improved survival rate of HNSCC patients over the last 40 years has been limited. Failure to identify the molecular origins of development of HNSCC and the genetic basis of the disease and its biological heterogeneity impedes the development of new therapeutic methods. These results indicate a need to identify more genetic factors underlying this complex disease, which can be better used in early detection and prevention strategies. The lack of reliable animal models to investigate the underlying molecular processes is one of the most significant barriers to understanding HNSCC tumors. In this report, we explore and discuss potential research prospects utilizing the Collaborative Cross mouse model and crossing it to mice carrying single or double knockout genes (e.g. Smad4 and P53 genes) to identify genetic factors affecting the development of this complex disease using genome‐wide association studies, epigenetics, microRNA, long noncoding RNA, lncRNA, histone modifications, methylation, phosphorylation, and proteomics. Head and neck squamous cell cancer (HNSCC) is the sixth most frequent cancer worldwide. Annually, there are more than 830 000 individuals diagnosed with HNSCC globally, and more than 430 000 patients die of this disease. HNSCC is a fatal heterogeneous disease with highly variable tumor sites and biological behaviors. It arises from the squamous epithelium of the oral cavity, oropharynx, nasopharynx, larynx, and hypopharynx, their hypothetical locations. The tongue and larynx are the most often affected areas. This cancer can cause irregular patches or ulcers in the mouth and throat, along with unusual bleeding or pain, depending on the location. Additionally, it was shown that it might lead to persistent sinus congestion, sore earache, and pain or difficulty in swallowing, with other complications including a hoarse voice, breathing difficulty, or enlarged lymph nodes. The classical risk factors for HNSCC are smoking and excessive alcohol use. Viral infections, including human papillomaviruses (HPV) and Epstein–Barr virus (EBV) cause a substantial and rising proportion of these tumors. Previous studies have shown the crucial role of host genetic susceptibility in HNSCC development. Despite the advances in our knowledge of epidemiology and pathogenesis, the improved survival rate of HNSCC patients over the past four decades has been limited. The lack of identifying the molecular carcinogenesis of HNSCC development and the genetic basis of the disease and biological heterogeneity impedes the development of new therapeutic methods. These results indicate a need to enhance the identification of more genetic factors underlying this complex disease, which can be better sued in early detection and prevention strategies. The lack of reliable animal models to explore the underlying molecular mechanisms is one of the most severe impediments to better understanding the HNSCC tumors. In this report, we address HNSCC and discuss future research opportunities using the Collaborative Cross (CC) mouse model and crossing it to mice carrying single or double knockout genes (e.g. Smad4 and P53 genes) to comprehensively identify genetic factors affecting the development of this complex by studying genome‐wide association study (GWAS), epigenetics, small and microRNA, long noncoding RNA (lncRNA), lncRNA, Histone modifications, Acetylation, Methylation, phosphorylation, and Proteomics.

Smad4, a critical tumor suppressor gene, plays a significant role in pancreatic biology and tumorigenesis. Genetic background and sex are known to influence phenotypic outcomes, but their impact on pancreatic weight in Smad4-deficient mice remains unclear. This study investigates the impact of Smad4 deficiency on pancreatic weight in first-generation (F1) mice from diverse collaborative cross (CC) lines, focusing on the influence of genetic background and sex. F1 mice were generated by crossbreeding female CC mice with C57BL/6J-Smad4tm1Mak males. Genotyping confirmed the presence of Smad4 knockout alleles. Mice were housed under standard conditions, euthanized at 80 weeks, and their pancreatic weights were measured, adjusted for body weight, and analyzed for effects of Smad4 deficiency, sex, and genetic background. The overall population of F1 mice showed a slight but non-significant increase in adjusted pancreatic weights in heterozygous knockout mice compared to wild-type mice. Sex-specific analysis revealed no significant difference in males but a significant increase in adjusted pancreatic weights in heterozygous knockout females. Genetic background analysis showed that lines CC018 and CC025 substantially increased adjusted pancreatic weights in heterozygous knockout mice. In contrast, other lines showed no significant difference or varied non-significant changes. The interplay between genetic background and sex further influenced these outcomes. Smad4 deficiency affects pancreatic weight in a manner significantly modulated by genetic background and sex. This study highlights the necessity of considering these factors in genetic research and therapeutic development, demonstrating the value of the collaborative cross mouse population in dissecting complex genetic interactions.

