Intravascular large B-cell lymphoma(IVLBCL) is a rare and aggressive type of lymphoma with diverse and nonspecific clinical manifestations, often leading to misdiagnosis. This article reports a case of IVLBCL in a middle-aged male patient who initially presented with pulmonary arterial hypertension(PAH). The patient exhibited progressive hypoxemia and PAH, showing poor response to standard PAH therapy. Laboratory tests indicated a hyperinflammatory state and significantly elevated lactate dehydrogenase levels, while imaging revealed diffuse bilateral lung lesions. Random skin biopsy identified atypical B lymphocytes within subcutaneous capillaries, confirming the diagnosis of IVLBCL. Following treatment with the ZR-CHOP regimen, the patient's symptoms and laboratory parameters improved markedly. By reviewing relevant literature, this article systematically outlines the diagnostic and therapeutic process of this case, aiming to provide insights for the clinical recognition of such rare presentations.

Over the past two decades, genome-wide association study(GWAS) has identified numerous genetic variants and loci associated with heritable diseases. With the gradual maturation and saturation of GWAS methodologies, transcriptome-wide association study(TWAS) offers a novel perspective by linkinggenetic phenotypes to gene expression levels. By integrating TWAS with other multi-omics analyses, researchers can gain a deeper understanding of heritable diseases. This article provides an overview of recent groundbreaking and representative TWAS methods and tools, analyzes their strengths and limitations, and discusses future trends in TWAS development.

Objective: To evaluate the current situation of thermal environment in primary and secondary school classrooms during winter, and to analyze students thermal comfort needs, so as to provide a basis for improving classroom thermal environment. Methods: From December 16 to 26, 2024, a stratified cluster random sampling method was used to select 90 classrooms from 15 primary and secondary schools in centralized/air conditioned heating areas(Liaoning Province, Tianjin City, Shanghai City) and naturally ventilated areas(Anhui Province and Jiangxi Province)for on site environmental measurement. A questionnaire survey was conducted among 743 students. The differences between groups using the χ 2 test were compared. Based on actual measurement data, a predicted mean vote prepared percentage of dissatisfied (PMV-PPD) model for centralized/air conditioned classrooms and an adaptive model for naturally ventilated classrooms were established, and the thermal neutral temperature and comfort interval were calculated. Results: The average outdoor temperature during on site measurement was 4.00(0.20，7.00)℃. In classrooms with centralized or air conditioned heating systems, the measured average temperature was (19.33±2.59)℃, with a thermal comfort range of 20.35-25.35 ℃ and a thermal neutral temperature of 22.85 ℃. And 13.92% of students reported feeling cold, while 80.80% felt comfortable. In classrooms with natural ventilation, the measured average temperature was (12.26±1.83)℃, with a thermal neutral temperature of 19.67 ℃ and a thermal comfort range of 16.17-23.17 ℃. About 48.33% of students reported feeling cold, and 49.81 % felt comfortable.The results of univariate analysis showed that there were statistically significant differences in shoe thickness, temperature sensation, relative humidity sensation and wind speed sensation between centralized/air conditioned heating areas ( χ 2= 7.01 , 31.47, 13.57, 13.80,all P <0.05). There were also statistically significant differences in school stage for primary and secondary school students, body mass index, classroom location for seat, temperature sensation, relative humidity sensation and wind speed sensation between naturally ventilated areas ( χ 2=42.13, 11.13, 11.04, 60.39, 29.27, 38.46,all P <0.05). Conclusions There are differences in thermal environment and students subjective thermal comfort in primary and secondary schools under different ventilation modes in winter. The temperature standards for heated classrooms should be revised, and differentiated environmental regulation strategies should be adopted based on different ventilation methods to improve students health and comfort levels.

