Grey modelling and real-time forecasting for the approximate non-homogeneous white exponential law BDS clock bias sequences.

Precise forecasting of satellite clock bias is crucial for ensuring service quality and enhancing the efficiency of real-time precise point positioning (PPP).The grey model with many benefits is an excellent choice for predicting real-time clock bias. However, the absolute prediction error of a small number of satellites is very high in actual forecasting process. In order to address this issue, a non-homogeneous white exponential law grey model has been constructed specifically for predicting clock bias sequences with non-homogeneous class ratio approximating constants. Considering that any model is difficult to apply to different kinds of satellite clocks and clock bias signals, an adaptive selection strategy for forecast model is proposed to ensure more excellent prediction accuracy. Afterwards, a prediction scenario based on the observed products of the BeiDou satellite navigation system (BDS) is created to demonstrate the effectiveness of the approach described in this article. The outcomes of the method are then compared with those of a single grey model and the ultra-rapid predicted products. The outcomes demonstrate that this strategy's prediction accuracy is less than 1 ns/day and that its prediction uncertainty is much decreased, which may improve data selection for real-time applications like real-time kinematics (RTK) and PPP.

The CAREPAL-8: a short screening tool for multidimensional family caregiver burden in palliative care.

BACKGROUND: Family caregivers of terminally ill and dying people do not only experience varying levels but also different dimensions of caregiver-related strain and burden. The aim of the study was to develop a short multidimensional screening tool for the detection of burden in family caregivers in palliative care. METHODS: Family caregivers of cancer patients newly admitted to specialist inpatient palliative care (N = 232) completed questionnaires on psychological burden, quality of life, social support and need fulfillment. A latent class mixture model was used to identify discrete classes of family caregivers related to their multidimensional caregiver burden. Multinomial logistic regression analyses were performed to identify the most predictive items from a set of established questionnaires. RESULTS: Four latent classes of family caregivers were identified: Currently stable caregivers (37%), Caregivers with unmet needs (20%), Psychologically burdened caregivers (30%), and High-risk caregivers (13%). Each of these classes describes a different risk profile of multidimensional family caregiver burden, although family caregivers exhibit high levels of distress across all classes. From a set of 48 items, we identified eight items that predicted the class membership best. These items represent the items of the novel multidimensional screening tool: The 8-item Screening Tool for Family Caregiver Burden in Palliative Care (CAREPAL-8). Except for social support, the items maintained fidelity to the conceptualization of multidimensional caregiver burden used in this study. A preliminary classification system was developed, which has yet to be validated. CONCLUSIONS: This study represents the first step in the establishment of a practical, self-administered screening tool that might help healthcare providers to tailor caregiver care according to their burden in daily practice. Brevity of the 8-item tool might facilitate its use in routine clinical care.

Risk stratification of new-onset psychiatric disorders using clinically distinct traumatic brain injury phenotypes.

BACKGROUND: Patients with traumatic brain injury (TBI) constitute a highly heterogeneous population, with varying risks for New-onset Psychiatric Disorders (NPDs). The objectives of this study were to identify TBI phenotypes and determine how NPDs differ among these phenotypes. METHODS: Hospitalized TBI patients from 2003 to 2019 were obtained from the provincial trauma registry. Propensity score matching was conducted to balance covariates among patients with TBI and controls. To uncover heterogeneity in TBI, latent class analysis (LCA)-based clustering was applied. LCA was conducted separately for two TBI cohorts: those with and without pre-injury psychiatric conditions The effect of classes on NPDs was assessed using log binomial regression models. RESULTS: A total of 3,453 patients with TBI and 13,112 controls were included in the analysis. In a conditional regression involving propensity matched patients with TBI and controls, TBI was significantly associated with the development of NPD-A (OR: 2.78; 95% CI: 2.49-3.09), as well as NPD-P (OR: 2.36; 95% CI: 2.07-2.70). Eight distinct latent classes were identified which differed in the incidence of NPDs. Four classes displayed a 53% (RR:1.53; 95% CI: 1.31-1.78), 48% (RR:1.48; 95% CI: 1.26-1.74), 28% (RR:1.28; 95% CI: 1.08-1.54), and 20% (RR: 1.20, 95%CI: 1.03-1.39), increased NPD risk. CONCLUSION: TBI is a significant predictor of NPDs. There are clinically distinguishable phenotypes with different patterns of NPD risk among patients with TBI. Identifying individuals with respect to their phenotype may improve risk stratification of patients with TBI and promote early intervention for psychiatric care in this vulnerable population.