It is increasingly recognized that young, chow‐fed inbred mice poorly model the complexity of human carcinogenesis. In humans, age and adiposity are major risk factors for malignancies, but most genetically engineered mouse models (GEMM) induce carcinogenesis too rapidly to study these influences. Standard strains, such as C57BL/6, commonly used in GEMMs, further limit the exploration of aging and metabolic health effects. A similar challenge arises in modeling periodontitis, a disease influenced by aging, diabesity, and genetic architecture. We propose using diverse mouse populations with hybrid vigor, such as the Collaborative Cross (CC) × ApcMin hybrid, to slow disease progression and better model human colorectal cancer (CRC) and comorbidities. This perspective highlights the advantages of this model, where delayed carcinogenesis reveals interactions with aging and adiposity. Unlike ApcMin mice, which develop cancer rapidly, CC × ApcMin hybrids recapitulate human‐like progression. This facilitates the identification of modifier loci affecting inflammation, diet susceptibility, organ size, and polyposis distribution. The CC × ApcMin model offers a transformative platform for studying CRC as a disease of adulthood, reflecting its complex interplay with aging and comorbidities. The insights gained from this approach will enhance early detection, management, and treatment strategies for CRC and related conditions. It is increasingly recognized that young, chow‐fed inbred mice poorly model the complexity of human carcinogenesis in multiple respects. In humans, age and adiposity are the major risk factors for the majority of malignancies. Although the development of genetically engineered mouse models (GEMM) of cancer has recapitulated many aspects of human cancer, the models typically induce carcinogenesis so rapidly that the influence of aging and disturbed metabolic health is unexplored. We considered that the development of periodontitis (PD) is a problem of similar biomedical complexity involving unexplored components of aging, diabesity, and host genetic architecture. To address both these problems, we reasoned that diverse mouse populations exhibiting hybrid vigor would allow slower development of both familial carcinogenesis and PD. Here we summarize the approach of identifying quantitative trait loci (QTL) by crossing GEMMs with recombinant inbred lines from the Collaborative Cross, a diverse population of mice derived from five laboratory and three wild mouse strains. The expected insights from this research program are crucial for early detection and better management and treatment of disease.

Background Over the past few decades, a threefold increase in obesity and type 2 diabetes (T2D) has placed a heavy burden on the health‐care system and society. Previous studies have shown correlations between obesity, T2D, and neurodegenerative diseases, including dementia. It is imperative to further understand the relationship between obesity, T2D, and cognitive deficits. Methods This investigation tested and evaluated the cognitive impact of obesity and T2D induced by high‐fat diet (HFD) and the effect of the host genetic background on the severity of cognitive decline caused by obesity and T2D in collaborative cross (CC) mice. The CC mice are a genetically diverse panel derived from eight inbred strains. Results Our findings demonstrated significant variations in the recorded phenotypes across different CC lines compared to the reference mouse line, C57BL/6J. CC037 line exhibited a substantial increase in body weight on HFD, whereas line CC005 exhibited differing responses based on sex. Glucose tolerance tests revealed significant variations, with some lines like CC005 showing a marked increase in area under the curve (AUC) values on HFD. Organ weights, including brain, spleen, liver, and kidney, varied significantly among the lines and sexes in response to HFD. Behavioral tests using the Morris water maze indicated that cognitive performance was differentially affected by diet and genetic background. Conclusions Our study establishes a foundation for future quantitative trait loci mapping using CC lines and identifying genes underlying the comorbidity of Alzheimer's disease (AD), caused by obesity and T2D. The genetic components may offer new tools for early prediction and prevention. Previous studies have shown correlations between obesity, type 2 diabetes (T2D), and neurodegenerative diseases, including dementia. A wide range of illnesses can result in dementia, including Alzheimer's disease (AD). The disorders that fall under the general category of “dementia” are caused by abnormal alterations in the brain. To counteract their significant influence on public health, it is imperative to gain an in‐depth understanding of the relationship between obesity, T2D, and cognitive deficits. We evaluate the cognitive impact of obesity and T2D induced by high‐fat diet (HFD) in collaborative cross (CC) mice. Our findings have demonstrated differences in the assessed phenotypes between the different CC lines and the C57BL/6J reference line. These findings highlight the role the host's genetic background plays in determining the degree of obesity and T2D development, as well as how it affects different organ weights and cognitive deficiencies that could worsen into AD when faced with a HFD challenge.

