Objective The purpose of this study was to investigate the bacterial communities of biting midges and ticks collected from three sites in the Poyang Lake area,namely,Qunlu Practice Base,Peach Blossom Garden,and Huangtong Animal Husbandry,and whether vectors carry any bacterial pathogens that may cause diseases to humans,to provide scientific basis for prospective pathogen discovery and disease prevention and control. Methods Using a metataxonomics approach in concert with full-length 16S rRNA gene sequencing and operational phylogenetic unit(OPU)analysis,we characterized the species-level microbial community structure of two important vector species,biting midges and ticks,including 33 arthropod samples comprising 3,885 individuals,collected around Poyang Lake. Results A total of 662 OPUs were classified in biting midges,including 195 known species and 373 potentially new species,and 618 OPUs were classified in ticks,including 217 known species and 326 potentially new species.Surprisingly,OPUs with potentially pathogenicity were detected in both arthropod vectors,with 66 known species of biting midges reported to carry potential pathogens,including Asaia lannensis and Rickettsia bellii,compared to 50 in ticks,such as Acinetobacter lwoffii and Staphylococcus sciuri.We found that Proteobacteria was the most dominant group in both midges and ticks.Furthermore,the outcomes demonstrated that the microbiota of midges and ticks tend to be governed by a few highly abundant bacteria.Pantoea sp7 was predominant in biting midges,while Coxiella sp1 was enriched in ticks.Meanwhile,Coxiella spp.,which may be essential for the survival of Haemaphysalis longicornis Neumann,were detected in all tick samples.The identification of dominant species and pathogens of biting midges and ticks in this study serves to broaden our knowledge associated to microbes of arthropod vectors. Conclusion Biting midges and ticks carry large numbers of known and potentially novel bacteria,and carry a wide range of potentially pathogenic bacteria,which may pose a risk of infection to humans and animals.The microbial communities of midges and ticks tend to be dominated by a few highly abundant bacteria.

AIM: To investigate the effectiveness of panretinal photocoagulation(PRP)combined with intravitreal conbercept(IVC)for patients with different stages of proliferative diabetic retinopathy(PDR).METHODS: Retrospective study. The medical records for 100 patients(100 eyes)with PDR treated with PRP combined with IVC from January 2018 to June 2020 were reviewed, including 34 eyes with early PDR(group A), 43 with high-risk PDR(group B), and 23 with fibrovascular PDR(group C). The baseline information, best corrected visual acuity(BCVA), central macular thickness(CMT), the rate of vitrectomy and retinal detachment of the patients in the three groups at 1, 3, 6mo and the last follow-up after combination treatment were observed.RESULTS: The patients were followed up for 14.60±11.64mo(6-52mo), with a mean age of 54.22 ±9.32 years. We found 15 eyes(15.0%)who underwent vitrectomy after the combination treatment. The vitrectomy rates of the three groups were 2.9% in group A, 13.9% in group B, and 34.7% in group C. We found no instances of retinal detachment after the treatments. Most patients demonstrated improved BCVA and CMT values with the treatments.CONCLUSION: PRP combined with IVC is safe and effective in patients with different PDR stages.