Africa must create centres of educational excellence for innovation and development.

This paper explores the following development questions that perplex most Africans: "Why do African countries rely on foreign companies and foreign experts for almost all our development projects? Why can't we build our own roads, process our own food, and mine our own minerals, oil, and gas? Why don't we have world-class hospitals and industries? How can we have so much natural wealth and yet be so poor? Why do we invent so little?" The answer lies in our failure to implement idea number two. There are two major ideas in educational policy. Idea number one is the obligation to educate all children because it is their fundamental human right as enshrined in the 1948 United Nations Declaration of Human Rights. Idea number two is the strategy of establishing and sustaining world class schools and universities for the education of the most highly gifted and highly talented citizens. Developed countries deploy both ideas aggressively. Underdeveloped countries in Africa have not implemented idea number two. Countries that have deployed idea number two have at least one university ranked among the top 200 in the world. The presence of great universities (top 200) in a country is a 21st century indicator of the presence of high levels of innovation, technology, development and wealth in that country. According to the three major rankings of world universities (Shanghai-ARWU, THE, and QS-topuniversities.com), none of the world's top 100 great universities is in Africa. Although Africa was a pioneer among the continents in innovations such as human language, domestication of fire, making of tools, invention of agriculture, development of writing, and creation of great centers of learning in ancient times, it has fallen behind other continents over the last 500 years and it has been disrupted by enslavement and colonization, and the structural adjustment programs (SAPs) of the IMF and the World Bank. Ancient African centers of innovation included the Ancient City of Benin and Timbuktu in Western Africa, the Kingdom of Kush and ancient Egypt in northern Africa, Axum in Eastern Africa, Mapungubwe and Great Zimbabwe in Southern Africa, and the Kingdom of Kongo in central-Africa. An African renaissance will only occur when we implement idea number two by establishing world class schools and at least one great university per African country.

Cet article explore les questions de développement suivantes qui intriguent la plupart des Africains : « Pourquoi les pays africains s'appuient-ils sur des entreprises et des experts étrangers pour presque tous nos projets de développement ? Pourquoi ne pouvons-nous pas construire nos propres routes, transformer nos propres aliments et extraire nos propres minéraux, pétrole et gaz ? Pourquoi n'avons-nous pas d'hôpitaux et d'industries de classe mondiale ? Comment pouvons-nous avoir autant de richesses naturelles et pourtant être si pauvres ? Pourquoi inventons-nous si peu ? La réponse réside dans notre échec à mettre en œuvre l'idée numéro deux. Il y a deux idées majeures en politique éducative. L'idée numéro un est l'obligation d'éduquer tous les enfants, car il s'agit de leur droit humain fondamental tel que consacré dans la Déclaration des droits de l'homme des Nations Unies de 1948. L'idée numéro deux est la stratégie consistant à créer et à maintenir des écoles et des universités de classe mondiale pour l'éducation des citoyens les plus doués et les plus talentueux. Les pays développés déploient ces deux idées de manière agressive. Les pays sous-développés d'Afrique n'ont pas mis en œuvre l'idée numéro deux. Les pays qui ont déployé l'idée numéro deux comptent au moins une université classée parmi les 200 meilleures au monde. La présence de grandes universités (les 200 meilleures) dans un pays est un indicateur du XXIe siècle de la présence de niveaux élevés d'innovation, de technologie, de développement et de richesse dans ce pays. Selon les trois principaux classements des universités mondiales (Shanghai-ARWU, THE et QS-topuniversities.com), aucune des 100 meilleures universités mondiales ne se trouve en Afrique. Bien que l'Afrique ait été un continent pionnier en matière d'innovations telles que le langage humain, la domestication du feu, la fabrication d'outils, l'invention de l'agriculture, le développement de l'écriture et la création de grands centres d'apprentissage dans l'Antiquité, elle a pris du retard sur les autres continents au fil du temps. Ces 500 dernières années ont été perturbées par l'esclavage et la colonisation, ainsi que par les programmes d'ajustement structurel (PAS) du FMI et de la Banque mondiale. Les anciens centres d'innovation de l'Afrique comprenaient l'ancienne ville du Bénin et Tombouctou en Afrique de l'Ouest, le royaume de Kouch et l'Égypte ancienne en Afrique du Nord, Axum en Afrique de l'Est, Mapungubwe et le Grand Zimbabwe en Afrique australe et le royaume de Kongo en Afrique centrale. . Une renaissance africaine ne se produira que lorsque nous mettrons en œuvre l'idée numéro deux en créant des écoles de classe mondiale et au moins une grande université par pays africain.

