BACKGROUND:Crosslinked sodium hyaluronate gel has good mechanical property,biocompatibility,and biodegradability,and can be used as an isolated protective material in tumor radiation therapy to protect endangered organs from damage caused by excess radiation dose. OBJECTIVE:To investigate the safety and efficacy of crosslinked sodium hyaluronate gel in reducing the dose of radiation to dangerous organs during brachytherapy. METHODS:A total of 16 specific pathogen-free Kunming mice of the same age and similar body weight were selected as experimental subjects and divided into experimental group and control group by the random number table method,with 8 mice in each group.125I seeds were implanted subcutaneously in the back of mice in the experimental group,and then crosslinked sodium hyaluronate gel was injected around the radioactive particles.Only 125I seeds were implanted subcutaneously in the back of mice in the control group.After injection,the distance between the radioactive particles and the epidermis was measured by spiral CT scan,and the surface radiation dose was measured by radiation dosimeter.Within 10 weeks after injection,the growth state,survival rate,skin radiation damage,and gel retention of mice were observed. RESULTS AND CONCLUSION:(1)Spiral CT scan showed that the implanted gel was relatively concentrated and created an effective distance between the radioactive seeds and the epidermis.The body surface radiation dose of the experimental group was significantly lower than that of the control group(P<0.01).(2)During the experimental observation period,mice in both groups survived;mice in the control group showed obvious irritability and other unstable behavior in the late experimental period,and some mice in the experimental group showed similar behavior.The daily food intake of mice in the two groups had no significant change,and the body mass showed the same increasing trend.After implantation of radioactive seeds,the two groups of mice showed different degrees of radioactive skin injury.From day 23 after injection to the end of the experiment,the skin radiation injury score of the experimental group was lower than that of the control group(P<0.01).At week 10 after implantation,6 mice in the experimental group had no obvious gel residue under their skin,and 2 mice had a very small amount of scattered gel-like samples under their skin.(3)Therefore,the crosslinked sodium hyaluronate injection technique can increase the space between the radioactive target area of 125I seeds and the organ at risk outside the target through physical space occupying,which can effectively reduce the dose of the organ at risk,and play a role in the isolation and protection of the organ at risk.

Objective To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.Methods The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes,and the differential genes were identified by Random forest,LASSO regression and SVM algorithms.Based on these differential genes,an artificial neural network model was constructed,and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807.The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma.The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers.Results A total of 24 differential genes were obtained,including 11 up-regulated and 13 down-regulated genes.Seven most relevant mitochondria-related genes(POLB,GSR,KRAS,NT5DC2,NOX4,IGF1,and TGM2)were screened using 3 machine learning algorithms,and the artificial neural network diagnostic model was constructed.The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis(0.740 for the verification dataset and 0.980 for cross-over validation).RT-qPCR detected significant up-regulation of POLB,GSR,KRAS,NOX4,IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma.Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells,immature B cells,resting dendritic cells,memory activated CD4+T cells,M0 macrophages,monocytes,resting memory CD4+T cells and mast cell activation.Conclusion The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.

Objective:To explore the genetic characteristics of a child with 18q terminal deletion syndrome.Methods:Clinical data of a child presented at the Lianyungang Maternal and Child Health Care Hospital on July 20, 2023 was collected. Peripheral blood sample from the child was subjected to G-banded chromosomal karyotyping and chromosomal microarray analysis (CMA). Relevant literature was searched from CNKI, WanFang and PubMed databases over the past decade (from November 1, 2013 to November 1, 2023) using keywords including "18q-syndrome", "18q deletion syndrome" and "18q terminal deletion". This study was approved by Medical Ethics Committee of the Lianyungang Maternal and Child Health Care Hospital (Ethics No. LYG-MER2021017).Results:The child, a 4-year-and-6-month-old female, had manifested short stature, intellectual disability, distinctive facial features, aortic regurgitation, auditory canal atresia, and white matter lesions. She was found to have a karyotype of 46, XX, del(18)(q21), whilst the result of CMA was arr[GRCh37]18q21.33q23(60065821_77317445)×1. Both of her parents were found to have a normal karyotype. Literature review has retrieved 7 reports which involved 11 cases with a terminal 18q23 deletion. The phenotypes of cardiac abnormalities have been diverse, with pulmonary stenosis, atrial septal defect and ventricular septal defect being most common.Conclusion:The 18q terminal deletion probably underlay the multiple congenital anomalies and mental retardation in this child.

