The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

Volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) are common organic compounds in indoor and outdoor air, and enter the human body primarily through the respiratory tract and directly damage the respiratory system. Previous studies have suggested that exposure to VOCs/SVOCs may associate with the prevalence, incidence, and progression of asthma, but the extent of the associations is still vague. Furthermore, biomarkers for efficient and simple asthma diagnosis, typing, and attack prediction remain unclear at this stage. From the perspective of the collection and detection methods of VOCs/SVOCs, this paper summarized the epidemiological associations and underlying biological mechanisms between VOCs/SVOCs exposure and the prevalence, incidence, and progression of asthma in children/adults. It also demonstrated the application of VOCs/SVOCs in recent years in assisting asthma diagnosis, such as distinguishing asthma patients from the healthy population, differentiating different asthma phenotypes, and predicting asthma acute exacerbations, aiming to provide a scientific basis for improving current asthma management.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Objective:To explore effect of miR-125a-3p on skin injury and inflammatory response in psoriasis rats and its mechanism.Methods:SD rats were randomly divided into control group,psoriasis group,miR-NC group and miR-125a-3p group.Psoriasis Area and Severity Index(PASI)score and Baker score were measured on rats;levels of IL-6,IL-1β and TNF-α in rat skin tissue were detected by ELISA;qRT-PCR was used to detect miR-125a-3p expression;mRNA and protein expressions of forkhead box protein M1(FOXM1)in rat skin tissue were detected by qRT-PCR and Western blot.Keratinocytes from psoriasis rats were isolated and cultured,targeting relationship between miR-125a-3p and FOXM1 was verified by dual-luciferase reporter gene assay.Inhibiting or overexpressing miR-125a-3p and FOXM1 was overexpressed on basis of overexpressing miR-125a-3p,respectively.miR-125a-3p,FOXM1 mRNA and protein expressions in cells and IL-6,IL-1β,TNF-α levels in cell culture supernatants were detected,CCK-8 method was applied to detect cell viability,and flow cytometry was used to detect apoptosis rate.Results:Compared with control group,miR-125a-3p expression in skin tissue of rats in psoriasis group was decreased,PASI score,Baker score,IL-6,IL-1β,TNF-α levels,and FOXM1 mRNA and protein expressions were increased(P<0.05);compared with psoriasis group and miR-NC group,expression of miR-125a-3p in skin tissue of rats in miR-125a-3p group was increased,PASI score,Baker score,IL-6,IL-1β,TNF-α levels,and expressions of FOXM1 mRNA and protein were decreased(P<0.05).There was a targeting relationship between miR-125a-3p and FOXM1.After inhibiting miR-125a-3p expression,FOXM1 mRNA and protein expressions in cells,cell viability and IL-6,IL-1β,TNF-α levels in cell culture supernatant were increased,and apoptosis rate was decreased(P<0.05),while overexpression of miR-125a-3p had opposite effect.Overexpression of FOXM1 attenuated effects of overexpression of miR-125a-3p on cell viability,apopto-sis rate and inflammatory response.Conclusion:miR-125a-3p is lowly expressed in skin lesions of psoriasis rats,whose overexpression may inhibit proliferation of keratinocytes and promote apoptosis by targeting FOXM1,improve skin injury and reduce inflammatory response in psoriasis rats.

Objective:To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.Methods:The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-Ⅳ v2.0 (MIMIC-Ⅳ v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA).Results:A total of 3?295 patients with septic shock were included, with 2?164 surviving and 1?131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2?307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95% CI) was 0.795-0.849], 0.823 (95% CI was 0.796-0.849), and 0.810 (95% CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95% CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95% CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca 2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score Ⅲ (SAPSⅢ), the highest WBC, acute physiology score Ⅲ (APSⅢ), the lowest Na +, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95% CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APSⅢ score, SAPSⅢ score, and sequential organ failure assessment (SOFA) score] with AUC (95% CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. Conclusions:The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca 2+, Hb, WBC, SAPSⅢ score, APSⅢ score, Na +, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

Objective:This study investigates the influence of the apolipoprotein E ε4(APOE ε4)genotype on the relationship between glucose-lipid metabolism disorders and diabetes-related cognitive impairment(DCI).Methods:A case-control study was conducted involving 891 patients with type 2 diabetes mellitus(T2DM)with a mean age of(62.1±13.8)years, all of whom underwent elective surgery at the First Hospital of Shanxi Medical University between January 2017 and December 2022.Among these participants, 229 were diagnosed with DCI(case group), while 662 were cognitively normal(control group).Routine clinical information was collected, and peripheral venous blood samples were analyzed for glycated hemoglobin(HbA1c)and blood lipid levels.The single nucleotide polymorphisms rs429358 and rs7412 were analyzed to determine the presence of the APOE ε4 genotype.Stepwise Logistic regression was employed to identify independent risk factors for DCI, and subgroup analyses were performed to evaluate the effect of the APOE ε4 genotype on the relationship between HbA1c and blood lipid levels in relation to DCI risk. Results:Among all patients, female gender( OR=1.915, 95% CI: 1.393-2.631, P<0.001), longer duration of T2DM( OR=1.169, 95% CI: 1.087-1.257, P<0.001), elevated triglycerides( OR=1.161, 95% CI: 1.041-1.294, P=0.007), and being an APOE ε4 carrier( OR=1.638, 95% CI: 1.115-2.405, P=0.012)were identified as independent risk factors for developing DCI.High levels of low-density lipoprotein(LDL)were found to be independently associated with an increased risk of DCI specifically in APOE ε4 carriers( OR=1.408, 95% CI: 1.060-1.870, P=0.018), but not in non-APOE ε4 carriers( P>0.05).In contrast, elevated HbA1c was independently associated with a higher risk of DCI in non-APOE ε4 carriers( OR=1.220, 95% CI: 1.040-1.430, P=0.014), but not in APOE ε4 carriers( P>0.05).Additionally, elevated triglycerides were independently linked to an increased risk of DCI across the entire sample and within each APOE ε4 genotype subgroup. Conclusions:The APOE genotype plays a significant role in modulating the relationship between dyslipidemia and the risk of developing DCI.This highlights the critical importance of lipid metabolism disorders and APOE risk genes in both the development and progression of DCI.These findings offer valuable insights for future clinical and mechanistic studies focused on DCI.

