[This corrects the article DOI: 10.1016/j.apsb.2022.03.002.].

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models' generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.

Objective:To investigate the correlation between occupational ionizing radiation exposure and abnormal metabolic indicators, providing a basis for assessing the health risks of occupational ionizing radiation workers and establishing a risk prediction model for chronic metabolic diseases.Methods:In January 2023, 7708 individuals were randomly selected from the occupational health examination data in Zhejiang Province. After excluding 16 individuals due to record errors, 2698 on-the-job workers exposed to ionizing radiation from 2020 to 2021 were selected as the exposure group, 2027 pre-employment workers exposed to ionizing radiation from 2016 to 2022 were selected as the pre-employment control group, and 2967 non-ionizing radiation workers from 2016 to 2022 were selected as the control group. Demographic data, blood routine, urine routine, biochemical indicators, and peripheral blood lymphocyte micronucleus rate of each group were collected. One-way ANOVA and rank sum test were used for comparison of indicators. The exposure group was divided into different groups based on age, exposure duration, and body mass index (BMI), and the correlation between indicators and occupational ionizing radiation was analyzed. Lasso variable selection was conducted by constructing a penalty coefficient (λ), and a complete regression model was established.Results:There were statistically significant differences in indicators such as blood pressure, heart rate, and average hemoglobin concentration between the exposure group and the control group, as well as the pre-employment control group ( P<0.05). Through Lasso variable selection, 19 indicators including exposure length, systolic blood pressure (SBP), diastolic blood pressure (DBP), body weight, body mass index (BMI), urine pH value, red blood cell count, microscopic white blood cells, casts, absolute value of monocytes, mean corpuscular volume of red blood cells, mean hemoglobin concentration, alkaline phosphatase, albumin-to-globulin ratio, total bile acid, α-L-fucosidase, urea, creatinine, and low-density lipoprotein cholesterol (LDL-C). There were statistically significant differences in exposure length, SBP, DBP, body weight, BMI, microscopic white blood cells, casts, albumin-to-globulin ratio, total bile acid, α-L-fucosidase, urea, creatinine, LDL-C, and mean corpuscular volume of red blood cells among workers of different ages in the exposure group ( P<0.05) ; there were statistically significant differences in SBP, DBP, body weight, BMI, microscopic white blood cells, casts, albumin-to-globulin ratio, total bile acid, α-L-fucosidase, urea, creatinine, LDL-C, and mean corpuscular volume of red blood cells among workers with different exposure durations ( P<0.05) ; there were statistically significant differences in exposure length, SBP, DBP, body weight, BMI, red blood cells, microscopic white blood cells, casts, albumin-to-globulin ratio, total bile acid, α-L-fucosidase, urea, creatinine, LDL-C, absolute value of monocytes, mean corpuscular volume of red blood cells, and mean hemoglobin concentration among workers with different BMIs ( P<0.05) . Conclusion:Occupational ionizing radiation is associated with abnormal metabolic indicators such as blood pressure, heart rate, total bile acid, α-L-fucosidase, urea, and creatinine in the human body. More attention should be paid to the risk of chronic metabolic diseases among workers exposed to ionizing radiation.

Objective To evaluate the clinical efficacy of buccal acupuncture combined with tuina in treating frozen shoulder and explore its potential mechanism of action.Methods Sixty-four patients diagnosed with frozen shoulder were enrolled from the Rehabilitation Department of Qingyuan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Chinese Medicine between May 2024 and December 2024.Patients were randomly divided into an observation group and a control group using a random number table,with 32 cases in each group.The control group received traditional Chinese tuina therapy,while the observation group received additional buccal acupuncture treatment.Three treatment sessions constituted one course,with a total of two courses administered.Clinical efficacy was evaluated after treatment,and the Visual Analogue Scale(VAS)scores for shoulder pain,Constant-Murley Shoulder Score(CMS),and Hamilton Anxiety Scale(HAMA)scores were observed before and after intervention.Serum levels of interleukin 6(IL-6)and C-reactive protein(CRP)were also compared.Results(1)After treatment,the total effective rate of the observation group was 93.75%(30/32),and that of the control group was 75.00%(24/32),and the efficacy of the observation group was superior to that of the control group,and the difference was statistically significant(P＜0.05).(2)After treatment,the VAS scores of patients in the two groups were significantly reduced(P＜0.05),and the observation group was significantly superior to the control group in improving the patients'VAS scores,with a statistically significant difference(P＜0.05).(3)After treatment,the CMS scores of patients in the two groups were significantly increased(P＜0.05),and the observation group was significantly superior to the control group in improving patients'CMS scores,with statistically significant differences(P＜0.05).(4)After treatment,the HAMA scores of patients in the two groups were significantly increased(P＜0.05),and the observation group was significantly superior to the control group in improving patients'HAMA scores,with statistically significant differences(P＜0.05).(5)After treatment,the IL-6 and CRP levels of patients in the two groups were significantly improved(P＜0.05),and the observation group was significantly superior to the control group in improving the IL-6 and CRP levels,and the difference was statistically significant(P＜0.05).Conclusion The combination of buccal acupuncture and traditional Chinese tuina therapy significantly alleviates shoulder pain,improves shoulder joint function,reduces anxiety,and decreases inflammatory markers IL-6 and CRP in patients with frozen shoulder,demonstrating notable clinical efficacy.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effec-tive MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models' generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer po-tential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.

