Machine learning-based cluster analysis identifies four unique phenotypes of patients with degenerative cervical myelopathy with distinct clinical profiles and long-term functional and neurological outcomes.

BACKGROUND: Degenerative cervical myelopathy (DCM), the predominant cause of spinal cord dysfunction among adults, exhibits diverse interrelated symptoms and significant heterogeneity in clinical presentation. This study sought to use machine learning-based clustering algorithms to identify distinct patient clinical profiles and functional trajectories following surgical intervention. METHODS: In this study, we applied k-means and latent profile analysis (LPA) to identify patient phenotypes, using aggregated data from three major DCM trials. The combination of Nurick score, NDI (neck disability index), neck pain, as well as motor and sensory scores facilitated clustering. Goodness-of-fit indices were used to determine the optimal cluster number. ANOVA and post hoc Tukey's test assessed outcome differences, while multinomial logistic regression identified significant predictors of group membership. FINDINGS: A total of 1047 patients with DCM (mean [SD] age: 56.80 [11.39] years, 411 [39%] females) had complete one year outcome assessment post-surgery. Latent profile analysis identified four DCM phenotypes: "severe multimodal impairment" (n = 286), "minimal impairment" (n = 116), "motor-dominant" (n = 88) and "pain-dominant" (n = 557) groups. Each phenotype exhibited a unique symptom profile and distinct functional recovery trajectories. The "severe multimodal impairment group", comprising frail elderly patients, demonstrated the worst overall outcomes at one year (SF-36 PCS mean [SD]: 40.01 [9.75]; SF-36 MCS mean [SD], 46.08 [11.50]) but experienced substantial neurological recovery post-surgery (ΔmJOA mean [SD]: 3.83 [2.98]). Applying the k-means algorithm yielded a similar four-class solution. A higher frailty score and positive smoking status predicted membership in the "severe multimodal impairment" group (OR 1.47 [95% CI 1.07-2.02] and 1.58 [95% CI 1.25-1.99, respectively]), while undergoing anterior surgery and a longer symptom duration were associated with the "pain-dominant" group (OR 2.0 [95% CI 1.06-3.80] and 3.1 [95% CI 1.38-6.89], respectively). INTERPRETATION: Unsupervised learning on multiple clinical metrics predicted distinct patient phenotypes. Symptom clustering offers a valuable framework to identify DCM subpopulations, surpassing single patient reported outcome measures like the mJOA. FUNDING: No funding was received for the present work. The original studies were funded by AO Spine North America.

Exploring the influence of slaughterhouse type and slaughtering steps on Campylobacter jejuni contamination in chicken meat: A cluster analysis approach.

Campylobacter jejuni (C. jejuni), a foodborne pathogen, poses notable hazards to human health and has significant economic implications for poultry production. This study aimed to assess C. jejuni contamination levels in chicken carcasses from both backyard and commercial slaughterhouses in Chiang Mai province, Thailand. It also sought to examine the effects of different slaughtering practices on contamination levels and to offer evidence-based recommendations for reducing C. jejuni contamination. Through the sampling of 105 chicken carcasses and subsequent enumeration of C. jejuni, the study captured the impact of various slaughtering practices. Utilizing k-modes clustering on the observational and bacterial count data, the research identified distinct patterns of contamination, revealing higher levels in backyard operations compared to commercial ones. The application of k-modes clustering highlighted the impact of critical slaughtering practices, particularly chilling, on contamination levels. Notably, samples with the lowest bacterial counts were typically from the chilling step, a practice predominantly found in commercial facilities. This observation underpins the recommendation for backyard slaughterhouses to incorporate ice in their post-evisceration soaking process. Mimicking commercial practices, this chilling method aims to inhibit C. jejuni growth by reducing carcass temperature, thereby enhancing food safety. Furthermore, the study suggests backyard operations adopt additional measures observed in commercial settings, like segregating equipment for each slaughtering step and implementing regular cleaning protocols. These strategic interventions are pivotal in reducing contamination risks, advancing microbiological safety in poultry processing, and aligning with global food safety enhancement efforts.