Objective:To analyze the clinical and imaging features of patients with COVID-19-related osmotic demyelination syndrome (ODS).Methods:COVID-19-related ODS cases diagnosed in the Department of Neurology, Peking Union Medical College Hospital from January 2020 to September 2023 were retrospectively reviewed. And their past medical history, possible triggers, clinical manifestations, imaging manifestations, treatment and prognosis were summarized.Results:A total of 5 patients with COVID-19-related ODS were included. Electrolyte disturbances acted as an inducement of ODS in all patients (5/5),4 of whom with hyponatremia. Four of 5 patients first presented with disturbance of consciousness, followed by predominant dystonia. Imaging of all patients (5/5) showed isolated extrapontine myelinolysis (EPM). With the prolongation of the course of disease, such signal intensity could return to normal, and lesions showed atrophic changes in some patients. The patients′ clinical symptoms were partly relieved within a few days to a few months after treatment.Conclusions:COVID-19-related ODS is mostly associated with hyponatremia, and EPM is more common. COVID-19 should be considered as a risk factor for ODS.

China prospective multicenter birth cohort (Prospective Omics Health Atlas birth cohort, POHA birth cohort) study was officially launched in 2022. This study, in collaboration with 12 participating units, aims to establish a high-quality, multidimensional cohort comprising 20 000 naturally conceived families and assisted reproductive families. The study involves long-term follow-up of parents and offspring, with corresponding biological samples collected at key time points. Through multi-omics testing and analysis, the study aims to conduct multi-omics big data research across the entire maternal and infant life cycle. The goal is to identify new biomarkers for maternal and infant diseases and provide scientific evidence for risk prediction related to maternal diseases and neonatal health.

Clinical data of 15 primary central nervous system lymphoma (PCNSL) children aged ≤18 years admitted to our hospital between May 2013 to May 2023 were retrospectively analyzed. Our goal was to summarize the clinical features of children and investigate the therapeutic effect of a high-dose methotrexate (HD-MTX) based chemotherapy regimen on this disease. The male-to-female ratio was 2.7∶1, and the median age was 7.2 (2.3-16.4) years at diagnosis. The initial clinical symptoms were primarily cranial hypertension, with imaging findings revealing multiple lesions. Pediatric PCNSL with normal immune function has a favorable prognosis with HD-MTX-based chemotherapy. Patients with a stable disease can be treated with minimal or no maintenance. HD-MTX-based chemotherapy remains effective when the disease progresses or recurs after an initial course of non-HD-MTX-based chemotherapy.

Objective:To evaluate the efficacy and safety of hypomethylating agents (HMA) in patients with myelodysplastic syndromes (MDS) .Methods:A total of 409 MDS patients from 45 hospitals in Zhejiang province who received at least four consecutive cycles of HMA monotherapy as initial therapy were enrolled to evaluate the efficacy and safety of HMA. Mann-Whitney U or Chi-square tests were used to compare the differences in the clinical data. Logistic regression and Cox regression were used to analyze the factors affecting efficacy and survival. Kaplan-Meier was used for survival analysis. Results:Patients received HMA treatment for a median of 6 cycles (range, 4-25 cycles) . The complete remission (CR) rate was 33.98% and the overall response rate (ORR) was 77.02%. Multivariate analysis revealed that complex karyotype ( P=0.02, OR=0.39, 95% CI 0.18-0.84) was an independent favorable factor for CR rate. TP53 mutation ( P=0.02, OR=0.22, 95% CI 0.06-0.77) was a predictive factor for a higher ORR. The median OS for the HMA-treated patients was 25.67 (95% CI 21.14-30.19) months. HMA response ( P=0.036, HR=0.47, 95% CI 0.23-0.95) was an independent favorable prognostic factor, whereas complex karyotype ( P=0.024, HR=2.14, 95% CI 1.10-4.15) , leukemia transformation ( P<0.001, HR=2.839, 95% CI 1.64-4.92) , and TP53 mutation ( P=0.012, HR=2.19, 95% CI 1.19-4.07) were independent adverse prognostic factors. There was no significant difference in efficacy and survival between the reduced and standard doses of HMA. The CR rate and ORR of MDS patients treated with decitabine and azacitidine were not significantly different. The median OS of patients treated with decitabine was longer compared with that of patients treated with azacitidine (29.53 months vs 20.17 months, P=0.007) . The incidence of bone marrow suppression and pneumonia in the decitabine group was higher compared with that in the azacitidine group. Conclusion:Continuous and regular use of appropriate doses of hypomethylating agents may benefit MDS patients to the greatest extent if it is tolerated.