Background With the progression of industrialization, an increasing number of emerging contaminants are entering aquatic environments, posing significant threats to the safety of drinking water. Therefore, establishing a system for identifying unknown hazardous factors and implementing safety warning mechanisms for drinking water is of paramount importance. Among these efforts, non-target screening plays a critical role, but its effectiveness is largely constrained by the scope of coverage of sample pre-treatment methods. Objective To integrate modern chromatography/mass spectrometry techniques with advanced data mining methods to develop a non-discriminatory sample pre-treatment method for comprehensive enrichment of unknown contaminants in drinking water, laying a technical foundation for the discovery and identification of unknown organic hazardous factors in drinking water. Methods A non-discriminatory pre-treatment method based on supramolecular and solid-phase extraction was developed. The final target compounds including 333 pesticides, 194 pharmaceuticals and personal care products (PPCPs), and 59 per- and polyfluoroalkyl substances (PFASs) were used for optimizing the pre-treatment method, confirming its coverage. The impacts of different eluents on the absolute recovery rates of target compounds were compared to select the conditions with the highest recovery for sample pre-treatment. The effects of different supramolecular solvents and salt concentrations on target compound recovery were also evaluated to determine the most suitable solvent and salt concentration. Results The solid-phase extraction elution solvents, supramolecular extraction solvents, and salt concentrations were optimized based on the target compound recovery rates. The optimal recovery conditions were achieved using 2 mL methanol, 2 mL methanol (containing 1% formic acid), 2 mL ethyl acetate, 2 mL dichloromethane, hexanediol supramolecular solvent, and 426 mg salt. The detection method developed based on these conditions showed a good linear relationship for all target compounds in the range of 0.1-100.0 ng·mL⁻¹, with R² > 0.99. The method’s limit of detection ranged from 0.01 ng⁻¹ to 0.95 ng⁻¹, and 95% of target compounds were recovered in the range of 20%-120%, with relative standard deviation (RSD) less than 30%, indicating good precision. Conclusion The combined pre-treatment method of solid-phase extraction and supramolecular solvent extraction can effectively enrich contaminants in drinking water across low, medium, and high polarities, enabling broad-spectrum enrichment of diverse trace contaminants in drinking water. It provides technical support for broad-spectrum, high-throughput screening and identification of organic pollutants in drinking water, and also serves as a reference for establishing urban drinking water public safety warning systems.

INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers age, sex and cardiac troponin I (TnI) results to risk-stratify patients for type 1 myocardial infarction. METHOD: Patients aged ≥25 years who presented to the emergency department (ED) of Singapore General Hospital with symptoms suggestive of acute coronary syndrome with no diagnostic 12-lead electrocardiogram (ECG) changes were included. Participants had serial ECGs and high-sensitivity troponin assays performed at 0, 2 and 7 hours. The primary outcome was the adjudicated diagnosis of type 1 myocardial infarction at 30 days. We compared the performance of MI3 in predicting the primary outcome with the European Society of Cardiology (ESC) 0/2-hour algorithm as well as the 99th percentile upper reference limit (URL) for TnI. RESULTS: There were 1351 patients included (66.7% male, mean age 56 years), 902 (66.8%) of whom had only 0-hour troponin results and 449 (33.2%) with serial (both 0 and 2-hour) troponin results available. MI3 ruled out type 1 myocardial infarction with a higher sensitivity (98.9, 95% confidence interval [CI] 93.4-99.9%) and similar negative predictive value (NPV) 99.8% (95% CI 98.6-100%) as compared to the ESC strategy. The 99th percentile cut-off strategy had the lowest sensitivity, specificity, positive predictive value and NPV. CONCLUSION The MI3 algorithm was accurate in risk stratifying ED patients for myocardial infarction. The 99th percentile URL cut-off was the least accurate in ruling in and out myocardial infarction compared to the other strategies.
Humans ; Male ; Female ; Emergency Service, Hospital ; Middle Aged ; Electrocardiography ; Machine Learning ; Singapore ; Chest Pain/blood* ; Troponin I/blood* ; Myocardial Infarction/blood* ; Risk Assessment/methods* ; Aged ; Algorithms ; Acute Coronary Syndrome/blood* ; Adult ; Sensitivity and Specificity