Phenotyping variability in early socio-communicative skills in young children with autism and its influence on later development.

Children with autism spectrum disorder (ASD) often face challenges in early social communication skills, prompting the need for a detailed exploration of specific behaviors and their impact on cognitive and adaptive functioning. This study aims to address this gap by examining the developmental trajectories of early social communication skills in preschoolers with ASD aged 18-60 months, comparing them to age-matched typically developing (TD) children. Utilizing the early social communication scales (ESCS), the research employs a longitudinal design to capture changes over time. We apply a principal component analysis (PCA) to ESCS variables to identify underlying components, and cluster analysis to identify subgroups based on preverbal communication profiles. The results reveal consistent differences in early social communication skills between ASD and TD children, with ASD children exhibiting reduced skills. PCA identifies two components, distinguishing objects-directed behaviors and social interaction-directed behaviors. Cluster analysis identifies three subgroups of autistic children, each displaying specific communication profiles associated with distinct cognitive and adaptive functioning trajectories. In conclusion, this study provides a nuanced understanding of early social communication development in ASD, emphasizing the importance of low-level behaviors. The identification of subgroups and their unique trajectories contributes to a more comprehensive understanding of ASD heterogeneity. These findings underscore the significance of early diagnosis, focusing on specific behaviors predicting cognitive and adaptive functioning outcomes. The study encourages further research to explore the sequential development of these skills, offering valuable insights for interventions and support strategies.

Research on the relation between hydro-chemical and geological characteristics in karst area: Case study in Zhong Liang Mountain, Southwest China.

Karst groundwater, which is one of most important drinking water sources, is vulnerable to be polluted as its closed hydraulic relation with surface water. Thus, it is very important to identify the groundwater source to control groundwater pollution. The Pearson correlation coefficient among major ions (Na + K+, Ca2+, Mg2+, HCO3 -, SO4 2-, and Cl-) was employed to deduce the groundwater types in Zhong Liang Mountain, Southwest China. Then, the combined method of principal component analysis and cluster analysis were employed to identify the groundwater sources in a typical karst region of southwest China. The results shown that (1) the high positive correlation between cations and anions indicated the water-rock reaction of Ca-HCO3, Ca-SO4, (Na + K)-Cl, and Mg-SO4. (2) The major two principal components that would represent water-rock reaction of CaSO4 and Ca-HCO3 would, respectively, explain 60.41% and 31.80% of groundwater information. (3) Based on the two principal components, 33 groundwater samples were clustered into eight groups through hierarchical clustering, each group has similar water-rock reaction. The findings would be employed to forecast the surge water, that was an important work for tunnel construction and operation. PRACTITIONER POINTS: The components of groundwater was highly correlated with water-rock reaction. The principal component analysis screens the types of groundwater. The cluster analysis identifies the groundwater sources.

Cluster analysis of clinical manifestations assigns systemic lupus erythematosus-phenotype subgroups: a multicentre study on 440 patients.

OBJECTIVE: Systemic lupus erythematous (SLE) is a heterogenous disease characterised by a large panel of autoantibodies and a wide spectrum of clinical signs and symptoms that engender different outcomes. We aimed to identify distinct, homogeneous SLE patients' phenotypes. METHODS: This retrospective study enrolled SLE patients meeting the Systemic Lupus International Collaborating Clinics (SLICC) classification criteria, enrolled in the French multicentre "APS (antiphospholipid syndrome) and SLE‿ Registry. Based on 29 variables selected to cover a broad range of clinical and laboratory (excluding autoantibodies) SLE manifestations, unsupervised multiple correspondence analysis followed by hierarchical ascendent-clustering analysis assigned different phenotypes. RESULTS: We included 440 patients, mostly women (94.3%). Median age at SLE diagnosis was 24 (IQR 19-32) years. Cluster analysis yielded three distinct subgroups based on cumulative clinical manifestations, not autoantibody pattern. Cluster 1 (n=91) comprised mostly Caucasian patients, with APS-associated clinical and biological manifestations, e.g., livedo, seizure, thrombocytopaenia and haemolytic anaemia. Cluster 2 (n=221), the largest, included patients with mild clinical manifestations, mainly articular, more frequently associated with Sjögren's syndrome and with less frequent autoantibody-positivity. Cluster 3 (n=128) consisted of patients with the largest panel of SLE-specific clinical manifestations (cutaneous, articular, proliferative nephritis, pleural, cardiac and haematological), the most frequent autoantibody-positivity, low complement levels, and more often of Asian and sub-Saharan African origin. CONCLUSION: This unsupervised clustering method distinguished three distinct SLE patient subgroups, highlighting SLE heterogeneity.