HLA typing: A review of methodologies and clinical impact on haematopoietic cell transplantation.

The importance of the HLA gene system in haematopoietic cell transplant outcomes was established early on and advances in both fields have led to ever increasing success of this clinical therapy. In large part, improvements in the understanding of HLA have been driven by the advancement in typing technologies. Each iteration of typing technology has improved the resolution of HLA typing, and often enabled the identification of polymorphism within the HLA loci. The discovery of the enormous amount of variation in the HLA genes, and the need to be able to characterise this for clinical HLA typing, has often resulted in a move away from one typing method to another more suited to typing of this complexity. Today, the gold standard for HLA typing are methods that can produce definitive HLA typing results.

HLA-DM and HLA-DO interplay for the peptide editing of HLA class II in healthy tissues and leukemia.

HLA class II antigen presentation is modulated by the activity of the peptide editor HLA-DM and its antagonist HLA-DO, with their interplay controlling the peptide repertoires presented by normal and malignant cells. The role of these molecules in allogeneic hematopoietic cell transplantation (alloHCT) is poorly investigated. Balanced expression of HLA-DM and HLA-DO can influence the presentation of leukemia-associated antigens and peptides targeted by alloreactive T cells, therefore affecting both anti-leukemia immunity and the potential onset of Graft versus Host Disease. We leveraged on a large collection of bulk and single cell RNA sequencing data, available at different repositories, to comprehensively review the level and distribution of HLA-DM and HLA-DO in different cell types and tissues of the human body. The resulting expression atlas will help future investigations aiming to dissect the dual role of HLA class II peptide editing in alloHCT, and their potential impact on its clinical outcome.

Testing for Measurement Invariance (MI): Do the Structures of Microaggression, Discrimination, and Resilience Among Black Women Living with HIV Remain the Same Across Time?

Assessing measurement invariance and the interplay of discrimination, microaggressions, and resilience among Black women living with HIV (BWLWH) across time utilizing latent class and repeated measure analysis may provide novel insights. A total of 151 BWLWH in a southeastern U.S. city completed surveys focused on multiple forms of microaggressions and discrimination (race, gender, sexual orientation, or HIV-related) and resilience factors (social support, self-efficacy, post-traumatic growth) at baseline, 3 months, and 6 months. To capture the psychosocial domains of discrimination, microaggressions, and resilience, three latent factors were developed and measured across three time points. Latent class analysis was also conducted to identify and compare meaningful subgroups based on varying levels of discrimination, microaggressions, and resilience reported. Three latent classes were created. MI testing suggested that measurement invariance was partially met (established metric invariance and scalar invariance), and it is possible to compare factor means of discrimination, microaggressions, and resilience across time. Latent factor mean scores of microaggressions and discrimination decreased after 3 and 6 months and increased for resilience after 6 months and varied over time across the three latent classes identified. The subgroup with the lowest level of discrimination and microaggressions and the highest level of resilience reported at baseline, experienced increases in resilience after months 3 and 6. Clinical interventions, research, and policies aimed at promoting resilience and reducing structural and social barriers linked to racism, sexism, HIV stigma, and classism are needed to improve the health and well-being of BWLWH.

