Gorham-Stout disease(GSD) is a rare osteolytic disorder characterized by spontaneous and progressive osteolysis, along with abnormal angiogenesis and lymphangiogenesis, with no new bone formation. We present a case of a 15-year-old female admitted due to " recurrent right leg pain for 5 years, 11 months after undergoing right femoral fracture surgery". Through comprehensive integration of the patient's clinical phenotype, laboratory tests, imaging findings, pathological examinations, and molecular biological test results, GSD was considered highly likely. A multidisciplinary treatment approach was conducted, including a combination of zoledronic acid and sirolimus to inhibit osteolysis, along with rehabilitation training and orthopedic intervention, providing a personalized and comprehensive treatment strategy.

Case 1 was a 7-year-old girl; Case 2 was her 3-year-old younger brother. Both children developed pink urine shortly after birth and exhibited blistering on photo-exposed areas (face and hands), followed by ulceration, crusting, scarring, and joint contractures leading to impaired mobility. Genetic testing in both patients identified a homozygous variant in the UROS gene, c.776T>C (p.Leu259Pro), confirming autosomal recessive congenital erythropoietic porphyria due to UROS mutations. This case report highlights that congenital erythropoietic porphyria should be considered in infants and young children with unexplained hemolytic anemia, pink urine, and severe photosensitive dermatitis. Early genetic testing is recommended to facilitate timely intervention and improve outcomes.
Humans ; Porphyria, Erythropoietic/genetics* ; Female ; Child ; Child, Preschool ; Male ; Siblings ; Mutation ; Uroporphyrinogen III Synthetase/genetics*

OBJECTIVE: To study the clinical and laboratory characteristics of monoclonal gammopathy anemia and explore the risk factors associated with anemia in monoclonal gammopathy. METHODS: A retrospective analysis was conducted on 5 539 patients who underwent immunofixation electrophoresis at the First Affiliated Hospital of Chengdu Medical College from January 2016 to February 2024. A total of 351 newly diagnosed M protein positive patients were selected as the study subjects, including 270 in the anemia group and 81 in the non-anemia group. Laboratory test results were compared between the two groups, and logistic regression models were used to analyze the risk factors for anemia. ROC curve analysis was performed to evaluate the predictive value of risk factors for anemia in monoclonal gammopathy. RESULTS: The proportion of non-anemic patients was 23.1% (81/351), with a median age of 67(60-75) years; the proportion of anemic patients was 76.9% (270/351), with a median age of 70(63-75) years. The total protein, globulin, urea, creatinine, uric acid, β₂-microglobulin, and ceruloplasmin levels in the anemia group were higher than those in the non-anemia group ( P < 0.05), while albumin, neutrophil count, lymphocyte count, monocyte count, complement C3, complement C4, haptoglobin, and transferrin levels were lower in the non-anemia group ( P < 0.05). After adjustment, multivariate logistic regression analysis shows that elevated GLB, increased β₂-MG, decreased ANC, and reduced complement C3 were independent risk factors for anemia in monoclonal gammopathy ( P < 0.05). ROC curve analysis demonstrates that GLB, β₂-MG, ANC, and complement C3 had good predictive value for anemia associated with monoclonal gammopathy. CONCLUSION Elevated GLB, increased β₂-MG, decreased ANC, and reduced complement C3 are independent risk factors for anemia in monoclonal gammopathy (P < 0.05). The combined assessment of these four factors has good predictive value for anemia in monoclonal gammopathy.
Humans ; Retrospective Studies ; Anemia/complications* ; Aged ; Middle Aged ; Paraproteinemias/diagnosis* ; Risk Factors ; Male ; Female ; Logistic Models ; ROC Curve ; Complement C3

OBJECTIVE: To evaluate the dynamic changes of glucocorticoid (GC) dose and the feasibility of GC discontinuation in rheumatoid arthritis (RA) patients under the background of Chinese medicine (CM). METHODS: This multicenter retrospective cohort study included 1,196 RA patients enrolled in the China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN) from September 1, 2019 to December 4, 2023, who initiated GC therapy. Participants were divided into the Western medicine (WM) and integrative medicine (IM, combination of CM and WM) groups based on medication regimen. Follow-up was performed at least every 3 months to assess dynamic changes in GC dose. Changes in GC dose were analyzed by generalized estimator equation, the probability of GC discontinuation was assessed using Kaplan-Meier curve, and predictors of GC discontinuation were analyzed by Cox regression. Patients with <12 months of follow-up were excluded for the sensitivity analysis. RESULTS: Among 1,196 patients (85.4% female; median age 56.4 years), 880 (73.6%) received IM. Over a median 12-month follow-up, 34.3% (410 cases) discontinued GC, with significantly higher rates in the IM group (40.8% vs. 16.1% in WM; P<0.05). GC dose declined progressively, with IM patients demonstrating faster reductions (median 3.75 mg vs. 5.00 mg in WM at 12 months; P<0.05). Multivariate Cox analysis identified age <60 years [P<0.001, hazard ratios (HR)=2.142, 95% confidence interval (CI): 1.523-3.012], IM therapy (P=0.001, HR=2.175, 95% CI: 1.369-3.456), baseline GC dose ⩽7.5 mg (P=0.003, HR=1.637, 95% CI: 1.177-2.275), and absence of non-steroidal anti-inflammatory drugs use (P=0.001, HR=2.546, 95% CI: 1.432-4.527) as significant predictors of GC discontinuation. Sensitivity analysis (545 cases) confirmed these findings. CONCLUSIONS RA patients receiving CM face difficulties in following guideline-recommended GC discontinuation protocols. IM can promote GC discontinuation and is a promising strategy to reduce GC dependency in RA management. (Trial registration: ClinicalTrials.gov, No. NCT05219214).
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Arthritis, Rheumatoid/drug therapy* ; Glucocorticoids/therapeutic use* ; Medicine, Chinese Traditional ; Retrospective Studies