Fire protection priorities in the oak forests of Iran with an emphasis on vertebrate habitat preservation.

This study examines the impact of fire incidents on wildlife and habitats in the western oak forests of Iran (Zagros region). These forests are globally recognized for their exceptional biodiversity but are frequently threatened by wildfires. To achieve this, the study uses the space-time scan statistics permutation (STSSP) model to identify areas with a higher frequency of fires. The study also analyzes the effects of fires on the Zagros forests from 2000 to 2021 using remote-sensing MODIS data. Also, to understand the elements at risk of fire, burned areas were assessed based on the richness of vertebrate species, determined by the distribution of 88 vertebrate species. The results show that the annual fire rate in the Zagros forests is 76.2 (fire occurrences per year), calculated using the Poisson distribution. Findings show the highest fire rates are found in the northwest and a part of the south of the Zagros. The northwest of the Zagros also has the largest number of single fires and clusters, indicating a wide spatial distribution of fire in these regions. On the other side, it was unexpectedly found that these regions have the richest number of species and higher habitat value. The results demonstrate a significant correlation between the value of the habitat and the extent of burned areas (p < 0.05). The study also reveals that the greatest impact of fires is on small vertebrates. The overlap of frequent fire spots with the richest regions of Zagros oak forests in terms of vertebrate diversity emphasizes the need for strategic forest risk reduction planning, especially in these priority zones.

Assessing the impact of history of medicine research: A scientometric and altmetric analysis.

Background and Aims: After conducting a comprehensive literature search of two medical electronic databases, PubMed and Embase, as well as two citation databases, Web of Science Core Collections (WoS) and Scopus, we aimed to conduct an Altmetric and Scientometric analysis of the History of Medicine literature in medical research. Methods: The following software tools were used for analyzing the retrieved records from PubMed and Embase databases and conducting a collaboration analysis to identify the countries involved in scientific medical papers, as well as clustering keywords to reveal the trend of History of Medicine research for the future. These software tools (VOSviewer 1.6.18 and Spss 16) allowed the researchers to visualize bibliometric networks, perform statistical analysis, and identify patterns and trends in the data. Results: Our analysis revealed 53,771 records from PubMed and 54,405 records from EMBASE databases retrieved in the field of History of Medicine by 105,286 contributed authors in WoS. We identified 157 countries that collaborated on scientific medical papers. By clustering 59,995 keywords, we were able to reveal the trend of History of Medicine research for the future. Our findings showed a positive association between traditional bibliometrics and social media metrics such as the Altmetric Attention Score in the History of Medicine literature (p < 0.05). Conclusion: Sharing research findings of articles in social scientific networks will increase the visibility of scientific works in History of Medicine research, which is one of the most important factors influencing the citation of articles. Additionally, our overview of the literature in the medical field allowed us to identify and examine gaps in the History of Medicine research.