OBJECTIVE: To explore the application value of artificial intelligence (AI)-assisted chromosomal karyotype analysis in the diagnosis of prenatal chromosomal mosaicism. METHODS: A retrospective analysis was conducted on 172 pregnant women who underwent amniocentesis at the Department of Medical Genetics and Prenatal Diagnosis, the Third Affiliated Hospital of Zhengzhou University between January 2019 and December 2024. All cases whose fetuses were diagnosed with chromosomal mosaicism via karyotype analysis and stratified into two groups based on the analytical software employed: the conventional analysis group (n = 70), which utilized Leica analysis software for karyotype image recognition and cell counting; and the AI-assisted analysis group (n = 102), which utilized AI-assisted software for the same procedures. The clinical performance of AI-assisted karyotype analysis in diagnosing chromosomal mosaicism was comprehensively evaluated by comparing the types of mosaic karyotypes, distribution of mosaic ratios, and verification outcomes of different detection modalities between the two groups. This study was approved by the Medical Ethics Committee of the Third Affiliated Hospital of Zhengzhou University (Ethics No.: 2024-406-01). RESULTS: No statistically significant difference was observed in baseline characteristics (maternal age, gestational week, and indications for prenatal diagnosis) between the two groups. Regarding the detection efficacy for numerical and structural mosaicisms, no significant difference was found in the detection of numerical mosaicism. However, the conventional analysis group exhibited a significantly higher detection rate of autosomal structural mosaicism compared to the AI-assisted group (11.43% vs. 0.98%, P < 0.05). Numerical mosaicism cases were further verified using copy number variation sequencing (CNV-seq) and/or fluorescence in situ hybridization (FISH). The AI-assisted group demonstrated a significantly lower inconsistency rate (5.56% vs. 20.41%, P < 0.05) compared to the conventional group. For low-proportion (< 10%) chromosomal mosaicism, the AI-assisted group had a significantly lower detection rate (13.25% vs. 29.69%, P < 0.05). Subsequent validation of low-proportion mosaicism by CNV-seq and/or FISH showed a higher consistency rate in the AI-assisted group (81.82% vs. 54.55%), though the difference did not reach statistical significance (P = 0.360). CONCLUSION For the karyotyping analysis of prenatal chromosomal mosaicism, AI-assisted karyotype analysis shows high accuracy and consistency in identifying numerical chromosomal mosaicism, particularly in reducing the detection of low-proportion (< 10%) mosaicism while improving verification accuracy. AI-assisted analysis can significantly improve the detection accuracy of numerical mosaicism and mitigate the risk of misclassification for low-proportion (< 10%) mosaicism, thereby providing more precise clinical evidence for the prenatal diagnosis of chromosomal mosaicisms.
Humans ; Female ; Mosaicism ; Pregnancy ; Karyotyping/methods* ; Artificial Intelligence ; Prenatal Diagnosis/methods* ; Adult ; Retrospective Studies ; Chromosome Disorders/genetics* ; Amniocentesis

Objective: To understand the current status and associated factors of sleep problems among preschool children in Hainan Province, so as to provide scientific evidence for improving sleep health in this population. Methods: From January 2021 to June 2022, a total of 4 105 preschool children aged 3-6 years from 62 kindergartens in Hainan Province were selected using stratified cluster random sampling method. Demographic information and lifestyle habits were collected through the Hainan Province Child Growth and Development Survey Questionnaire. The Children s Sleep Habits Questionnaire (CSHQ) was employed to assess sleep status. Unconditional binary Logistic regression model was applied to investigate the associated factors of sleep problems among preschool children. Results: The overall CSHQ score for children was 58.03±18.84, with 80.95% of preschool children exhibiting sleep related issues. The top three most prevalent sleep problem domains were bedtime resistance (72.42%), sleep anxiety ( 54.88 %), and parasomnias (38.86%). Logistic regression analysis revealed that higher family annual income ( OR=0.60, 95%CI = 0.45-0.79), higher maternal education level ( OR=0.53, 95%CI =0.32-0.89), regular or daily vitamin D supplementation ( OR=0.77, 95%CI =0.60-0.99), and fully self initiated eating behavior ( OR=0.71, 95%CI =0.59-0.85) were negatively related with children s sleep problems; in addition, screen exposure ( OR=1.27, 95%CI =1.06-1.51) and picky eating ( OR= 1.47 , 95%CI =1.21-1.78) were positively related to children s sleep problems (all P <0.05). Conclusion The high detection rate of sleep problems among preschool children in Hainan Province is multifactorially associated with family environment, dietary habits, and lifestyle behaviors.

