ObjectiveTo investigate the association between single and combined exposure to arsenic （As）， mercury （Hg）， and cadmium （Cd） in urine and hearing loss in adults. MethodsA total of 400 adult patients were recruited for the study， comprising 200 participants in the normal hearing group and 200 in the hearing loss group. General demographic information of the study subjects was collected by a self-administered questionnaire， morning urine samples were tested for As， Hg， and Cd concentrations， and hearing status was assessed by Pure Tone Audiometry. A binary Logistic regression model was used to explore the association between the concentrations of the three metals in urine and hearing loss， and a Bayesian kernel-machine regression （BKMR） model was used to further explore the association between the concentrations of the three metal mixtures and hearing loss. ResultsAfter correction for urinary creatinine， As， Hg， and Cd concentrations were higher in the hearing loss group than those in the normal hearing group （P<0.05）. Binary Logistic regression indicated that concentrations of As， Hg， and Cd were positively associated with hearing loss， and the risk of hearing loss was significantly higher in the second quartile of Cd concentration compared to the first quartile （OR=2.000， 95%CI： 1.009-3.964）. BKMR modeling analysis showed that the combined exposure levels of As， Hg， and Cd significantly increased the risk of hearing loss， with Cd being the most important contributor （posterior inclusion probability=0.412）， and that there were potential interactions between As and Cd， and Cd and Hg. ConclusionCombined exposure to As， Hg， and Cd in urine is significantly associated with adult hearing loss， with Cd being the primary contributor in the exposure combination. The interactions between As and Cd， as well as Cd and Hg， may have adverse effects on hearing loss.

Objective:To explore the impact and predictive value of subclinical hypothyroidism on metabolic associated fatty liver disease (MAFLD).Methods:The study adopted a cross-sectional design. A total of 6 597 adults with subclinical hypothyroidism who received health check-ups in the Health Examination Center of Hebei General Hospital from 2018 to 2022 were selected as subjects. The participants were randomly divided into a modeling group (4 617) and a validation group (1 980) in a 7∶3 ratio. The basic information and serological indicators were collected in those subjects. The general clinical characteristics of the MAFLD and non-MAFLD participants were compared in the modeling group. Multivariate logistic regression analysis was used to explore the impact of thyroid hormone on MAFLD. The R language was applied to construct a nomogram prediction model, and internal validation and external validation of the model were performed to evaluate the prediction performance of the model.Results:Of the 6 597 study subjects, the prevalence of MAFLD was 31.14% (2 054/6 597). After preliminary comparison of baseline data from the modeling group, multivariate analysis showed that free triiodothyronine/free thyroxine (FT3/FT4) ( OR=20.945,95% CI: 5.862-74.838), male ( OR=1.394,95% CI: 1.109-1.752), age ( OR=1.017,95% CI: 1.009-1.025), diastolic blood pressure ( OR=1.017,95% CI: 1.004-1.021), waist circumference ( OR=1.049,95% CI:1.031-1.067), body mass index ( OR=1.236,95% CI:1.175-1.299), triglyceride ( OR=1.525,95% CI: 1.379-1.687), low density lipoprotein cholesterol ( OR=1.493,95% CI: 1.313-1.698), fasting glucose ( OR=1.173,95% CI:1.101-1.249) were risk factors for MAFLD, and high density lipoprotein cholesterol ( OR=0.243,95% CI: 0.163-0.363), aspartate aminotransferase/alanine aminotransferase ( OR=0.242,95% CI: 0.183-0.320) were protective factors for MAFLD (all P<0.05). The results showed that the two groups of calibration curves showed the consistency between MAFLD occurrence risk and actual occurrence risk. The AUCs of internal and external validation was 0.897 and 0.907, respectively. This nomogram model had a high predictive value. Conclusion:FT3/FT4 is an independent risk factor for MAFLD in the population with subclinical hypothyroidism. The prediction model incorporating thyroid function and metabolic parameters may provide clinical value for early identification of MAFLD.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the relationship between the expressions of miR-217 and miR-4286 in gastric cancer tissues and gastric cancer complicated with Helicobacter pylori(HP)infection,as well as their predictive value for prognosis.Methods:A total of 225 patients who underwent radical gastrectomy for gastric cancer in the First Af-filiated Hospital of Xingtai Medical College from January 2021 to February 2023 were selected and divided into the HP+group and the HP-group according to whether they were complicated with HP infection.The expressions of miR-217 and miR-4286 in gastric cancer tissues were detected by real-time fluorescence quantitative PCR,the rela-tionship between the expressions of miR-217 and miR-4286 and HP infection in patients with gastric cancer was evaluated by Spearman correlation analysis.Regular outpatient reexaminations were conducted for a 2-year follow-up.Gastric cancer patients with recurrence or all-cause death during the follow-up period were included in the poor prognosis group,and the rest were included in the good prognosis group.Univariate and multivariate Logistic regres-sion analysis methods were used to screen the influencing factors of gastric cancer prognosis,the ROC curve was used to evaluate the predictive value of miR-217 and miR-4286 expressions for the prognosis of gastric cancer.Re-sults:Compared with the HP-group,the expressions of miR-217 and miR-4286 in the HP+group were up-regulated(P<0.05),and the expressions of miR-217 and miR-4286 were positively correlated with gastric cancer pa-tients with HP infection(r=0.652,0.485,P<0.05).Compared with the good prognosis group,the proportion of TNM stage Ⅲ,lymph node metastasis and HP infection in the poor prognosis group was higher,and the expressions of miR-217 and miR-4286 were up-regulated,with statistical significance(P<0.05).The results of multivariate Logistic regression analysis showed that TNM stage Ⅲ(OR=1.425),lymph node metastasis(OR=1.548),HP infection(OR=1.465),high expression of miR-217(OR=1.608),and high expression of miR-4286(OR=1.544)are the influencing fac-tors for poor prognosis of gastric cancer(P<0.05).The ROC curve showed that the AUC of the combined prediction of miR-217 and miR-4286 in gastric cancer tissues for poor prognosis in patients with gastric cancer was 0.922,which was higher than that of miR-217(0.817)and miR-4286(0.806)alone(Z=6.527,7.025;both P<0.05).Conclu-sion:The expressions of miR-217 and miR-4286 in gastric cancer tissues are closely related to HP infection in gas-tric cancer patients,and the combination of Mir-217 and Mir-4286 is effective in predicting the poor prognosis after radical gastritis.