Objective:To investigate the correlation between occupational ionizing radiation exposure and abnormal metabolic indicators, providing a basis for assessing the health risks of occupational ionizing radiation workers and establishing a risk prediction model for chronic metabolic diseases.Methods:In January 2023, 7708 individuals were randomly selected from the occupational health examination data in Zhejiang Province. After excluding 16 individuals due to record errors, 2698 on-the-job workers exposed to ionizing radiation from 2020 to 2021 were selected as the exposure group, 2027 pre-employment workers exposed to ionizing radiation from 2016 to 2022 were selected as the pre-employment control group, and 2967 non-ionizing radiation workers from 2016 to 2022 were selected as the control group. Demographic data, blood routine, urine routine, biochemical indicators, and peripheral blood lymphocyte micronucleus rate of each group were collected. One-way ANOVA and rank sum test were used for comparison of indicators. The exposure group was divided into different groups based on age, exposure duration, and body mass index (BMI), and the correlation between indicators and occupational ionizing radiation was analyzed. Lasso variable selection was conducted by constructing a penalty coefficient (λ), and a complete regression model was established.Results:There were statistically significant differences in indicators such as blood pressure, heart rate, and average hemoglobin concentration between the exposure group and the control group, as well as the pre-employment control group ( P<0.05). Through Lasso variable selection, 19 indicators including exposure length, systolic blood pressure (SBP), diastolic blood pressure (DBP), body weight, body mass index (BMI), urine pH value, red blood cell count, microscopic white blood cells, casts, absolute value of monocytes, mean corpuscular volume of red blood cells, mean hemoglobin concentration, alkaline phosphatase, albumin-to-globulin ratio, total bile acid, α-L-fucosidase, urea, creatinine, and low-density lipoprotein cholesterol (LDL-C). There were statistically significant differences in exposure length, SBP, DBP, body weight, BMI, microscopic white blood cells, casts, albumin-to-globulin ratio, total bile acid, α-L-fucosidase, urea, creatinine, LDL-C, and mean corpuscular volume of red blood cells among workers of different ages in the exposure group ( P<0.05) ; there were statistically significant differences in SBP, DBP, body weight, BMI, microscopic white blood cells, casts, albumin-to-globulin ratio, total bile acid, α-L-fucosidase, urea, creatinine, LDL-C, and mean corpuscular volume of red blood cells among workers with different exposure durations ( P<0.05) ; there were statistically significant differences in exposure length, SBP, DBP, body weight, BMI, red blood cells, microscopic white blood cells, casts, albumin-to-globulin ratio, total bile acid, α-L-fucosidase, urea, creatinine, LDL-C, absolute value of monocytes, mean corpuscular volume of red blood cells, and mean hemoglobin concentration among workers with different BMIs ( P<0.05) . Conclusion:Occupational ionizing radiation is associated with abnormal metabolic indicators such as blood pressure, heart rate, total bile acid, α-L-fucosidase, urea, and creatinine in the human body. More attention should be paid to the risk of chronic metabolic diseases among workers exposed to ionizing radiation.