In order to solve the problem of strong subjectivity and difficulty in quantification,clinical objectification mainly adopts the techniques of image processing,computer vision and machine learning.The acquisition and processing of prior knowledge is a key link in the objectification of inspection,as well as an important elaboration of the quantification of subjective judgment and macro performance in objectification research.However,there is still a lack of in-depth summary and parametric processing of prior knowledge.Based on the analysis of the current research status of objectification of inspection,this paper uses data mining technology to summarize the experience of TCM inspection.Moreover,the observation information can be transformed into quantifiable digital features through natural language processing and representation learning.Meanwhile,the application of deep learning can realize automatic diagnosis and analysis of observation images to improve accuracy and efficiency,and promote the process of TCM modernization.

Objective:To study the role of special microbial communities in the development of periodontitis in type 2 diabetes melli-tus(T2DM)patients.Methods:40 subjects aged 20-70 years were included and divided into 3 groups:moderate to severe periodon-titis with T2DM(SP.T2DM,n=15),moderate to severe periodontitis group(SP,n=15)and normal healthy group(N,n=10).The basic information,periodontal clinical indicators and blood sugar of the subjects were recorded.Subgingival plaque samples were col-lected,DNA samples of the plaque were extracted,and sequenced by Illumina NovaSeq6000 platform.The microbial diversity,eco-logical characteristics and functions of the plaque were analyzed by Uparse,SPSS and other softwares.Results:481 species in 22 phyla,30 classes,73 orders,129 families and 265 genera were obtained from the samples.Beta polymorphism analysis showed that the species composition of CP.T2DM group and CP group was similar.Alpha polymorphism analysis showed that the species richness and evenness in CP.T2DM group and CP group were higher than those in N group(P＜0.01).Venn diagram analysis showed that the species richness of the plaque in CP.T2DM group was the highest,followed by CP group and the lowest in N group.At the genus lev-el,Klebsiella and Bifidobacterium in CP.T2DM group were larger than those in CP group and N group(P＜0.05),and between group CP and N,P＞0.05.At the species level,the Capnocytophaga leadbetteri in CP.T2DM group was higher than that in CP group and N group(P＜0.05),between group CP and N,P＞0.05;There were some differences in the microbial community structure of subgingival plaque among the 3 groups.The species richness of subgingival flora in patients with CP and T2DM was higher than that in patients with CP and healthy people.Conclusion:The increase of Klebsiella,Bifidobacterium and Capnocytophaga leadbetter in subgingival flora of patients with moderate and severe periodontitis may be related to the development of T2DM.

Objective To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.Methods The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes,and the differential genes were identified by Random forest,LASSO regression and SVM algorithms.Based on these differential genes,an artificial neural network model was constructed,and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807.The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma.The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers.Results A total of 24 differential genes were obtained,including 11 up-regulated and 13 down-regulated genes.Seven most relevant mitochondria-related genes(POLB,GSR,KRAS,NT5DC2,NOX4,IGF1,and TGM2)were screened using 3 machine learning algorithms,and the artificial neural network diagnostic model was constructed.The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis(0.740 for the verification dataset and 0.980 for cross-over validation).RT-qPCR detected significant up-regulation of POLB,GSR,KRAS,NOX4,IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma.Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells,immature B cells,resting dendritic cells,memory activated CD4+T cells,M0 macrophages,monocytes,resting memory CD4+T cells and mast cell activation.Conclusion The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.