A molecularly imprinted electrochemical sensor for rapid detection of tenuazonic acid(TeA)was developed based on the Au-MoS2/MOF(Fe2+/Fe3+)high-efficiency catalytic cycle amplification strategy,using p-aminobenzoic acid(PABA)as the functional monomer,and TeA as the template molecule.The molecularly imprinted polymer(MIP)was prepared on the surface of Au-MoS2/MOF(Fe2+/Fe3+)modified electrode through electropolymerization.By introducing flower-like MoS2 nanoflakes(MoS2 NFs)as a co-catalyst into a mixed-valence structured Fe-MOF(Fe2+/Fe3+),the H2O2 electrochemical signal of the MIP/Au-MoS2/MOF(Fe2+/Fe3+)/GCE was significantly enhanced.Under optimal conditions,the sensor exhibited good selectivity and high sensitivity toward TeA.A linear relationship(R2=0.992)was observed between the electrochemical response and TeA concentration in the range of 0.001-10 μg/kg,with a detection limit of 0.3 ng/kg.The developed method was successfully applied to determination of TeA in fruit samples,with recoveries ranging from 90.8%to 110.8%,and relative standard deviations from 1.9%to 8.4%.

Targeting drug delivery systems mediated by nanoparticles has shown great potential in the diagnosis and treatment of cancer. However, influences of different tumor progressions on the accumulation of nanoparticles, especially the ligand-modified active targeting nanoparticles are seldom exploited. In this work, the accumulation and penetration of RGD-modified gold nanoparticles (active AuNPs) with different sizes were investigated in orthotopic breast cancer with different tumor progressions. The results showed that the smallest active AuNPs had better accumulation and permeation effects in early tumor tissues with the relatively looser extracellular matrix, larger gaps, lower interstitial fluid pressure, and less receptor expression, which was due to size effects. However, the larger active AuNPs had better accumulation and penetration effects in late tumor tissues with highly expressed target receptors integrin α _v β ₃ because of the multivalent interactions between larger active nanoparticles and integrin α _v β ₃. In the midterm, tumor accumulation of active AuNPs was equally influenced by size effects and multivalent interactions. Therefore, RGD-modified nanoparticles with sizes of 7 and 90 nm accumulated more in tumors. This study will guide a rational design of active targeting nanoparticles for enhancing the diagnosis and treatment of tumors based on their progressions.

OBJECTIVES: Digitally collect data on the preparation space of the molar occlusal surface for full crown restoration and analyze the minimum preparation spaces and areas prone to insufficient preparation. METHODS: A total of 846 molars (excluding third molars) designated for repair with zirconium dioxide crowns were selected. All molars were prepared by using the fixed-depth groove method under visual guidance. A digital impression was collected, and the occlusal surface of the preparation was divided into regions: mesiobuccal area, mesiolingual area, distobuccal area, distolingual area, mesial marginal ridge, and distal marginal ridge. Image measurement software (3Shape Unite) was employed to record the smallest preparation space on the molar occlusal surface, and a space of <1 mm was defined as insufficient preparation. The chi-square test was utilized for the statistical analysis of data. RESULTS: Significant differences were observed in the areas with the smallest preparation space on the occlusal surface of molars across different quadrants (P<0.05). Notably, the area with the smallest preparation space on the occlusal surface of the maxillary first molar was most frequently found in the mesiobuccal area, whereas the smallest preparation spaces on other molars were predominantly located in the distolingual area. The insufficient preparation of the occlusal surface occurred in 41.0% (347 cases) of teeth. The distribution of prone areas corresponded with the distribution of the smallest spatial areas. CONCLUSIONS Although the fixed-depth groove method is employed, the preparation space on the molar surface unavoidably remains uneven under visual guidance. The identification of the areas prone to minimum preparation spaces can serve as a valuable guide for clinicians to prevent insufficient preparation.
Humans ; Molar/anatomy & histology* ; Crowns ; Zirconium