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that about one third of diabetes patients have signs of diabetic retinopathy, and another third may suffer from serious retinopathy that threatens vision. However, the pathogenesis of diabetic retinopathy is still unclear, and there is no systematic method to detect the onset of the disease and effectively predict its occurrence. In this study, we used medical detection data from diabetic retinopathy patients to determine key biomarkers that induce disease onset through back propagation neural network algorithm and hierarchical clustering analysis, ultimately obtaining early warning signals of the disease. The key markers that induce diabetic retinopathy have been detected, which can also be used to explore the induction mechanism of disease occurrence and deliver strong warning signal before disease occurrence. We found that multiple clinical indicators that form key markers, such as glycated hemoglobin, serum uric acid, alanine aminotransferase are closely related to the occurrence of the disease. They respectively induced disease from the aspects of the individual lipid metabolism, cell oxidation reduction, bone metabolism and bone resorption and cell function of blood coagulation. The key markers that induce diabetic retinopathy complications do not act independently, but form a complete module to coordinate and work together before the onset of the disease, and transmit a strong warning signal. The key markers detected by this algorithm are more sensitive and effective in the early warning of disease. Hence, a new method related to key markers is proposed for the study of diabetic microvascular lesions. In clinical prediction and diagnosis, doctors can use key markers to give early warning of individual diseases and make early intervention.

Phenotypic subgroup in serologically active clinically quiescent systemic lupus erythematosus: A cluster analysis based on CSTAR cohort.

BACKGROUND: Serologically active clinically quiescent (SACQ) is a state within systemic lupus erythematosus (SLE) characterized by elevated serologic markers without clinical activity. The heterogeneity in SACQ patients poses challenges in disease management. This multicenter prospective study aimed to identify distinct SACQ subgroups and assess their utility in predicting organ damage. METHODS: SACQ was defined as a sustained period of at least 6 months with persistent serologic activity, marked by positive anti-double-stranded DNA (dsDNA) antibodies and/or hypocomplementemia, and without clinical activity. Cluster analysis was employed, utilizing 16 independent components to delineate phenotypes. FINDINGS: Among the 4,107 patients with SLE, 990 (24.1%) achieved SACQ within 2.0 ± 2.3 years on average. Over a total follow-up of 7,105.1 patient years, 340 (34.3%) experienced flares, and 134 (13.5%) developed organ damage. Three distinct SACQ subgroups were identified. Cluster 1 (n = 219, 22.1%) consisted predominantly of elderly males with a history of major organ involvement at SLE diagnosis, showing the highest risk of severe flares (16.4%) and organ damage (27.9%). Cluster 2 (n = 279, 28.2%) was characterized by milder disease and a lower risk of damage accrual (5.7%). Notably, 86 patients (30.8%) in cluster 2 successfully discontinued low-dose glucocorticoids, with 49 of them doing so without experiencing flares. Cluster 3 (n = 492, 49.7%) featured the highest proportion of lupus nephritis and a moderate risk of organ damage (11.8%), with male patients showing significantly higher risk of damage (hazard ratio [HR] = 4.51, 95% confidence interval [CI], 1.82-11.79). CONCLUSION: This study identified three distinct SACQ clusters, each with specific prognostic implications. This classification could enhance personalized management for SACQ patients. FUNDING: This work was funded by the National Key R&D Program (2021YFC2501300), the Beijing Municipal Science & Technology Commission (Z201100005520023), the CAMS Innovation Fund (2021-I2M-1-005), and National High-Level Hospital Clinical Research Funding (2022-PUMCH-D-009).

New insights into recalled parental behavior in social anxiety disorder: A cluster analytic approach.

BACKGROUND: Social Anxiety Disorder (SAD) is a highly heterogeneous disorder. To enlighten its heterogeneity, this study focused on recalled parental behavior and aimed to empirically identify if there are subgroups of SAD based on recalled parental behavior by means of cluster analysis. Further, the study investigated whether those subgroups differed on clinical, trauma, and personality variables. METHODS: This study included 505 individuals diagnosed with SAD and 98 adult controls who were asked to fill out the Parental Bonding Instrument (PBI), the Adverse Childhood Experiences Questionnaire (ACE), and the Temperament and Character Inventory (TCI). Cluster analysis determined whether there are meaningful SAD subgroups based on PBI. The clusters obtained were compared with each other and with the control group with regard to clinical, ACE, and TCI variables. RESULTS: The cluster analysis revealed two SAD clusters based on recalled parental behavior. SAD individuals in the first cluster (49.3 %) perceived their parents as intermediately caring, but not as overcontrolling. SAD individuals in the second cluster (50.7 %) perceived their parents as less caring and overcontrolling, reported more severe clinical symptoms and trauma, and had lower values in Self-Directedness and Cooperativeness. LIMITATIONS: The present study is cross-sectional, therefore unable to confirm causal interpretations. CONCLUSION: Parenting is meaningful to enlighten the heterogeneity of SAD symptomatology and to specify treatment approaches as there are two meaningful subgroups in individuals with SAD corresponding to differences in clinical presentation, trauma, and personality.