Combining features on vertical ground reaction force signal analysis for multiclass diagnosing neurodegenerative diseases.

Neurodegenerative diseases (NDDs), which are caused by the degeneration of neurons and their functions, affect a significant part of the world's population. Although gait disorders are one of the critical and common markers to determine the presence of NDDs, diagnosing which NDD the patients have among a group of NDDs using gait data is still a significant challenge to be addressed. In this study, we addressed the multi-class classification of NDDs and aim to diagnose Parkinson's disease (PD), Amyotrophic lateral sclerosis disease (AD), and Huntington's disease (HD) from a group containing NDDs and healthy control subjects. We also examined the impact of disease-specific identified features derived from VGRF signals. Detrended Fluctuation Analysis (DFA), Dynamic Time Warping (DTW) and Autocorrelation (AC) were used for feature extraction on Vertical Ground Reaction Force (VGRF) signals. To compare the performance of the features, we employed Support Vector Machines, K-Nearest Neighbors, and Neural Networks as classifiers. In three-class problem addressing the classification of AD, PD and HD 93.3% accuracy rate was achieved, while in the four classes case, in which NDDs and HC groups were considered together, 93.5% accuracy rate was yielded. Considering the disease-specific impact of features, it is revealed that while DFA based features diagnose patients with AD with the highest accuracy, DTW has been shown to be more successful in diagnosing PD. AC based features provided the highest accuracy in diagnosing HD. Although gait disorder is common for NDDs, each disease may have its own distinctive gait rhythms; therefore, it is important to identify disease-specific patterns and parameters for the diagnosis of each disease. To increase the diagnostic accuracy, it is necessary to use a combination of features, which were effective for each disease diagnosis. Determining a limited number of disease-specific features would provide NDD diagnostic systems suitable to be deployed in edge-computing environments.

A new type of Class C ß-lactamases defined by PIB-1. A metal-dependent carbapenem-hydrolyzing ß-lactamase, from Pseudomonas aeruginosa: Structural and functional analysis.

Antibiotic resistance is one of most important health concerns nowadays, and ß-lactamases are the most important resistance determinants. These enzymes, based on their structural and functional characteristics, are grouped in four categories (A, B, C and D). We have solved the structure of PIB-1, a Pseudomonas aeruginosa chromosomally-encoded ß-lactamase, in its apo form and in complex with meropenem and zinc. These crystal structures show that it belongs to the Class C ß-lactamase group, although it shows notable differences, especially in the Ω- and P2-loops, which are important for the enzymatic activity. Functional analysis showed that PIB-1 is able to degrade carbapenems but not cephalosporins, the typical substrate of Class C ß-lactamases, and that its catalytic activity increases in the presence of metal ions, especially zinc. They do not bind to the active-site but they induce the formation of trimers that show an increased capacity for the degradation of the antibiotics, suggesting that this oligomer is more active than the other oligomeric species. While PIB-1 is structurally a Class C ß-lactamase, the low sequence conservation, substrate profile and its metal-dependence, prompts us to position this enzyme as the founder of a new group inside the Class C ß-lactamases. Consequently, its diversity might be wider than expected.

From Triple- to Penta-Exposed Multiple Myeloma: A Real-World Study in a Medicare Population.