Objective To investigate the expression of T cell subsets in the spleen of BTBR T+Itpr3tf autistic mouse at 4,8,and 12 weeks of age,and to determine the optimal age for studying the relationship between immune abnormalities and autism in BTBR autistic mice.Methods It randomly selected 5～6 male BTBR mouse at 4 weeks,8 weeks,and 12 weeks of age and C57BL/6J mouse of the same gender at corresponding ages for the three-box social interaction test,the self-grooming test,and the marble-burying test;Single cell suspensions were prepared from the spleens of mouse at 8 and 12 weeks of age,and flow cytometry was used to detect 8 subsets of T cells(TH 1,TH2,TH17,TC1,TC2,TC17,TFH,and Treg).Results Compared with C57BL/6J mouse of the same age,BTBR mouse at 4 weeks,8 weeks,and 12 weeks of age showed a decrease in social time(P＜0.001),an increase in grooming time(P＜0.01,P＜0.001),and an increase in the number of marbles buried(P＜0.01,P＜0.001)in BTBR mouse at 8 weeks and 12 weeks of age.As well,the expression of TH 1(P＜0.001),TH2(P＜0.01),TC 1(P＜0.05),TC2(P＜0.001),and TFH(P＜0.01)cells in 8-week-old BTBR mouse were significantly increased,while the expression of Treg(P＜0.001)cells were significantly decreased;The expression of TH 1(P＜0.01),TH2(P＜0.01),TH 17(P＜0.05),TC1(P＜0.01),TC2(P＜0.001),TC 17(P＜0.01),and TFH(P＜0.001)increased in 12-week-old BTBR mouse,while the expression of Treg(P＜0.05)cells decreased.At different age stages(P＜0.050)the ratio of TH 1/Treg and TC 1/Treg in 8-week-old BTBR mouse were significantly higher than those in 12 week old mouse,while the TC 17/Treg ratio decreased.Conclusions BTBR mouse at different developmental stages exhibit varying degrees of abnormal increase in Teff/Treg ratio.Based on result of behavioral test,it is recommended to use 8-week-old BTBR mice for research on autism and immune abnormalities.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

PURPOSE: Deep vein thrombosis (DVT) of the left and right lower extremities was treated in the same way, but the left and right extremities received different levels of attention. This study aimed to investigate the differences between the right and left lower extremity deep vein thrombosis (LEDVT). METHODS: Clinical characteristics of LEDVT patients from July 2020 to June 2022 were retrospectively analyzed to compare the incidence of LEDVT on different limbs, demographics, predisposing factors, and anatomical characteristics. The exclusion criteria were bilateral LEDVT and recurrent thrombosis. Measured data was analyzed using independent samples t-test or Mann-Whitney test. Count data were analyzed by Chi-square test. A p < 0.05 was considered a statistically significant difference. RESULTS: There were 478 patients included in this study and the ratio of left to right LEDVT on the left and right limbs was 3.16:1 (363:115). Left LEDVT predominantly affected female, with the major aged > 50 years (50 - 60 years: 16.80%; > 60 years: 57.30%). The primary predisposing factor was iliac vein compression syndrome, with iliofemoral thrombosis being the main type. Male patients with LEDVT on the right limb were predominant and the age of onset was usually ≤ 60 years (52.17%). The main predisposing factor was recent surgery or trauma (< 30 days) and femoropopliteal thrombosis was the main type. In more detail, the left iliac vein was compressed mainly in the proximal segment, and the right iliac vein was compressed mainly in the intermediate and distal segments. Recent surgery or trauma to the locomotor system and genitourinary system often induced LEDVT. CONCLUSION The incidence of LEDVT on the left is significantly higher than that on the right. LEDVT on different sides has different characteristics, which is crucial for prevention and diagnosis in the relevant population so there are also differences in treatment of the affected limbs.
Humans ; Venous Thrombosis/etiology* ; Male ; Female ; Middle Aged ; Lower Extremity/blood supply* ; Retrospective Studies ; Adult ; Aged ; Age Factors ; Sex Factors ; Risk Factors ; Acute Disease ; Incidence ; Aged, 80 and over ; Young Adult ; Adolescent