Research on IgG4-related disease (IgG4-RD), an autoimmune condition recognized to be a unique disease entity only two decades ago, has processed from describing patients' symptoms and signs to summarizing its critical pathological features, and further to investigating key pathogenic mechanisms. Challenges in gaining a better understanding of the disease, however, stem from its relative rarity-potentially attributed to underrecognition-and the absence of ideal experimental animal models. Recently, with the development of various high-throughput techniques, "omics" studies at different levels (particularly the single-cell omics) have shown promise in providing detailed molecular features of IgG4-RD. While, the application of omics approaches in IgG4-RD is still at an early stage. In this paper, we review the current progress of omics research in IgG4-RD and discuss the value of machine learning methods in analyzing the data with high dimensionality.
Humans ; Immunoglobulin G4-Related Disease/metabolism* ; Immunoglobulin G/metabolism* ; Machine Learning ; Animals ; Proteomics/methods*

AIM:To explore the relationship between the amplitude of diurnal variations in the choroidal vascular index(CVI)and annual changes in spherical equivalent(SE)and axial length in school-aged children.METHODS: Prospective study. Totally 39 cases(39 eyes)of Chinese school-age children aged 7 to 12 that diagnosed as emmetropia and myopia and error of refraction at optometry clinics of Eye Hospital, Wenzhou Medical University from July to August 2021 were selected. While 33 cases(33 eyes)were finally included, with 6 cases of loss to 1 a follow-up. A total of 16 cases(16 eyes)with annual growth of SE<0.5 D and 17 cases(17 eyes)with annual growth of SE≥0.5 D were divided into a non-progression group and progression group, respectively. Swept-source optical coherence tomography(SS-OCT)and custom choroidal analysis software were used to longitudinally observe diurnal variations in CVI of subjects, and the association between CVI diurnal amplitude and annual changes in SE and axial length was analyzed.RESULTS:It showed no significant correlation between the CVI diurnal amplitude at 1 mm from the fovea and annual changes in SE of the non-progressive group(P=0.65), while in the progression group, the CVI diurnal amplitude at 1 mm from the fovea was negatively correlated with annual changes in SE(P=0.048). However, no significant correlation was identified between CVI diurnal amplitude and annual changes in axial length in either group(all P>0.05). The diurnal amplitude of the CVI at the 1 mm foveal center had an effect on annual SE progression(P=0.039). Conversely, the diurnal amplitude of axial length, the annual changes in axial length, and the maximum or minimum time of CVI demonstrated significant associations with SE progression(all P> 0.05).CONCLUSION: Diurnal variations in CVI amplitude are associated with SE progression in school-aged children, providing a basis for further understanding the choroid-related changes in the process of myopia onset and progression.

Objective:To optimize the image quality of PET/CT following 90Y-selective internal radiation therapy ( 90Y-SIRT) using block-sequential regularized expectation maximization (BSREM) reconstruction algorithm, and to evaluate its impact of different β values on image quality and quantitative analysis. Methods:A retrospective study was conducted on 8 male patients with hepatic tumors (age: 62(52, 71) years) treated at Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine Tsinghua University, between June 2024 and January 2025. All patients were treated with 90Y resin microspheres (2.6(0.9, 3.6)GBq) and underwent post-treatment 90Y PET/CT liver imaging. Imaging data were reconstructed using BSREM with different noise penalty weighting factors ( β values: 0, 300, 1000, 1500, 2500, 3500, 4000, 6000, 8000, 10000). Visual assessment was independently performed by two nuclear medicine physicians, using a 4-point scale (1=worst, 4=best). The mean score was considered as the final score. The consistency of the 2 reviewers was calculated and analyzed by Kappa test. Visual scores of different β value groups were compared by Friedman test. The β value demonstrating highest mean score and optimal consistency was selected as the optimal. Quantitative analysis was performed using MIM software to calculate the maximum absorbed dose ( Dmax) and the mean absorbed dose ( Dmean) for tumor, normal liver, and whole liver regions, and the CV was used to evaluate the impact of β values. Results:The visual assessment consistency of reviewers in 3 β value groups (0, 3500, 6000) were the highest (7/8) (all kappa=0.88, all P<0.05). Visual scores of the 10 β value groups were significantly different ( χ2=28.74, P<0.001), and the visual scores of 2 β value groups (3500, 4000) were the highest, both of which were 4.0(4.0, 4.0). Overall, visual assessment identified β=3500 as the optimal. Quantitative analysis revealed that, (1) Dmax in all regions (tumor, normal liver, whole liver) decreased with the increasing β values, stabilizing when β>1000 ( CV 56%-67%); (2) Dmean remained stable across different β values ( CV 0.04%-5.00%). Conclusions:In BSREM reconstruction, β=3500 is the optimal parameter for improving 90Y-PET image quality. β values significantly affect Dmax (stabilizing at β > 1000), but have no significant impact on Dmean, suggesting that reconstruction parameters primarily influence dose distribution morphology rather than average dose assessments.