Novel Sleep Phenotypic Profiles Associated With Incident Atrial Fibrillation in a Large Clinical Cohort.

BACKGROUND: While sleep disorders are implicated in atrial fibrillation (AF), the interplay of physiologic alterations and symptoms remains unclear. Sleep-based phenotypes can account for this complexity and translate to actionable approaches to identify at-risk patients and therapeutic interventions. OBJECTIVES: This study hypothesized discrete phenotypes of symptoms and polysomnography (PSG)-based data differ in relation to incident AF. METHODS: Data from the STARLIT (sleep Signals, Testing, And Reports LInked to patient Traits) registry on Cleveland Clinic patients (≥18 years of age) who underwent PSG from November 27, 2004, to December 30,2015, were retrospectively examined. Phenotypes were identified using latent class analysis of symptoms and PSG-based measures of sleep-disordered breathing and sleep architecture. Phenotypes were included as the primary predictor in a multivariable-adjusted Cox proportional hazard models for incident AF. RESULTS: In our cohort (N = 43,433, age 51.8 ± 14.5 years, 51.9% male, 74.9% White), 7.3% (n = 3,166) had baseline AF. Over a 7.6- ± 3.4-year follow-up period, 8.9% (n = 3,595) developed incident AF. Five phenotypes were identified. The hypoxia subtype (n = 3,245) had 48% increased incident AF (HR: 1.48; 95% CI: 1.34-1.64), the apneas + arousals subtype (n = 4,592) had 22% increased incident AF (HR: 1.22; 95% CI: 1.10-1.35), and the short sleep + nonrapid eye movement subtype (n = 6,126) had 11% increased incident AF (HR: 1.11; 95% CI: 1.01-1.22) compared with long sleep + rapid eye movement (n = 26,809), the reference group. The hypopneas subtype (n = 2,661) did not differ from reference (HR: 0.89; 95% CI: 0.77-1.03). CONCLUSIONS: Consistent with prior evidence supporting hypoxia as an AF driver and cardiac risk of the sleepy phenotype, this constellation of symptoms and physiologic alterations illustrates vulnerability for AF development, providing potential value in enhancing our understanding of integrated sleep-specific symptoms and physiologic risk of atrial arrhythmogenesis.

Negative symptoms in treatment-resistant schizophrenia and its relationship with functioning.

BACKGROUND: Recent operational criteria for treatment-resistant schizophrenia (TRS) recognized positive and negative symptoms. TRS patients may have heterogeneity in negative symptoms, but empirical data were lacking. We aimed to characterize TRS patients based on negative symptoms using cluster analysis, and to examine between-cluster differences in social functioning. METHODS: We administered the Clinical Assessment Interview of Negative symptoms (CAINS), Brief Negative Symptom Scale (BNSS), the Positive and Negative Syndrome Scale (PANSS) and the Social and Occupational Functional Assessment (SOFAS to 126 TRS outpatients. All patients also completed the Temporal Experience of Pleasure Scale (TEPS), the Emotion Expressivity Scale (EES), and the Social Functional Scale (SFS). A two-stage hierarchical cluster analysis was performed with the CAINS, TEPS and EES as clustering variables. We validated the clusters using ANOVAs to compare group differences in the BNSS, PANSS, SOFAS and SFS. RESULTS: Clustering indices supported a 3-cluster solution. Clusters 1 (n = 46) and 3 (n = 16) exhibited higher CAINS scores than Cluster 2 (n = 64), and were negative-symptom TRS subtypes. Cluster 1 reported lower TEPS than Cluster 3; but Cluster 3 reported lower EES than Cluster 1. Upon validation, Clusters 1 and 3 exhibited higher BNSS scores than Cluster 2, but only Cluster 1 exhibited lower SOFAS and higher PANSS general symptoms than Cluster 2. Both Clusters 1 and 3 had higher self-report functioning than Cluster 2. CONCLUSION: We provided evidence for heterogeneity of negative symptoms in TRS. Negative symptoms can characterize TRS patients and predict functional outcome.