Objective:To construct the quality evaluation scale of specification of management for humanistic caring in outpatients and test its reliability and validity.Methods:Referring to the group standards in Specification of Management for Humanistic Caring in Outpatients released by the China Association for Life Care,as well as relevant guidelines and literature,a pool of items for the quality evaluation scale of specification of management for humanistic caring in outpatients was formed.After expert consultation and expert argumentation,a quality evaluation scale of specification of management for humanistic caring in outpatients was constructed.From January to February 2024,243 hospital managers from 5 hospitals in Zhengzhou were selected as survey subjects to conduct item analysis,and reliability and validity testing on the scale.Results:Two rounds of expert inquiry and two rounds of expert argumentation were conducted,with questionnaire response rates of 92.00%and 100.00%,respectively,and expert authority coefficients of 0.952.In the second round of the expert inquiry scale,the mean importance score of the first-level indicators was 4.80 to 5.00,the full score ratio was 88.00%to 100.00%,the coefficient of variation was 0.04 to 0.17,and Kendall's coefficient of concordance was 0.857(P＜0.001);the mean importance score of the second-level indicators was 4.60 to 5.00,the full score ratio was 80.00%to 100.00%,the coefficient of variation was 0.00 to 0.21,and Kendall's coefficient of concordance was 0.775(P＜0.001);the mean importance score of the third-level indicators was 4.60 to 5.00,the full score ratio was 76.00%to 100.00%,the coefficient of variation was 0.00 to 0.21,and Kendall's coefficient of concordance was 0.830(P＜0.001).Finally,a quality evaluation scale of specification of management for humanistic caring in outpatients was formed,including 5 first-level indicators,25 second-level indicators,and 58 third-level indicators.Exploratory factor analysis produced 5 common factors with a cumulative variance contribution rate of 74.628%.The Pearson correlation coefficients between the five-factor scores ranged from 0.648 to 0.798,and the correlation coefficients between the factor scores and the total score of the scale ranged from 0.784 to 0.938.The scale-level content validity index(S-CVI)of the scale was 0.945,the item-content validity index(I-CVI)was 0.725 to 1.000,the Cronbach's alpha coefficient of the total scale was 0.973,and the retest reliability coefficient was 0.934.Conclusion:The constructed quality evaluation scale of specification of management for humanistic caring in outpatients has good scientific validity and reliability,and can be used as an evaluation tool for specification of management for humanistic caring in outpatients.

Objective:To investigate the distribution of physical and chemical water improvement areas of drinking water-borne endemic fluorosis in Inner Mongolia Autonomous Region, as well as the use of household water purifiers.Methods:From April to October 2021, a survey was conducted in a drinking water-borne endemic fluorosis areas in Inner Mongolia Autonomous Region where physical and chemical water improvement was carried out. The survey included the basic situation of the affected villages (number of permanent households, number of permanent residents, historical water fluoride content) and the use of residential water purifiers. Household peripheral water samples were collected to test the water fluoride content. Water purifier installation rate, normal usage rate, qualified water fluoride rate in normal usage, and the proportion of households covered by filter replacement departments were calculated.Results:In Inner Mongolia Autonomous Region, the physical and chemical water improvement areas of drinking water-borne endemic fluorosis were distributed in 2 735 villages in 11 leagues (cities) throughout the region, with 192 950 permanent households and 540 216 permanent residents. The average historical water fluoride content in all leagues (cities) was 2.18 mg/L, and the current average water fluoride content was 0.40 mg/L. A total of 134 763 water purifiers were installed, with an installation rate of 69.84% (134 763/192 950). A total of 10 773 households were surveyed, with 10 396 households using water purifiers normally and a normal usage rate of 96.50% (10 396/10 773). Among them, 10 158 households had qualified water fluoride of normal usage, with a qualified water fluoride rate of 97.71% (10 158/10 396). Of the 10 396 households using water purifiers normally, 3 974 households (38.23%) had filter cartridges used within one year, and 3 961 households had qualified water fluoride, with a qualified rate of water fluoride of 99.67% (3 961/3 974). Six thousand four hundred and twenty-two households (61.77%) had filter cartridges used for more than one year, with 6 197 households had qualified water fluoride and a qualified rate of water fluoride of 96.50% (6 197/6 422). There was a statistically significant difference in the qualified rate of water fluoride between purifiers with different filter cartridge usage times (χ 2 = 110.73, P < 0.001). Among the 10 773 surveyed households, the filter cartridges replacement department covered 10 470 households, accounting for 97.19% (10 470/10 773). Conclusions:In Inner Mongolia Autonomous Region, the physical and chemical water improvement areas of drinking water-borne endemic fluorosis are widely distributed, and the normal usage rate of household water purifiers is relatively high. The qualified rate of water fluoride in household water purifiers with filter cartridges used for more than one year is low.

