BACKGROUND: Generalized pustular psoriasis (GPP), a rare and recurrent autoinflammatory disease, imposes a substantial burden on patients and society. Awareness of GPP in China remains limited. METHODS: This cross-sectional survey, conducted between September 2021 and May 2023 across 14 hospitals in China, included GPP patients of all ages and disease phases. Data collected encompassed demographics, clinical characteristics, economic impact, disease severity, quality of life, and treatment-related complications. Risk factors for GPP recurrence were analyzed. RESULTS: Among 127 patients (female/male ratio = 1.35:1), the mean age of disease onset was 25 years (1st quartile [Q1]-3rd quartile [Q3]: 11-44 years); 29.2% had experienced GPP for more than 10 years. Recurrence occurred in 75.6% of patients, and nearly half reported no identifiable triggers. Younger age at disease onset ( P  = 0.021) and transitioning to plaque psoriasis ( P  = 0.022) were associated with higher recurrence rates. The median diagnostic delay was 8 months (Q1-Q3: 2-41 months), and 32.3% of patients reported misdiagnoses. Comorbidities were present in 53.5% of patients, whereas 51.1% experienced systemic complications during treatment. Depression and anxiety affected 84.5% and 95.6% of patients, respectively. During GPP flares, the median Dermatology Life Quality Index score was 19.0 (Q1-Q3: 13.0-23.5). This score showed significant differences between patients with and without systemic symptoms; it demonstrated correlations with both depression and anxiety scores. Treatment costs caused financial hardship in 55.9% of patients, underscoring the burden associated with GPP. CONCLUSIONS The substantial disease and economic burdens among Chinese GPP patients warrant increased attention. Patients with early onset disease and those transitioning to plaque psoriasis require targeted interventions to mitigate the high recurrence risk.
Humans ; Male ; Female ; Psoriasis/pathology* ; Adult ; Cross-Sectional Studies ; Adolescent ; Child ; Young Adult ; Quality of Life ; Middle Aged ; China/epidemiology* ; Recurrence ; Risk Factors ; Surveys and Questionnaires ; East Asian People

As a new type of production factor that can empower the development of new quality productivity, the data element is an important engine to promote the high quality development of the industry. Traditional Chinese medicine(TCM) dispensing is the most basic work of TCM clinical pharmacy, and its quality directly affects the clinical efficacy of TCM. The integration of data elements and TCM dispensing can stimulate the innovation and vitality of the TCM dispensing industry and promote the high-quality and sustainable development of the industry. A large-scale, detailed, and systematic study on TCM dispensing was conducted. The innovative practice path of data fusion construction in the whole process of TCM dispensing was investigated by integrating the digital resources "nine full activities" of TCM dispensing, creating the digital dictionary of "TCM clinical information data elements", and exploring innovative applications of TCM dispensing driven by data and technology, so as to promote the standardized, digital, and intelligent development of TCM dispensing in medical health services. The research content of this project was successfully selected as the second batch of "Data element×" typical cases of National Data Administration in 2024, which is the only selected case in the field of TCM.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal ; Humans

OBJECTIVE: Pneumoconiosis, a lung disease caused by irreversible fibrosis, represents a significant public health burden. This study investigates the causal relationships between gut microbiota, gene methylation, gene expression, protein levels, and pneumoconiosis using a multi-omics approach and Mendelian randomization (MR). METHODS: We analyzed gut microbiota data from MiBioGen and Esteban et al. to assess their potential causal effects on pneumoconiosis subtypes (asbestosis, silicosis, and inorganic pneumoconiosis) using conventional and summary-data-based MR (SMR). Gene methylation and expression data from Genotype-Tissue Expression and eQTLGen, along with protein level data from deCODE and UK Biobank Pharma Proteomics Project, were examined in relation to pneumoconiosis data from FinnGen. To validate our findings, we assessed self-measured gut flora from a pneumoconiosis cohort and performed fine mapping, drug prediction, molecular docking, and Phenome-Wide Association Studies to explore relevant phenotypes of key genes. RESULTS: Three core gut microorganisms were identified: Romboutsia ( OR = 0.249) as a protective factor against silicosis, Pasteurellaceae ( OR = 3.207) and Haemophilus parainfluenzae ( OR = 2.343) as risk factors for inorganic pneumoconiosis. Additionally, mapping and quantitative trait loci analyses revealed that the genes VIM, STX8, and MIF were significantly associated with pneumoconiosis risk. CONCLUSIONS This multi-omics study highlights the associations between gut microbiota and key genes ( VIM, STX8, MIF) with pneumoconiosis, offering insights into potential therapeutic targets and personalized treatment strategies.
Humans ; Male ; East Asian People/genetics* ; Europe ; Gastrointestinal Microbiome ; Lung ; Macrophage Migration-Inhibitory Factors/metabolism* ; Mendelian Randomization Analysis ; Multiomics ; Pneumoconiosis/microbiology* ; Intramolecular Oxidoreductases