Feces and intestinal contents of pigs suspected with porcine epidemic diarrhea virus were collected from a farm in Minqin County,Gansu Province,China.After the suspected positive sam-ples were detected by RT-PCR,Vero cells were used to isolate and culture them in vitro.The suc-cessfully isolated virus was identified in the laboratory,and its whole genome sequence was ana-lyzed for genetic evolution.The pathogenicity was evaluated by animal regression test.The results showed that typical syncytial lesions could be observed when the PEDV-positive treatment solu-tion was inoculated with Vero cells in the 4th generation,and the virus titer in the 6th generation reached 10-4 75TCID50/mL.PEDV-like virions with a diameter of about 100 nm and a round shape with obvious capsular membranes and spikes were observed by electron microscopy.Whole genome sequencing analysis showed that the total length of this strain was 28 085 bp,which was far from the G1 subtype represented by the classical strain CV777(96.6％),and had a high homology with the G2b strains BC-2011-1,IA1,USA/Colorado/2013 and WELL(98.6％).This indicated that the strain belonged to the G2b epidemic strain.The animal regression test showed that the 5-day-old piglets developed vomiting,acute watery diarrhea,emaciation and mental depression within 12 h after the attack,and the symptoms worsened and died within 24 h.After autopsy,the infected piglets could be observed with stomach swelling,high intestinal heave,thin and transparent intesti-nal wall,and undigested milk clots inside.In summary,a PEDV G2b epidemic strain was success-fully isolated and identified in this study,and its whole genome sequence and pathogenicity were analyzed,providing research materials for future studies on PEDV gene function,pathogenic mech-anism and vaccine development.

Objective:To investigate the mechanism of immunomodulatory effects of umbilical cord mesenchymal stem cells(UC-MSCs)modified by miR-125b-5p on systemic lupus erythematosus(SLE).Methods:The expression level of miR-125b-5p was detected by real-time fluorescence quantitative PCR in UC-MSCs and peripheral blood mononuclear cells(PBMCs)from SLE patients and health checkers.Annexin V-FITC/PI apoptosis detection kit was used to detect the effect of miR-125b-5p on apoptosis of UC-MSCs.MRL/lpr mice in each group were injected with UC-MSCs via tail vein,and T-lymphocyte subsets in the spleen of the MRL/lpr mice were detected by flow cytometry after 5 weeks.The expression levels of interleukin(IL)-4 and IL-17A in serum of MRL/lpr mice were detected by ELISA.Hematoxylin-eosin staining was used to observe the pathological manifestations of the lungs and kidneys of the MRL/lpr mice.Results:miR-125b-5p was significantly down-regulated in PBMCs of SLE patients compared with healthy controls(P＜0.01).Compared with the UC-MSCs group,the expression of miR-125b-5p in UC-MSCs modified by miR-125b-5p group was increased(P＜0.01).The survival rate of UC-MSCs was significantly increased by miR-125b-5p(P＜0.01).Compared with the untreated group of MRL/lpr mice,the expression level of IL-4 in serum was increased(P＜0.05);the expression level of IL-17A was decreased(P＜0.05);the proportion of Th17 cells in the spleen of MRL/lpr mice was decreased(P＜0.05);the inflammatory cells infiltration and micro-thrombosis of lungs and kidneys of MRL/lpr mice were significantly reduced in the UC-MSCs modified by miR-125b-5p treatment group.Conclusion:UC-MSCs modified by miR-125b-5p have immunomodulatory effects on systemic lupus erythematosus.

Objective To standardize the selection of clinical research outcome indicators,which can objectively evaluate the clinical efficacy or effect of traditional Chinese medicine in the treatment of myasthenia gravis.This study aims to standardize the construction of the core outcome set of clinical research of traditional Chinese medicine in the treatment of myasthenia gravis.Methods We followed the core outcome set development specification(COS-STAD)to carry out research,established a research working group,which set up a Delphi-method advisory group.Two graduate students of working group conducted a document research and meetings of patients to establishe an outcome set item pool of myasthenia gravis in clinical trials of Chinese medicine under the instruction of other members.With the questionnaire based on the content of item pool,we then carried out Delphi-method expert consultations and a consensus meeting.Results The core outcome set of clinical research on myasthenia gravis treated with traditional Chinese medicine included five outcome domains:endpoint outcome,myasthenia gravis symptom evaluation,medication evaluation,quality of life evaluation and safety outcome;Nine outcome measures:recurrence rate,incidence of hormone complications,incidence of crisis,QMGS scale(MGFA quantitative myasthenia gravis score),daily activity scale of MG patients(ADL),analysis of immunosuppressant dosage,analysis of glucocorticoid dosage,analysis of cholinesterase inhibitor dosage,and incidence of adverse events.Conclusion The five outcome domains and nine outcome measures included in the core outcome set can be used as outcome options for the efficacy evaluation of myasthenia gravis clinical research.