Background Meteorological factors are among the key extrinsic triggers for the onset and exacerbation of cardiovascular and cerebrovascular diseases (CVD). Against the backdrop of sustained global warming, elucidating the impact of ambient temperature and atmospheric pressure on CVD, especially on pre-hospital CVD emergent events, has become imperative for evidence-based prevention and emergency preparedness. Objective To quantify the temporal trends of daily mean temperature and atmospheric pressure and their associations with pre-hospital CVD emergent events in Yantai, and to explore effect modification by demographic subgroups and geographic areas, thereby providing an empirical basis for the rational allocation of emergency medical resources. Methods Pre-hospital CVD emergency data from January 1, 2016 to December 31, 2022 were selected from the Yantai 120 Emergency Medical Command System. Synchronous meteorological factors and environmental pollutant data were obtained from the websites of the National Oceanic and Atmospheric Administration and the National Centers for Environmental Information of the United States. Time-series analysis combined with distributed lag non-linear model was used to analyze the association between daily temperature, atmospheric pressure, and pre-hospital CVD emergencies. Average annual percentage changes (AAPC) were calculated using Joinpoint (version 5.2.0.0) to reflect temporal trends. Spearman correlation analysis was employed to screen variables with low collinearity for inclusion in the multi-pollutant adjusted models. Results From 2016 to 2022, a total of 268275 pre-hospital CVD emergency cases were recorded in Yantai City, showing an increasing annual trend (AAPC=7.16%, P=0.002). Emergency volume, daily temperature, atmospheric pressure, and PM₁₀ displayed marked seasonal patterns, with summer bottoms and winter peaks for emergencies, daily temperature higher in summer and lower in winter, and the inverse for atmospheric pressure. Correlation analysis showed that both daily temperature and atmospheric pressure were positively correlated with O₃, with correlation coefficients of 0.745 and 0.723, respectively; negatively correlated with PM_2.5, with correlation coefficients of −0.246 and −0.235, respectively; and negatively correlated with PM₁₀, with correlation coefficients of −0.241 and −0.229, respectively (P < 0.05). A U-shaped correlation was revealed between daily temperature and pre-hospital CVD emergency risk. Using 24 °C as the reference, risk was elevated at both low and high daily temperatures, with the strongest cold effect at –11 ℃ (RR=1.53, 95%CI: 1.35, 1.73) and strongest heat effect at 30 ℃ (RR=1.06, 95%CI: 1.01, 1.11); cold effects were prolonged (lag0–14d) compared to heat effects. Atmospheric pressure exhibited a positive association with emergencies; using a median of 1012.2 hPa as the reference, the maximum risk occurred at 1037 hPa (RR=1.25, 95%CI: 1.03, 1.52), with a 0–5 d lag. In the subgroup analysis, the impact of low temperature was more pronounced among females (RR_{−9 ℃}=1.66), people over 60 years (RR_{−9 ℃}=1.91), and rural residents (RR_{−9 ℃}=1.76). High atmospheric pressure significantly increased the risk of pre-hospital CVD emergency among females (RR_{1035 hPa}=1.54), people under 18 years (RR_{1035 hPa}=3.62), and rural residents (RR_{1035 hPa}=1.46). The sensitivity analysis results indicated that, after excluding the impact of pandemic and the degrees of freedom for time variations, the effects of daily average temperature and atmospheric pressure on pre-hospital CVD emergency volume were consistent with the primary analysis findings. Conclusion Both non-optimal temperature and atmospheric pressure significantly increase the pre-hospital CVD emergency volume; therefore, emergency medical services should develop targeted response protocols, optimize resource allocation, and intensify public health education to mitigate the pre-hospital burden attributable to temperature and pressure fluctuations.

Objective To systematically evaluate the risk prediction models for acute kidney injury in patients with acute coronary syndrome (ACS) based on machine learning, providing a reference for clinical selection of appropriate risk assessment tools. Methods Clinical studies using machine learning methods for predicting the risk of acute kidney injury in ACS patients were retrieved from PubMed, Cochrane Library, Embase, Web of Science core database, CNKI, Wanfang Database, CBM, and VIP. The retrieval time was from the establishment of the database to May 24, 2025. The quality of the models were evaluated using the prediction model risk of bias assessment tool. Results Nine articles were included, and a total of 58 prediction models were constructed using 20 machine learning methods. The area under the receiver operating characteristic curve ranged from 0.733 to 0.894. The most commonly used predictors were age and creatinine. The overall bias risk of the included studies was relatively high, but the applicability was good.Conclusion Machine learning models can identify the risk of acute kidney injury in ACS patients. All models have good predictive potential, but they are still in the development stage. It is recommended that future studies adopt prospective design with external validation to improve the stability and predictive accuracy of the models.

Objective To analyze the epidemiological characteristics of poisonous mushroom-related foodborne disease incidents in Chongqing from 2010 to 2024, and to provide a scientific basis for effective prevention and control of mushroom-related foodborne diseases. Methods Data of poisonous mushroom related foodborne disease incidents reported by the CDCs of 39 districts and counties in Chongqing from 2010 to 2024 were collected, and their epidemic characteristics were analyzed. Results A total of 264 poisonous mushroom poisoning outbreaks were reported in Chongqing from 2010 to 2024, resulting in 1 046 cases of illness, 543 hospitalizations, and 15 deaths, with a mortality rate of 1.43%. The peak of poisoning incidents was from June to October, accounting for 89.02% of the total number of incidents. The top three high-incidence areas were Pengshui Miao Tujia Autonomous County, Dianjiang County, and Nanchuan District. Poisonous mushroom poisoning incidents occurring in household settings, accidental picking and ingestion, and cases involving unidentified mushroom species accounted for 89.02%, 83.33%, and 90.15% of all foodborne disease outbreak incidents, respectively. Conclusion Foodborne disease incidents related to poisonous mushrooms in Chongqing City show an upward trend, and the effectiveness of prevention and control needs to be improved. Based on their seasonal, regional and household clustering characteristics, efforts should be made before the peak season of mushroom poisoning in high-risk areas, targeting key populations with health education, monitoring and early warning, food safety supervision, and medical treatment.