The correct classification of orthodontic patients is essential in individualized diagnostics and treatment planning. However, due to the complexity of the craniofacial skeleton and differences related to gender, age, and ethnicity, cephalometric analysis can be prone to errors. This multicenter, cross-sectional study aimed to compare cephalometric measurements between skeletal class I and II in German orthodontic patients and analyze the effect of gender/age subgroups. In total, 556 German orthodontic patients were included and stratified into skeletal class I (  = 210) and II (  = 346), based on the individualized ANB of Panagiotidis and Witt (Calculated_ANB). Both classes presented a mean age of 13 with a range of 6.6-41 years and 5.4-53 years in classes I and II, respectively. Regarding the gender variations, most participants were females,  = 194 (56%) among class I, and  = 125 (60%) among class II. Cephalometric parameters were compared between classes and among age and gender-specific subgroups, followed by identifying correlations and performing principal component analysis (PCA). Class II patients presented a more considerable sagittal discrepancy between jaw bases than class I cases (Calculated_ANB 2.8° vs. 0.025°), a more horizontal growth pattern (Gonion angle 119° vs. 123°), and compensated inclinations of the incisors in the upper (+ 1/NL 71° vs. 68°) and lower jaw (-1/ML 84° vs. 80°). Correlations were found between sagittal, vertical, and dental cephalometric parameters, which were strongest in adult class II males. Finally, ML-NSL angle, SNPg angle, PFH/AFH ratio, and SNB angle are related to the variations of the first four components. The differences in cephalometric parameters between skeletal class I and II demonstrate certain configurations in vertical, sagittal, and dental parameters, and identifying these marks precisely will enable accurate diagnosis. In addition, the variations concerning gender and age highlight the possible influence of these factors on orthodontic diagnostics and treatment planning. Future studies with equal sample sizes among subgroups must validate these findings. Finally, the PCA results highlighted that the mandible's vertical and sagittal position has a strong influence on the diagnosis of skeletal class I/II, which highlights the importance of identifying the corresponding reference marks.

Background: Obesity and type 2 diabetes (T2D) promote inflammation, increasing the risk of colorectal cancer (CRC). High-fat diet (HFD)-induced obesity is key to these diseases through biological mechanisms. This study examined the impact of genetic background on the multimorbidity of intestinal cancer, T2D, and inflammation due to HFD-induced obesity. Methods: A cohort of 357 Collaborative Cross (CC) mice from 15 lines was fed either a control chow diet (CHD) or HFD for 12 weeks. Body weight was tracked biweekly, and blood glucose was assessed at weeks 6 and 12 via intraperitoneal glucose tolerance tests (IPGTT). At the study’s endpoint, intestinal polyps were counted, and cytokine profiles were analyzed to evaluate the inflammatory response. Results: HFD significantly increased blood glucose levels and body weight, with males showing higher susceptibility to T2D and obesity. Genetic variation across CC lines influenced glucose metabolism, body weight, and polyp development. Mice on HFD developed more intestinal polyps, with males showing higher counts than females. Cytokine analysis revealed diet-induced variations in pro-inflammatory markers like IL-6, IL-17A, and TNF-α, differing by genetic background and sex. Conclusions: Host genetics plays a crucial role in susceptibility to HFD-induced obesity, T2D, CRC, and inflammation. Genetic differences across CC lines contributed to variability in disease outcomes, providing insight into the genetic underpinnings of multimorbidity. This study supports gene-mapping efforts to develop personalized prevention and treatment strategies for these diseases.

This study investigates the significance of skeletal transverse dimension (STD) in orthodontic therapy and its impact on occlusal relationships. The primary goal is to enhance understanding and promote the integration of transverse skeletal diagnostics into routine orthodontic assessments. To achieve this aim, the study employs a comprehensive approach, utilizing model analysis, clinical assessments, radiographic measurements, and occlusograms. The initial step involves a meticulous assessment of deficiencies in the maxilla, mainly focusing on transverse dimension issues. Various successful diagnostic methods are employed to ascertain the type and presence of these deficiencies. Furthermore, the study compares surgically assisted maxillary expansion (SARME) and orthopedic maxillary expansion (OME) in addressing skeletal transverse issues. Stability assessments and efficacy analyses are conducted to provide valuable insights into the superiority of SARME over OME. The findings reveal that proper evaluation of STD is crucial in orthodontic diagnosis, as overlooking transverse dimension issues can lead to complications such as increased masticatory muscle activity, occlusal interferences, and an elevated risk of gingival recession. Surgically assisted maxillary expansion emerges as a more stable solution than orthopedic methods. In conclusion, incorporating skeletal transverse diagnostics into routine orthodontic assessments is imperative for achieving optimal occlusal relationships and minimizing negative consequences on dentition, periodontium, and joints. The study emphasizes the significance of accurate three-dimensional assessments and recommends the consideration of SARME over OME for addressing skeletal transverse deficiencies. Finally, the Collaborative Cross (CC) mouse model is also a novel mouse model for studying complex traits. Exploring the Collaborative Cross mouse model opens avenues for future research, promising further insights into transverse skeletal issues in orthodontics.