INTRODUCTION: Treatment of multiple myeloma (MM) has been transformed by novel therapies, including CD38 monoclonal antibodies (mAbs), immunomodulatory drugs (IMiDs), and proteasome inhibitors (PIs), resulting in increasing numbers of patients who are triple-class exposed (TCE; exposed to ≥ 1 drug in each class). Many patients are penta-exposed (PE; ≥ 2 IMiDs, ≥ 2 PIs, and a CD38 mAb), some are triple-class refractory (TCR), and some are PE and TCR (PE-TCR). Data on real-world outcomes in elderly patients with MM across this spectrum of exposure are limited. METHODS: Data from the Medicare Chronic Conditions Warehouse Database from November 2006-December 2020 were used to examine cohorts of TCE, TCR, PE, and PE-TCR patients. Outcomes were assessed from the start of the index line of therapy (LOT), defined as the first LOT after becoming TCE or PE. RESULTS: A total of 2830 TCE, 1371 TCR, 1121 PE, and 774 PE-TCR patients were identified. Pomalidomide was the most frequently used medication for the index LOT in all cohorts (32.6% [PE-TCR] to 43.3% [TCR]). The most frequently used regimens for the index LOT were pomalidomide plus daratumumab for TCE (17.2%) and PE (7.0%), pomalidomide plus carfilzomib for TCR (10.3%), and pomalidomide plus elotuzumab for PE-TCR (7.4%). Median time to discontinuation (TTD) ranged from 4.2 (PE-TCR) to 6.9 (TCE) months, and overall survival (OS) ranged from 13.0 (TCR) to 15.9 (PE) months. Healthcare resource utilization (HRU) was lowest for TCE and highest for PE-TCR patients. Mean monthly healthcare costs (HCC) ranged from $23,091 (TCE) to $24,412 (PE-TCR). MM medications represented 66.2% (PE-TCR) to 72.8% (TCE) of costs. CONCLUSIONS: In this study across a spectrum of Medicare TCE patients, there was heterogeneity in treatment regimens, suggesting no standard of care. TTD and OS were short, and HRU and HCC were high. These results underscore the potential for new therapies in this population.

An overview of early genetic predictors of IgA deficiency.

INTRODUCTION: Inborn errors of immunity (IEIs) refer to a heterogeneous category of diseases with defects in the number and/or function of components of the immune system. Immunoglobulin A (IgA) deficiency is the most prevalent IEI characterized by low serum level of IgA and normal serum levels of IgG and/or IgM. Most of the individuals with IgA deficiency are asymptomatic and are only identified through routine laboratory tests. Others may experience a wide range of clinical features including mucosal infections, allergies, and malignancies as the most important features. IgA deficiency is a multi-complex disease, and the exact pathogenesis of it is still unknown. AREAS COVERED: This review compiles recent research on genetic and epigenetic factors that may contribute to the development of IgA deficiency. These factors include defects in B-cell development, IgA class switch recombination, synthesis, secretion, and the long-term survival of IgA switched memory B cells and plasma cells. EXPERT OPINION: A better and more comprehensive understanding of the cellular pathways involved in IgA deficiency could lead to personalized surveillance and potentially curative strategies for affected patients, especially those with severe symptoms.

Socio-demographic factors and mental health trajectories in Australian children and primary carers: Implications for policy and intervention using latent class analysis.

Children's mental health status (MHS) is frequently influenced by their primary carers (PCs), underscoring the significance of monitoring disparities longitudinally. This research investigated the association between socio-demographic clusters and mental health trajectories among children and their PCs over time. Data from waves 6-9c2 of the Longitudinal Study of Australian Children (LSAC) were analyzed using Latent Class Analysis (LCA) to identify four socio-demographic classes among children aged 10-11 years at wave 6. Multinomial logistic regression and predictive marginal analysis explored associations between classes and mental health outcomes. PCs in Class 4 (disadvantaged and separated families with indigenous children) exhibited higher odds of borderline and abnormal MHS compared to Class 1 (prosperous and stable working families) across all waves. However, while MHS of PCs' impacted children consistently, the association with socio-demographic classes was significant only in wave 6. Class 4 children had elevated risks of mental illness compared to Class 1, while Class 3, characterized by educated working mothers, had lower risks. Reducing mental health risks entails addressing socio-economic disparities, supporting stable family structures, and offering tailored interventions like counseling and co-parenting support. Longitudinal monitoring and culturally sensitive approaches are crucial for promoting mental well-being across diverse groups.

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning.

Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate diagnosis and treatment. This study addresses critical challenges in the diagnostic imaging of lung and colon cancers, which are among the leading causes of cancer-related deaths worldwide. Recognizing the limitations of existing diagnostic methods, which often suffer from overfitting and poor generalizability, our research introduces a novel deep learning framework that synergistically combines the Xception and MobileNet architectures. This innovative ensemble model aims to enhance feature extraction, improve model robustness, and reduce overfitting.Our methodology involves training the hybrid model on a comprehensive dataset of histopathological images, followed by validation against a balanced test set. The results demonstrate an impressive classification accuracy of 99.44%, with perfect precision and recall in identifying certain cancerous and non-cancerous tissues, marking a significant improvement over traditional approach.The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Mapping (Grad-CAM), the model offers enhanced interpretability, allowing clinicians to visualize the diagnostic reasoning process. This transparency is vital for clinical acceptance and enables more personalized, accurate treatment planning. Our study not only pushes the boundaries of medical imaging technology but also sets the stage for future research aimed at expanding these techniques to other types of cancer diagnostics.

Targeting PI3K family with small-molecule inhibitors in cancer therapy: current clinical status and future directions.

The Phosphatidylinositol-3-kinase (PI3K) family is well-known to comprise three classes of intracellular enzymes. Class I PI3Ks primarily function in signaling by responding to cell surface receptor stimulation, while class II and III are more involved in membrane transport. Under normal physiological conditions, the PI3K signaling network orchestrates cell growth, division, migration and survival. Aberrant activation of the PI3K signaling pathway disrupts cellular activity and metabolism, often marking the onset of cancer. Currently, the Food and Drug Administration (FDA) has approved the clinical use of five class I PI3K inhibitors. These small-molecule inhibitors, which exhibit varying selectivity for different class I PI3K family members, are primarily used in the treatment of breast cancer and hematologic malignancies. Therefore, the development of novel class I PI3K inhibitors has been a prominent research focus in the field of oncology, aiming to enhance potential therapeutic selectivity and effectiveness. In this review, we summarize the specific structures of PI3Ks and their functional roles in cancer progression. Additionally, we critically evaluate small molecule inhibitors that target class I PI3K, with a particular focus on their clinical applications in cancer treatment. Moreover, we aim to analyze therapeutic approaches for different types of cancers marked by aberrant PI3K activation and to identify potential molecular targets amenable to intervention with small-molecule inhibitors. Ultimately, we propose future directions for the development of therapeutic strategies that optimize cancer treatment outcomes by modulating the PI3K family.

A review on over-sampling techniques in classification of multi-class imbalanced datasets: insights for medical problems.

There has been growing attention to multi-class classification problems, particularly those challenges of imbalanced class distributions. To address these challenges, various strategies, including data-level re-sampling treatment and ensemble methods, have been introduced to bolster the performance of predictive models and Artificial Intelligence (AI) algorithms in scenarios where excessive level of imbalance is present. While most research and algorithm development have been focused on binary classification problems, in health informatics there is an increased interest in the field to address the problem of multi-class classification in imbalanced datasets. Multi-class imbalance problems bring forth more complex challenges, as a delicate approach is required to generate synthetic data and simultaneously maintain the relationship between the multiple classes. The aim of this review paper is to examine over-sampling methods tailored for medical and other datasets with multi-class imbalance. Out of 2,076 peer-reviewed papers identified through searches, 197 eligible papers were chosen and thoroughly reviewed for inclusion, narrowing to 37 studies being selected for in-depth analysis. These studies are categorised into four categories: metric, adaptive, structure-based, and hybrid approaches. The most significant finding is the emerging trend toward hybrid resampling methods that combine the strengths of various techniques to effectively address the problem of imbalanced data. This paper provides an extensive analysis of each selected study, discusses their findings, and outlines directions for future research.

Inflammatory Trajectory and Anti-Inflammatory Pharmacotherapy in Frozen Elephant Trunk-Treated Acute Type I Aortic Dissection.