ObjectiveTo construct a group-based trajectory model (GBTM) for early medication adherence check-in, and to analyze the relationship between different trajectories and treatment outcomes in tuberculosis patients using data that were generated from smart tools for monitoring their medication adherence and check-in. MethodsFrom October 1, 2022 to September 30, 2023, a total of 163 pulmonary tuberculosis patients diagnosed in Fengxian District were selected as the study subjects. The GBTM was utilized to analyze the weekly active check-in trajectories of the subjects during the first 4 weeks and establish different trajectory groups. The χ² tests were employed to compare the differences between groups and logistic regression analysis was conducted to explore the relationship between different trajectory groups and treatment outcomes. ResultsA total of four groups were generated by GBTM analyses, of which a low level of punch card was maintained in group A, 6% of the drug users increased rapidly from a low level in group B, 17% of drug users increased gradually from a low level in group C, and 18% of drug users maintained a high level of punch card in group D. The trajectory group was divided into two groups according to homogeneity, namely the low level medication punch card group (group A) and the high level medication punch card group (group B, group C, and group D). The results of multivariate logistic regression analyses revealed that low-level medication check-in (OR=3.250, 95%CI: 1.089‒9.696), increasing age (OR=1.030, 95%CI: 1.004‒1.056), and not undergoing sputum examination at the end of the fifth month (OR=2.746, 95%CI: 1.090‒7.009) were significantly associated with poor treatment outcomes. ConclusionThe medication check-in trajectory of pulmonary tuberculosis patients within the first 4 weeks is correlated with adverse outcomes, or namely consistent low-level medication adherence check-ins are associated with poor treatment outcomes, while high-level medication adherence check-ins are associated with a lower incidence of adverse outcomes.

Objective To investigate the ultrasonographic characteristics of acute Stanford type A aortic dissection (ATAAD) involving the renal arteries and their relationship with renal function. Methods Patients with ATAAD admitted to Deyang People's Hospital from February 2013 to May 2023 were selected for the study. Based on whether the renal arteries were involved in the dissection, the patients were divided into two groups: a renal artery involvement group and a renal artery non-involvement group. General data and ultrasound characteristics of the two groups were compared. Logistic regression analysis and model correction were performed to analyze the relationship between ultrasound characteristics and renal function involvement in ATAAD patients. Receiver operating characteristic (ROC) curves were used to evaluate the predictive value of ultrasound characteristics for renal artery involvement in ATAAD patients. Additionally, patients in the renal artery involvement group were divided into normal renal function and abnormal renal function subgroups based on serum blood urea nitrogen (BUN) and serum creatinine (Scr) levels. Clinical data of the two subgroups were compared, and a log-binomial model was used to analyze the risk effects of ultrasound characteristics for abnormal renal function. Pearson correlation analysis was performed to assess the correlation between ultrasound characteristics of renal artery involvement and renal function indicators. Results A total of 163 patients were included, consisting of 106 males and 57 females, with a mean age of (50.06±10.46) years (ranging from 20 to 85 years). Significant differences in gender, Scr, and BUN were observed between the renal artery involvement group and the renal artery non-involvement group (P<0.001). Compared to the renal artery non-involvement group, the renal artery involvement group had an increased ascending aorta diameter, a greater proportion of ascending aortic dilation and poor renal perfusion (P<0.05). Logistic regression analysis indicated that ascending aorta diameter, ascending aortic dilation, and poor renal perfusion were independent factors for renal artery involvement (P<0.05). Ultrasonographic characteristics showed good predictive ability for renal artery involvement in ATAAD patients. Furthermore, the combination of the three characteristics yielded a higher predictive value for renal artery involvement. Compared to the normal renal function group, the abnormal renal function group had higher BUN and Scr levels, increased ascending aortic diameter, a greater proportion of ascending aortic dilation and poor renal perfusion (P<0.05). The log-binomial model analysis revealed that the risk ratios for ascending aortic diameter, ascending aortic dilation, and poor renal perfusion were statistically significant both before and after adjustment (P<0.05). Pearson correlation analysis revealed that ascending aortic diameter, ascending aortic dilation, and poor renal perfusion were strongly correlated with renal function parameters (P<0.05). Conclusion Ultrasound characteristics of ATAAD involving the renal arteries are associated with renal function. Ascending aorta diameter, ascending aortic dilation, and poor renal perfusion are independent risk factors for abnormal renal function.