Integration of two-dimensional echocardiography: A novel risk indicator for ST-segment elevation myocardial infarction.

AIMS: We aim to integrate the parameters of two-dimensional (2D) echocardiography and identify the high-risk population for all-cause mortality in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI). METHODS: The study involved a retrospective cohort population with STEMI who were admitted to Yongchuan Hospital of Chongqing Medical University between January 2016 and January 2019. Baseline data were collected, including 2D echocardiography parameters and left ventricular ejection fraction (LVEF). The parameters of 2D echocardiography were subjected to cluster analysis. Logistic regression models were employed to assess univariate and multivariate adjusted odds ratios (ORs) of cluster information in relation to all-cause mortality. Four logistic regression models were generated, utilizing cluster information, clinical variables, clinical variables in conjunction with LVEF, and clinical variables in conjunction with LVEF and cluster information as predictive variables, respectively. The area under the curve (AUC) were utilized to evaluate the incremental risk stratification value of cluster information. RESULTS: The study included 633 participants with 28.8% female, a mean age of 65.68 ± 11.98 years. Over the course of a 3-year follow-up period, 108 (17.1%) patients experienced all-cause mortality. Utilizing cluster analysis of 2D echocardiography parameters, the patients were categorized into two distinct clusters, with statistically significant differences observed in most clinical variables, echocardiography, and survival outcomes between the clusters. Multivariate regression analysis revealed that cluster information was independently associated with the risk of all-cause mortality with adjusted OR 7.33 (95% confidence interval [CI] 3.99-14.06, P < 0.001). The inclusion of LVEF enhanced the predictive capacity of the model utilized with clinical variables with AUC 0.848 (95% CI 0.809-0.888) versus AUC 0.872 (95% CI 0.836-0.908) (P < 0.001), and the addition of cluster information further improved its predictive performance with AUC 0.906 (95% CI 0.878-0.934, P < 0.001). This cluster analysis was translated into a free available online calculator (https://app-for-mortality-prediction-cluster.streamlit.app/). CONCLUSIONS: The 2D echocardiographic diagnostic information based on cluster analysis had good prognostic value for STEMI population, which was helpful for risk stratification and individualized intervention.

Gen Z's Willingness to Adopt Plant-Based Diets: Empirical Evidence from Greece, India, and the UK.

Comprising the largest population cohort on this planet, Gen Z presents a future-oriented consumer segment driven by climate change and food. This study sought to investigate Gen Z's perceptions toward plant-based foods and diets and explore the relationship that attitude components, meal preparation involvement, personal and lifestyle factors, and perceived barriers in adopting a plant-based diet have with willingness to adopt green-eating practices. Using cross-sectional data from university students in Greece, India, and the UK, various tools were employed to determine the factors influencing youths' consumer behavior toward animal-protein substitutes. PCA indicated the underlying dimensions of students' viewpoints on plant-based foods, whereas hierarchical and k-means clustering provided the cluster structure. An ordered probit model was estimated to delineate Gen Z's willingness to adopt plant-based diets and distinguish among mostly unwilling, somewhat willing, and mostly willing youths. Our findings identified two consumer segments, namely proponents and opponents of plant-based foods and diets, with statistically significant differences in the perceived health benefits of plant-based diets, attachment to animal-based proteins, perceived exclusion of animal-based foods, dissatisfaction with plant-based foods' attributes, and demand for ensuring adequate protein intake. The ordered probit model estimates showed that there is a "homogeneity" in the factors influencing youths' intention to adopt plant-based diets, with attitude components, meal preparation indicators, perceived barriers to eating "green", and personal factors, such as self-assessed knowledge of healthy eating and physical activity, being strongly associated with students' willingness to switch to plant-based diets in all three countries. Mapping potential obstacles and enablers in terms of shifting to more green-eating behaviors, our findings could add information to better understand the factors affecting food choice and youths' transition to a more sustainable lifestyle.