Objective To analyze the prescription patterns of Professor Huang Feng,a nationally renowned traditional Chinese medicine(TCM)practitioner,in treating osteomyelitis using data mining methods.Methods Prescription data from effective medical records of osteomyelitis treated by Professor Huang Feng between January 2018 and December 2022 were collected and screened.Microsoft Excel,SPSS Modeler 18.0,and SPSS Statistics 25 were used to analyze the frequency and the distribution of properties,flavors,and meridian tropism of prescribed medications,along with association rule analysis and cluster analysis of high-frequency drugs.Results A total of 137 prescriptions involving 86 Chinese medicinals were included.Eighteen high-frequency medicinals(frequency＞30 times)were identified,namely Glycyrrhizae Radix et Rhizoma,Astragali Radix,Coicis Semen,Angelicae Sinensis Radix,Smilacis Glabrae Rhizoma,Achyranthis Bidentatae Radix,Bletillae Rhizoma,Rehmanniae Radix,Paeoniae Radix Alba,Dendrobii Caulis,Polygalae Radix,Lablab Semen Album,Corydalis Rhizoma,Angelicae Dahuricae Radix,Drynariae Rhizoma,Sanguisorbae Radix,Poria,and Mume Fructus.Most of the prescribed medicinals were neutral in nature,sweet,bitter,and pungent in flavor,and had the meridian tropism of liver,spleen,and lung meridians.Association rule analysis yielded 67 drug association rules,and the high-support combinations were the drug combinations of Astragali Radix respectively with Coicis Semen,Angelicae Sinensis Radix,Smilacis Glabrae Rhizoma and Achyranthis Bidentatae Radix,reflecting the compatibility principles of supplementing and invigorating qi-blood,activating blood circulation to resolve stasis,and draining dampness to remove toxins.Cluster analysis revealed three core clusters:Cluster 1 consisted of Glycyrrhizae Radix et Rhizoma,Astragali Radix,Coicis Semen,Smilacis Glabrae Rhizoma,Angelicae Sinensis Radix,Bletillae Rhizoma,Paeoniae Radix Alba,Angelicae Dahuricae Radix,Mume Fructus,Polygalae Radix and Sanguisorbae Radix;Cluster 2 consisted of Rehmanniae Radix and Dendrobii Caulis;Cluster 3 consisted of Achyranthis Bidentatae Radix,Lablab Semen Album,Corydalis Rhizoma and Poria.Conclusion For the treatment of osteomyelitis,Professor Huang Feng follows the principle of combining supporting healthy qi with eliminating pathogens,focuses on clearing damp-heat and pathogenic toxins accompanied by activating blood circulation to resolve stasis,and lays stress on adaptation to local condition and activating spleen-stomach to reinforce vital qi.

This case report describes a 68-year-old male patient diagnosed with primary hepatocellular carcinoma (HCC). After receiving Yttrium-90 microsphere selective internal radiation therapy (⁹⁰Y-SIRT), the tumor significantly reduced in size, and tumor markers alpha fetoprotein (AFP) and abnormal prothrombin (PIVKA-Ⅱ) decreased. Postoperative pathological results showed minimal residual tumor cells, indicating that ⁹⁰Y-SIRT has good efficacy and safety in downstaging and conversion of HCC, thereby facilitating subsequent surgical resection.