Objective This study aims to investigate the current status and potential profiles of emotional inhibition in stoma patients with colorectal cancer,to analyze the differences and influencing factors,and thereby provide a basis for the development of intervention strategies.Methods A convenience sample of 348 colorectal cancer patients with ostomies was recruited from colorectal surgery departments and ostomy outpatient clinics of a tertiary A hospital in Anhui Province.Data were collected using the General Information Questionnaire,Emotional Inhibition Scale,Chinese Perceived Stress Scale,10 item Conner Davidson Resilience Scale,and Family Care Index Questionnaire.Latent profile analysis was performed using Mplus 8.3,while univariate analysis and multivariate logistic regression analysis were conducted using SPSS 26.0.Results Valid questionnaires were collected from 336 participants(response rate 96.6％).Colorectal cancer patients with ostomies demonstrated a mean emotional inhibition score of 30.84±10.49.Latent profile analysis identified 3 distinct emotional inhibition patterns:high inhibition-suppressive type(31.25％),moderate inhibition-adaptive type(39.88％),and low inhibition-excessive type(28.87％).Gender,residential location,household income per capita,ostomy self-care proficiency,perceived stress levels,psychological resilience scores and family functioning status emerged as significant determinants of emotional inhibition heterogeneity among colorectal cancer patients with ostomies across distinct latent profiles(P＜0.05).Conclusion Colorectal cancer patients with ostomies exhibit significant heterogeneity in emotional inhibition profiles.Clinicians should conduct early identification of these latent classes through standardized assessments and develop tailored interventions to improve emotional inhibition outcomes in this clinical population.

Objective To explore and analyze the current situation of consumer health activation index and its influencing factors in hospitalized elderly patients with multiple chronic conditions,and to provide reference basis for clinical intervention.Methods Convenience sampling method was used to select 332 hospitalized elderly patients with multiple chronic conditions from a tertiary hospital in Hefei City from July to December 2024 as the study subjects,and general information questionnaire,Consumer Health Activation Index,Health Literacy Management Scale,Scale of the Elderly Self-neglcet(Rural),and Perceived Social Support Scale were used to investigate the current situation of consumer health activation index of hospitalized elderly patients with multiple chronic conditions.Univariate analysis and multiple linear regression analysis were employed to explore the influencing factors of consumer health activation index in patients with multiple chronic conditions.Results A total of 332 questionnaires were distributed,and 325 valid questionnaires were retrieved.The score of the consumer health activation index of hospitalized elderly patients with multiple chronic conditions was(61.68±15.12).The results of multiple linear regression showed that literacy,average monthly household income,number of chronic diseases,medication frequency,self-care ability,health literacy management,perceived social support and self-neglect were the influencing factors for the positivity of hospitalized elderly patients with multiple chronic conditions(P＜0.05),which could explain 59.9％of the variance of the consumer health activation index of hospitalized elderly patients with multiple chronic conditions.Conclusion The consumer health activation index of hospitalized elderly patients with multiple chronic conditions is at a low level.In the future,precise and systematic intervention measures for enhancing positive health status can be formulated by taking into account relevant influencing factors,in order to promote the transformation of patients from passive health to active health,and thereby facilitating positive aging.

Objective To explore the impact and threshold effect of productive engagement on the elderly patients with chronic diseases with demoralization syndrome,and to provide theoretical basis for further improving the elderly patients with chronic diseases with demoralization syndrome.Methods A total of 350 elderly patients with chronic diseases from July to December 2024 in a tertiary hospital in Hefei were selected as the research subjects by convenience sampling method,and the general information questionnaire,Productive Engagement Scale and Demoralization Scale-Ⅱ were used for investigation.Using multiple stratified regression analysis to explore the impact of participation in productive activities on dementia syndrome in elderly patients with chronic diseases,and conducting threshold effect analysis.Results 341 valid questionnaires were collected.The score of demoralization syndrome in elderly patients with chronic diseases was 14.00(11.00,17.00),and the score of productive engagement was 37.00(29.50,46.00).Stratified regression analysis showed that productive engagement could independently explain 8.7％of the variance(P＜0.05).Curve fitting and threshold effect analysis showed that the degree of demoralization syndrome was not affected when the score of productive engagement of elderly patients with chronic diseases was ≤27 points(P＞0.05),and it was decreased with the increasing of productive engagement score＞27 points(P＜0.001).Conclusion The demoralization syndrome of elderly patients with chronic diseases were in the middle level,and productive engagement is negatively correlated with demoralization syndrome.Healthcare professionals can improve the level of demoralization syndrome by encouraging elderly patients with chronic diseases to actively participate in productive engagement activities.