Objective:To investigate the arsenic metabolism pattern and possible influencing factors in the population in drinking-water-borne endemic arsenic poisoning (drinking-water-borne arsenic poisoning for short) areas.Methods:In December 2004, a cluster sampling method was used to select arsenic poisoning population (arsenic poisoning group) and healthy population (control group) in drinking-water-borne arsenic poisoning area of Bayannur City, Inner Mongolia Autonomous Region as the survey subjects. A questionnaire survey was conducted. Arsenic content in drinking water at home of survey subjects, the levels of urinary arsenic and its metabolites, including [trivalent arsenic (As Ⅲ), inorganic arsenic (iAs), monomethylarsenic acid (pentavalent, MMA V), dimethylarsenic acid (pentavalent, DMA V), total arsenic (tAs), percentage of inorganic arsenic (iAs%), percentage of monomethylarsenic acid (MMA%), percentage of dimethylarsenic acid (DMA%), primary methylation index (PMI), secondary methylation index (SMI)] were tested using high performance liquid chromatography-inductively coupled plasma mass spectrometry; nail arsenic and nail selenium levels were tested using atomic fluorescence spectrometer. The influencing factors of arsenic metabolism pattern were analyzed by multiple linear regression. Results:A total of 536 survey subjects were included, including 155 individuals in the arsenic poisoning group and 381 in the control group. The water arsenic level ranged from 0.0 to 825.7 μg/L. Compared with the control group, there was no significant difference in the distribution of gender, education level and dental fluorosis in the arsenic poisoning group ( P > 0.05), but there were significant differences in the distribution of age, marital status, smoking, drinking and water arsenic ( P < 0.05). Compared with the control group, the levels of urinary As Ⅲ, iAs, MMA V, DMA V, tAs, MMA%, MMA/DMA and nail arsenic in the arsenic poisoning group were higher ( P < 0.05), while the levels of urinary DMA%, SMI and nail selenium were lower ( P < 0.05); but there was no statistically significant difference in the levels of urinary iAs% and PMI ( P > 0.05). Gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic were the influencing factors of urinary As Ⅲ (β = - 19.82, - 23.83, 0.61, 0.21, 7.26, 2.98, P < 0.05). Age, depth of wells, water arsenic and nail arsenic were the influencing factors of urinary tAs (β = 3.18, 3.25, 1.31, 15.59, P < 0.05). Gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic were the influencing factors of urinary iAs (β = - 20.47, - 25.90, 0.64, 0.25, 7.87, 3.11, P < 0.05). Age, gender, education level, water arsenic and nail arsenic were the influencing factors of urinary MMA V (β = 0.52, - 17.07, - 21.84, 0.22, 2.77, P < 0.05). Age, depth of wells, water arsenic and nail arsenic were the influencing factors of urinary DMA V (β = 2.35, 2.47, 0.85, 9.22, P < 0.05). Conclusions:Compared with healthy individuals, there are differences in arsenic metabolism pattern among individuals with drinking-water-borne arsenic poisoning. Age, gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic may be influencing factors of different arsenic metabolism patterns.