From the genesis to the present: The evolution of sublingual immunotherapy for cedar pollinosis.

In 2004, we started the initial attempt to evaluate the efficacy of SLIT for Japanese cedar pollinosis (JCP) using Japanese cedar (JC) pollen extract solution through a multicenter, placebo-controlled, double-blind comparative study. Based on its success in demonstrating the substantial efficacy of SLIT, we next conducted a larger-scale study by administering JC pollen to all JCP patients recruited. It was because of aiming to ascertain the effectiveness and safety of SLIT and its underlying mechanisms by comparing high- and non-responder patients. Despite limitations posed by liquid medication, significant effectiveness and safety demonstrated by the 2-year treatment served as the foundation for launching the first SLIT medicine for JCP, in 2014. Furthermore, in addition to the clearer Th1/Th2-imbalanced property in the high-responders, the possible involvement of bitter taste receptors in CD4+ T cells, apoptosis pathways in CD4+ T cells and basophils, and inducing a mast cell degranulation inhibitory molecule in the effect of SLIT was demonstrated. To solve the limitations posed by liquid medication, clinical trials evaluating JC pollen sublingual tablets started in 2014. Due to the minimal side effects, ease of administration, and convenient storage, the sublingual tablet medicine was launched in 2018. Giving the ongoing rise in demand for SLIT and considering that more than 1% of JCP patients are currently undergoing SLIT, the practical use of this treatment for multiple allergens is becoming increasingly important.

Investigating immune profile by CyTOF in patients with Eosinophilic Esophagitis after treatment with orodispersible budesonide.

Eosinophilic esophagitis (EoE) is a chronic Th2 mediated inflammatory disease of the esophagus driven by dietary or inhalant allergens which if left untreated, leads to fibrosis and poor esophageal function. Although the inflammation in the esophagus is dominated by eosinophils, there are also elevated levels of T and B cells. Blood samples from ten patients with EoE before and after treatment with orodispersible budesonide and ten healthy controls were compared using cytometry by time-of-flight (CyTOF). An antibody panel was designed that covers the major immunological cell populations with particular focus on eosinophils. The data was analyzed with multivariate methods and cluster analysis. Correlation analysis was done between immune markers and endoscopic, histological and symptomatologic assessments. Our analysis revealed that patients with EoE had lower levels of effector memory T cells after treatment with orodispersible budesonide to the same level as healthy subjects. In addition, more suppressive eosinophils were present in the circulation of EoE patients before treatment and more immature eosinophils were present after treatment. Furthermore, levels of galectin-10+ eosinophils correlated with histological findings in esophageal tissue from EoE patients. In all patients, the peak eosinophils were decreased after treatment with orodispersible budesonide. Intriguingly, 90% of the patients had remission in the histological assessment and 50% improved in the endoscopic assessment. This study reports a detailed immune profile in patients with EoE before and after treatment with orodispersible budesonide and it is a step toward finding blood-based immune parameters that could be useful to monitor response to treatment.

A new classification for emergency critically ill patients and analysis of their adverse events during intrahospital transport: A cluster analysis.