Objective:To investigate the effects of psychosocial factors on early puberty and its sex specific differences.Methods:Bengbu Yuhui Longitudinal Study of Children Development Cohort, consisting of 1 162 students in grades 1-3 (524 girls) and their parents, was established in April 2021 in Bengbu, Anhui Province. The parents were surveyed by questionnaire to collect the information about their children's 56 items of psychosocial factor exposures, including 5 dimensions: major life events, early life adversity, interpersonal/relational conflict, family economic status and daily life troubles. Pubertal growth and physical examinations were conducted by trained professionals at baseline, the 1 st (May 2022), 2 nd (October 2022), 3 rd (May 2023), and 4 th (October 2023) follow-up surveys. In this study, the pubertal development status data were from the 4 th follow-up examination. Fianally, the valid data on pubertal development of 1 105 children were obtained, including the data from 510 girls (46.15%). Multinomial logistic regression model was used to analyze the association between psychosocial factors and early puberty, and the final predictors of early puberty were explored by using four models [least absolute shrinkage and selection operator (LASSO), multiple logistic regression, ridge regression, and Bayesian]. Results:The detection rates of early puberty in boys and girls were 13.60% and 16.30%, respectively. The median of intra-group absolute correlation of psychosocial factor exposure (0.042-0.066) was generally higher than that of inter-group among girls (0.031-0.059), while no similar diference was found among boys. LASSO model showed that moving house, being raised by someone other than parents, sexual abuse, peer conflict, high monthly household income, and high exposure to electronics on school days were predictors of early puberty in girls, while moving house and high monthly household income were the predictors of early puberty in girls in all four models (LASSO, multiple logistic regression, ridge regression, and Bayesian). Among boys, only a transient loss from family was a predictor of early puberty.Conclusion:Early puberty in children can be predicted by psychosocial factors, and there are gender-specific differences.

ObjectiveTo characterize the seasonal patterns of influenza in Kunming City, Yunnan Province before, during, and after the COVID-19 pandemic, and provide scientific evidence for optimizing influenza prevention and control strategies. MethodsInfluenza-like illness (ILI) and etiological surveillance data for influenza from the 14^th week of 2010 to the 13^th week of 2024 in Kunming City of Yunnan Province were collected. Harmonic regression models were constructed to analyze the epidemic characteristics and seasonal patterns of influenza before (2010/2011‒2019/2020 influenza seasons), during (2020/2021‒2022/2023 influenza seasons), and after (2023/2024 influenza season) the COVID-19 pandemic. ResultsBefore the COVID-19 pandemic, influenza in Kunming City mainly exhibited an annual cyclic pattern without a significant semi-annual periodicity, peaking from December to February of the next year, with an epidemic duration of 20‒30 weeks. During the pandemic, influenza seasonality shifted, with an increase in semi-annual periodicity and an approximate one month delay in annual peaks. However, after the pandemic, the annual amplitude of influenza increased compared with that before the pandemic, and the epidemic duration extended by about one month. Although the annual peak largely reverted to the pre-pandemic levels, the annual peaks for different influenza subtypes/lineages had not fully recovered. ConclusionInfluenza seasonality in Kunming City underwent substantial alterations following the COVID-19 pandemic and has not yet fully reverted to pre-pandemic levels. Continuous surveillance on different subtypes/lineages of influenza viruses remains essential, and prevention and control strategies should be adjusted and optimized in a timely manner based on current epidemic trends.

ObjectiveThis study aims to develop and validate a novel multimodal predictive model， termed criss-cross network（CCNet）-directed graph neural network（DGNN）（CGN）， for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer， by integrating longitudinal chest computed tomography（CT） imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron， and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently， a DGNN was constructed to integrate heterogeneous features， where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally， model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss， facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set， achieving an area under the receiver operating characteristic curve（AUC） of 0.830， accuracy of 0.843， sensitivity of 0.657， specificity of 0.712， Cohen's Kappa of 0.417， and F₁ score of 0.544. Compared with unimodal baselines， the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components， substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine （TCM）-specific symptomatology led to an additional 5% improvement in AUC， underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis， it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model， by synergistically combining cross-attention encoding with directed graph-based feature integration， enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment， thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.