OBJECTIVE: To analyze the distribution of thalassemia (referred to as "thalassemia") gene variant types in the population of the Wuhan area, aiming to provide a genetic basis for the precise prevention and control as well as clinical diagnosis of thalassemia in the Wuhan region. METHODS: In this study, 2 133 suspected thalassemia patients and individuals undergoing prenatal screening who visited the Department of Hematology, Obstetrics and Gynecology, Reproductive Medicine, Pediatrics, and Neurology at Wuhan First Hospital from October 2022 to October 2024 were selected as the research subjects. Peripheral blood samples were collected from the patients. The common 27 thalassemia genotypes of α- and β-thalassemia were initially screened using fluorescence PCR melting curve analysis technology. For samples where the fluorescence PCR melting curve results indicated unknown variants or where the clinical phenotype was inconsistent with the common genotypes, Sanger sequencing technology was used for review and verification. RESULTS: Among the 2 133 specimens analyzed, common thalassemia gene variants were detected in 210 cases (9.85%, 210/2 133). A total of 156 cases (8.05%, 156/1 938) of thalassemia gene variants were detected in females and 54 cases (27.69%, 54/195) in males. A total of 94 cases (4.41%, 94/2 133) of α-thalassemia were detected, including 46 cases (2.16%, 46/2 133) of silent α-thalassemia, 47 cases (2.20%, 47/2 133) of mild α-thalassemia, and 1 case (0.05%, 1/2 133) of intermediate α-thalassemia. Additionally, 111 cases of β-thalassemia were identified (5.20%, 111/2 133), including 51 cases of β/β⁺ thalassemia (2.39%, 51/2 133), 59 cases of β/β⁰ thalassemia (2.77%, 59/2 133), and 1 case of β⁺/HbE thalassemia (0.05%, 1/2 133). αβ-composite thalassemia gene variants were detected in 5 cases (0.23%, 5/2 133), including 1 complex variant with a genotype of --^SEA/αα combined with CD41-42 (-TTCT) and 29(A>G), representing a heterozygous variant of three genotypes. Rare globin gene variants were detected in 3 cases, including HBB:c.60C>T, HBB:c.-146G>T, and HBA2:c.*12G>A. CONCLUSION The Wuhan region exhibits a relatively high prevalence of thalassemia genes with notable gender disparities. While maintaining focus on thalassemia screening for females, enhanced males screening efforts and genetic counseling should be implemented in future prevention programs.
Humans ; Female ; Male ; Genotype ; beta-Thalassemia/genetics* ; China ; Thalassemia/genetics* ; alpha-Thalassemia/genetics* ; Genetic Variation

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

This study examined the case of 27-year-old female who underwent orthognathic surgery to correct class 3 malocclusion, resulting in an unnatural smile. The research aimed to assess the efficacy of non-invasive treatment in enhancing facial muscle movement during smiling to achieve a natural smile. The patient received eight sessions of treatment using non-invasive devices, and facial assessment were conducted using three-dimensional scanner (Morpheus 3D Scanner) to evaluate facial features in both relaxed and maximum smile states pre- and post-treatment. The results demonstrated improvements in the symmetry of the lower lip height during smiling, along with increases in mouth width and volume in the upper central area. Subjectively, the patient also reported enhanced comfort while smiling. These findings suggest that non-invasive procedures can effectively improve unnatural smiles following orthognathic surgery, contributing to a more aesthetically pleasing smile presentation.

This study examined the case of 27-year-old female who underwent orthognathic surgery to correct class 3 malocclusion, resulting in an unnatural smile. The research aimed to assess the efficacy of non-invasive treatment in enhancing facial muscle movement during smiling to achieve a natural smile. The patient received eight sessions of treatment using non-invasive devices, and facial assessment were conducted using three-dimensional scanner (Morpheus 3D Scanner) to evaluate facial features in both relaxed and maximum smile states pre- and post-treatment. The results demonstrated improvements in the symmetry of the lower lip height during smiling, along with increases in mouth width and volume in the upper central area. Subjectively, the patient also reported enhanced comfort while smiling. These findings suggest that non-invasive procedures can effectively improve unnatural smiles following orthognathic surgery, contributing to a more aesthetically pleasing smile presentation.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

This study examined the case of 27-year-old female who underwent orthognathic surgery to correct class 3 malocclusion, resulting in an unnatural smile. The research aimed to assess the efficacy of non-invasive treatment in enhancing facial muscle movement during smiling to achieve a natural smile. The patient received eight sessions of treatment using non-invasive devices, and facial assessment were conducted using three-dimensional scanner (Morpheus 3D Scanner) to evaluate facial features in both relaxed and maximum smile states pre- and post-treatment. The results demonstrated improvements in the symmetry of the lower lip height during smiling, along with increases in mouth width and volume in the upper central area. Subjectively, the patient also reported enhanced comfort while smiling. These findings suggest that non-invasive procedures can effectively improve unnatural smiles following orthognathic surgery, contributing to a more aesthetically pleasing smile presentation.