The complement component 4 binding protein alpha(C4BPA)gene is the alpha chain of complement binding protein 4.As a plasma protein involved in the complement and coagulation systems,it can influence immune responses and lipid metabolism.In order to study the polymor-phism of C4BPA gene and its correlation with milk quality traits in Chinese Holstein cows,genom-ic DNA was extracted from blood samples of 92 Chinese Holstein cows,and the target fragment of C4BPA gene was amplified by PCR,and the association analysis was performed by using direct se-quencing to obtain the SNP loci and milk quality traits.The results showed that among the four SNPs found at the third intron of the C4BPA gene,I3-11 G＞A was highly significantly correlated with milk protein and urea nitrogen(P＜0.05),I3-291 T＞G was significantly correlated with lac-tose(P＜0.05),I3-374 C＞T was highly significantly correlated with lactose and urea nitrogen(P＜0.05),and I3-375 T＞G was highly significantly correlated with lactose(P＜0.05),milk pro-tein and urea nitrogen.The chi-square test values for each point indicated that the population was in genetic equilibrium.Individuals of haplotype combination H1 H1 had the highest lactose content,and haplotype combination H1H2 can be used as the best haplotype combination in the molecular selection work of dairy cows.

Objective This study aimed to investigate the relationship between multiple inflammatory markers and poor outcome in patients with intracerebral hemorrhage,and to compare their predictive abilities.Methods We retrospectively analyzed the patients with intracerebral hemorrhage admitted to the Department of Neurology of the First Affiliated Hospital of Nanchang University from January 1,2015 to March 31,2023.According to the Modified Rankin Scale at 90 days after onset,the patients were divided into good outcome(mRS≤2 points)and poor outcome(mRS score≥3 points).Clinical information,laboratory examinations and follow-up data were compared between the two groups.Inflammatory markers include neutrophil to lymphocyte ratio(NLR),platelet to lymphocyte ratio(PLR),monocyte to high-density lipoprotein ratio(MHR),systemic inflammatory response index(SIRI),systemic immune-inflammation index(SII),white blood cell count to mean platelet volume ratio(WMR),lymphocyte to monocyte ratio(LMR),and systemic coagulation-inflammation index(SCI).Univariate and multivariate logistic regression analysis were used to analyze the predictors of poor prognosis after intracerebral hemorrhage,and receiver operating characteristic curve(ROC)was constructed to compare their predictive ability.Results A total of 510 patients with intracerebral hemorrhage were included.Of those,297(58.2%)had good outcome,and 213(41.8%)had poor outcome.Comparison of baseline characteristics demonstrated that patients with poor prognosis had higher levels of white blood cells,neutrophils,high-density lipoprotein,fibrinogen,NLR,PLR,SIRI,SII,WMR at admission,larger baseline hematoma volume and baseline perihematomal edema volume,a higher proportion of lobar hemorrhage,older age,and lower levels of platelets,lymphocytes,LMR,and SCI(P<0.05).Multivariate regression analysis showed that NLR(OR:1.081,95%CI:1.032～1.1131,P=0.001),SIRI(OR:1.089,95%CI:1.014～1.169,P=0.019),SII(OR:1.000,95%CI:1.000～1.001,P=0.011),WMR(OR:2.627,95%CI:1.267～5.445,P=0.009)were independently associated with poor prognosis in patients with ICH.In ROC analysis,the area under the curve of NLR(0.729,95%CI:0.685～0.774)was higher than SIRI(0.692,95%CI:0.645～0.738),SII(0.688,95%CI:0.641～0.735)and WMR(0.65,95%CI:0.602～0.698)for predicting poor outcomes.Conclusion NLR,SIRI,SII and WMR at admission are independently associated with poor outcomes in patients with intracerebral hemorrhage,and NLR has the strongest predictive ability.

Obesity is a common chronic disease associated with an increased risk of multiple comorbidities and premature mortality. Tirzepatide, a glucose-dependent insulinotropic polypeptide (GIP)/glucagon-like peptide 1 (GLP-1) receptor agonist, has demonstrated significant efficacy in multiple studies in reducing body weight and fat mass, improving body composition and metabolic parameters, lowering the risk of obesity-related complications and improving health and quality of life. This review summarizes the underlying mechanisms of tirzepatide and recent advances in its clinical research, providing a reference for clinical practice.