BACKGROUND: Critical patients may experience various adverse events during transportation within hospitals. Therefore, quickly evaluating and classifying patients before transporting them from the emergency department and focusing on managing high-risk patients are critical. At present, no unified classification method exists; all the current approaches are subjective. AIMS: To ensure transportation safety, we conducted a cluster analysis of critically ill patients transferred from the emergency department to the intensive care unit. STUDY DESIGN: Single-centre cohort study. This study was conducted at a comprehensive first-class teaching hospital in Beijing. Convenience sampling and continuous enrolment were employed. We collected data from 1 January 2019, to 31 December 2021. All patients were transferred from the emergency department to the intensive care unit, and cluster analysis was conducted using five variables. RESULTS: A total of 584 patients were grouped into three clusters. Cluster 1 (high systolic blood pressure group) included 208 (35.6%) patients. Cluster 2 (high heart rate and low blood oxygen group) included 55 (9.4%) patients. Cluster 3 (normal group) included the remaining 321 (55%) patients. The oxygen saturation levels of all the patients were lower after transport, and the proportion of adverse events (61.8%) was the highest in Cluster 2 (p < .05). CONCLUSIONS: This study utilized data on five important vital signs from a cluster analysis to explore possible patient classifications and provide a reference for ensuring transportation safety. RELEVANCE TO CLINICAL PRACTICE: Before transferring patients, we should classify them and implement targeted care. Changes in blood oxygen levels in all patients should be considered, with a focus on the occurrence of adverse events during transportation among patients with high heart rates and low blood oxygen levels.

Identifying Variation in Physical Health Behaviors and Depressive Symptoms among Religiosity Clusters of African American Adults in the United States.

Religiosity is an important factor in the lives of many African Americans, who suffer a greater health burden than their White counterparts. In this study, we examined associations between dimensions of religiosity with health behaviors and depressive symptoms in a sample of African American adults in the United States. Participants (N = 2086) completed five measures of religiosity (religious involvement, positive and negative religious coping, scriptural influence, belief in illness as punishment for sin) and measures of several health behaviors, cancer screening behaviors, and depressive symptoms. Using cluster analysis to examine the deep structure of religiosity, three clusters emerged: Positive Religious, Negative Religious, and Low Religious. In general, the Positive Religious group engaged in more healthy behaviors (e.g., fruit and vegetable consumption, fecal occult blood test) and fewer risky health behaviors (e.g., smoke and consume alcohol), and reported fewer depressive symptoms than did the Negative Religious and/or Low Religious groups. Theoretical implications and implications for interventions by clergy and mental health professionals are discussed.

Nothing means anything if everything means something: exploring the issues of coping profiles and the person-centered approach.

BACKGROUND: Adopting a person-centered approach to coping potentially allows researchers to explore the multifaceted nature of the construct. However, this increasingly adopted approach also has limitations. Namely, employing cluster or latent profile analysis to investigate coping through a person-centered lens often brings a lack of generalizability and subjectivity in interpreting the generated profiles. As such, this study aimed to explore the impact of varied methodology in person-centered investigations of coping profiles. METHODS: 682 university students' (M = 21.3 years old, SD = 3.5) responses to the COPE Inventory were analyzed across item, subscale, and higher-order category levels using cluster and latent profile analysis to produce 6 finalized models for cross-method comparison. RESULTS: Throughout 19 analyses, approach coping, avoidance coping, low coping, and help-seeking profiles were consistently identified, alluding to the potential of universal coping trends. However, membership overlap across COPE structures and methodology was largely inconsistent, with individual participants classified into theoretically distinct profiles based on the methodology employed. CONCLUSION: While evidence suggests latent profile analysis provides a more rigorous approach, the significant impact of minor methodological variations urges a reevaluation of person-centered approaches and incorporation of multi-construct data to enhance the understanding of coping profiles.

Measuring the Scientific Impact of Growth Hormone Treatment in Burns: A Bibliometric Analysis Based on CiteSpace.

This study utilized CiteSpace software to conduct a bibliometric analysis of the literature related to the use of growth hormone in treating burns. The results showed that the research on this topic has attracted increasing attention from scholars worldwide, with the number of publications increasing annually. The research teams and institutions involved in this field are mainly concentrated in China, followed by the United States, Russia, and other countries. The analysis also revealed the prominent co-cited literature and the most influential authors in the field, such as Herndon，DN.and Li Y. The main research themes identified in the literature included the effects of growth hormone on wound healing, tissue repair and regeneration, inflammatory responses, and cell proliferation. Additionally, the research on the clinical applications of growth hormone in burn treatment has been expanded to include areas such as metabolic regulation, immune function, and the prevention of infections. The findings of this study provide useful insights into the current status and future directions of research in the field of growth hormone treatment of burns.