Background: Acute DeBakey type I aortic dissection is associated with high morbidity and mortality. Little is known regarding the role of leukocyte trajectory in prognosis. Methods: We included adult acute DeBakey type I aortic dissection patients with emergency frozen elephant trunk and total arch replacement in 2 cardiovascular centers (2020-2022). We used latent class mixed model to group patients according to their leukocyte patterns from hospital admission to the first 5 days after surgery. We investigated the association of leukocyte trajectory and 30-day and latest follow-up mortality (October 31, 2023), exploratorily analyzing the effects of ulinastatin treatment on outcome. Results: Of 255 patients included, 3 distinct leukocyte trajectories were identified: 196 in group I (decreasing trajectory), 34 in group II (stable trajectory), and 25 in group III (rising trajectory). Overall, 30-day mortality was 25 (9.8%), ranging from 8.2% (16/196) in group I, 8.8% (3/34) in group II, to 24.0% (6/25) in group III (P for trend = .036). Group III was associated with higher mortality both at 30 days (adjusted hazard ratio, 3.260; 95% CI, 1.071-9.919; P = .037) and at the last follow-up (adjusted hazard ratio, 2.840; 95% CI, 1.098-7.345; P = .031) compared with group I. Conclusions: Distinct and clinically relevant groups can be identified by analyzing leukocyte trajectories, and a rising trajectory was associated with higher short-term and midterm mortality.

Socio-economic status and head and neck cancer incidence in the Nordic countries.

BACKGROUND: The impact of societal factors on the occurrence of head and neck cancers (HNCs) remains understudied, especially in the Nordic countries. METHODS: To quantify the association between socio-economic status (SES) and the occurrence of HNCs, this cohort study uses data from the Nordic Occupational Cancer project that combine occupational and cancer registry data from 1961 to 2005 of 14.9 million individuals aged between 30 and 64 years. Occupational categories were combined into seven socio-economic categories. Standardized incidence ratio (SIR) analyses were conducted with the cancer incidence rates for the entire national study populations used as reference rates. RESULTS: Altogether, 83 997 HNCs-72% in men and 28% in women-were recorded. Among men, a gradient of risk associated with SES was observed for cancers of the tongue, other oral cavity subsites, pharynx, oropharynx and larynx in groups with lower SES. Managers showed decreased SIRs of 0.50 to -0.90 also for cancers of the lip, tongue, other oral cavity subsites, oropharynx, nasopharynx, nose and larynx. In contrast, excess risks of tongue, other oral cavity subsites, pharyngeal, oropharyngeal and laryngeal cancers were observed among clerical (SIRs 1.05-1.16), skilled workers (1.04-1.14), unskilled workers (1.16-1.26) and economically inactive men (1.38-1.87). Among women, no risk gradient similar to that in men was revealed. CONCLUSIONS: The current study underscores the influence of SES on the incidence of HNCs and highlights the need for targeted interventions, including tobacco and alcohol control policies, and improved access to healthcare services, particularly for socio-economically disadvantaged populations.

Motor progression trajectories and risk of mild cognitive impairment in Parkinson's disease: A latent class trajectory model from PPMI cohort.

AIMS: Rare studies have investigated the association between heterogeneity of motor progression and risk of early cognitive impairment in Parkinson's disease (PD). In this study, we aim to identify distinct trajectories of motor progression longitudinally and investigate their impact on predicting mild cognitive impairment (MCI). METHODS: A 5-year cohort including 415 PD patients at baseline was collected from the Parkinson's Progression Markers Initiative. The severity of motor symptoms was evaluated using the Movement Disorder Society Unified Parkinson's Disease Rating Scale part III. The latent class trajectory model and nonlinear mixed-effects model were used to analyze and delineate the longitudinal changes in motor symptoms. Propensity score matching (PSM) was used to minimize the impact of potential confounders. Cox proportional hazard models were applied to calculate hazard ratios for MCI, and a Kaplan-Meier curve was generated using the occurrence of MCI during the follow-up as the time-to-event. RESULTS: Two latent trajectories were identified: a mild and remitting motor symptoms class (Class 1, 33.01%) and a severe and progressive motor symptom class (Class 2, 66.99%). Patients in Class 2 initially exhibited severe motor symptoms that worsened progressively despite receiving anti-PD medications. In comparison, patients in Class 1 exhibited milder symptoms that improved following drug therapy and a slower progression. During a 5-year follow-up, patients in Class 2 showed a higher risk of developing MCI compared to those in Class 1 before PSM (Log-Rank 28.58, p < 0.001) and after PSM (Log-Rank 8.20, p = 0.004). CONCLUSIONS: PD patients with severe and progressive motor symptoms are more likely to develop MCI than those with mild and stable motor symptoms.

A preliminary investigation into the impact of soft tissue augmentation-based periodontal phenotype modification therapy for patients exhibiting class III decompensation.

BACKGROUND: Patients with skeletal angle Class III malocclusion usually have inadequate hard and soft tissue volume at the mandibular anterior teeth. The labial proclination at the teeth may lead to gingival recession. The purpose of this study was to explore whether periodontal phenotype modification therapy with soft tissue augmentation (PhMT-s) can prevent gingival recession in these patients. METHODS: Four patients with skeletal Class III malocclusion and a thin periodontal phenotype underwent surgical-orthodontic treatment. Prior to tooth movement, they underwent a minimally invasive vestibular incision with subperiosteal tunnel access combined with autogenous connective tissue grafts for periodontal phenotype modification with soft tissue augmentation (PhMT-s). The labial gingival thickness of the anterior mandibular teeth was measured at three distinct levels: at the cementoenamel junction (GT0), 3 mm apical to the CEJ (GT3), and 6 mm apical to the CEJ (GT6). These measurements were taken at baseline, three months following PhMT-s, and after tooth decompensation. Additionally, a biopsy sample was obtained from the PhMT-s site of one patient. All sections were subsequently stained using hematoxylin and eosin, Masson trichrome, Sirius Red, and immunohistochemistry. RESULTS: The thickness of the labial gingiva was increased about 0.42 to 2.00 mm after PhMT-s. At the end of pre-orthognathic surgical orthodontic treatment, the thickness of the labial gingiva was increased about - 0.14 to 1.32 mm compared to the baseline and no gingival recession occurred after the pre-orthognathic surgical orthodontic treatment. The histologic results demonstrated that the grafts obtained from the PhMT-s site exhibited increased deposition of collagen fibers. Moreover, the proportion of type III collagen increased and the grafts displayed significantly reduced positive expression of CD31 and OCN. CONCLUSIONS: PhMT-s increased the thickness of the soft tissue, stabilizing the gingival margin for teeth exhibiting a thin periodontal phenotype and undergoing labial movement. This is attributed to the increased deposition of collagen fibers.

Evaluation of Buccolingual Inclinations of First Molars Among Different Vertical Facial Patterns in Class II Division I Subjects.

Background Knowing the characteristics of vertical patterns is crucial to provide the best orthodontic treatment. Cone beam computed tomography (CBCT) offers a valuable tool for evaluating true buccolingual inclinations. The current study investigates the buccolingual inclination of first molars in adult subjects with different vertical facial patterns. Methods CBCT scans of 66 adult patients (31 males, 35 females) with a mean age of 31.6 years (SD: 6.4 years) exhibiting skeletal class II division I maxillomandibular relationships were employed. Participants were categorized into three groups based on linear and angular measurements: normodivergent group (n=22), hypodivergent group (n=22), and hyperdivergent group (n=22). The independent samples t-test and Mann-Whitney U-test were conducted to investigate statistical differences in terms of buccolingual inclination between the three vertical patterns. Results Statistically significant differences were observed in the buccolingual inclinations of both maxillary and mandibular first molars in the hypodivergent group compared to the other groups (p<0.05). Conclusions In patients with class II division I sagittal relationships, the buccolingual inclinations of the first molars exhibit similarities between normodivergent and hyperdivergent groups. However, these inclinations differ significantly in hypodivergent adult subjects.