Objective: To study the effect of anti-fibrotic tetrapeptide N-acetyl-seryl-aspartyl-lysyl-proline (Ac-SDKP) on phosphorylated heat shock protein 27 (P-HSP27) and zinc finger family transcriptional repressor 1 (SNAI1) expression to explore the anti-silicosis fibrosis effect of Ac-SDKP. Methods: In December 2014, the rat silicosis animal model was prepared by one-time bronchial infusion of silicon dioxide (SiO(2)) dust. 80 SPF healthy adult Wistar rats were selected, and the rats were divided into 8 groups according to the random number table method, 10 in each group. Model control group for 4 weeks (feeding for 4 weeks) , model control group for 8 weeks (feeding for 8 weeks) : bronchial perfusion with normal saline 1.0 ml per animal. Silicosis model group for 4 weeks (feeding for 4 weeks) and silicosis model group for 8 weeks (feeding for 8 weeks) : bronchial perfusion of 50 mg/ml SiO(2) suspension 1.0 ml per animal. Ac-SDKP administration group for 4 weeks (feeding for 4 weeks) , Ac-SDKP administration group for 8 weeks (feeding for 8 weeks) : Ac-SDKP 800 μg·kg(-1)·d(-1) was administered by intraperitoneal pump. Ac-SDKP preventive treatment group: 48 h after Ac-SDKP 800 μg·kg(-1)·d(-1) administration, bronchial perfusion of SiO(2) suspension 1.0 ml per animal, raised for 8 weeks. Ac-SDKP anti-fibrosis treatment group: after bronchial perfusion of 1.0 ml of SiO(2) suspension for 4 weeks, Ac-SDKP 800 μg·kg(-1)·d(-1) was administered for 4 weeks. Western blotting was used to detect the expression of P-HSP27, SNAI1, α-smooth muscle actin (α-SMA) , and collage typeⅠ and Ⅲ in each group. The expression of P-HSP27 and SNAI1 was detected by immunohistochemistry, and the co-localized expression of P-HSP27 and α-SMA was detected by laser confocal microscopy. Results: Compared with the model control group, the expressions of P-HSP27, SNAI1, α-SMA, and collage typeⅠ and Ⅲ in the silicosis fibrosis area of the rats in the silicosis model group were enhanced, and the differences were statistically significant (P<0.05) . After Ac-SDKP intervention, compared with silicosis model group for 8 weeks, the expressions of P-HSP27, SNAI1 α-SMA, and collage typeⅠ and Ⅲ in the Ac-SDKP preventive and anti-fibrosis treatment groups were significantly decreased, and the differences were statistically significant (P<0.05) . However, the expressions of P-HSP27 SNAI1, and collage typeⅠ and Ⅲ between the Ac-SDKP administration group and the model control group did not change significantly, and the differences were not statistically significant (P>0.05) . Laser confocal results showed that the positive cells expressing P-HSP27 and α-SMA in the lung tissue of the silicosis model group were more than those in the model control group. Compared with the silicosis model group, the Ac-SDKP prevention and anti-fibrosis treatment groups expressing the positive cells of P-HSP27 and α-SMA decreased. Compared with the model control group for 8 weeks, there were some double-positive cells expressing P-HSP27 and α-SMA in the nodules of the silicosis model group for 8 weeks. Conclusion: Ac-SDKP may play an anti-silicic fibrosis effect by regulating the P-HSP27/SNAI1 pathway.
Animals ; HSP27 Heat-Shock Proteins ; Oligopeptides ; Rats ; Rats, Wistar ; Silicon Dioxide ; Silicosis/metabolism*

Objective: To analyze the influencing factors of iron metabolism assessment in patients with myelodysplastic syndrome. Methods: MRI and/or DECT were used to detect liver and cardiac iron content in 181 patients with MDS, among whom, 41 received regular iron chelation therapy during two examinations. The adjusted ferritin (ASF) , erythropoietin (EPO) , cardiac function, liver transaminase, hepatitis antibody, and peripheral blood T cell polarization were detected and the results of myelofibrosis, splenomegaly, and cyclosporine were collected and comparative analyzed in patients. Results: We observed a positive correlation between liver iron concentration and ASF both in the MRI group and DECT groups (r=0.512 and 0.606, respectively, P<0.001) , only a weak correlation between the heart iron concentration and ASF in the MRI group (r=0.303, P<0.001) , and no significant correlation between cardiac iron concentration and ASF in the DECT group (r=0.231, P=0.053) . Moreover, transfusion dependence in liver and cardiac [MRI group was significantly associated with the concentration of iron in: LIC: (28.370±10.706) mg/g vs (7.593±3.508) mg/g, t=24.30, P<0.001; MIC: 1.81 vs 0.95, z=2.625, P<0.05; DECT group: liver VIC: (4.269±1.258) g/L vs (1.078±0.383) g/L, t=23.14, P<0.001: cardiac VIC: 1.69 vs 0.68, z=3.142, P<0.05]. The concentration of EPO in the severe iron overload group was significantly higher than that in the mild to moderate iron overload group and normal group (P<0.001) . Compared to the low-risk MDS group, the liver iron concentration in patients with MDS with cyclic sideroblasts (MDS-RS) was significantly elevated [DECT group: 3.80 (1.97, 5.51) g/L vs 1.66 (0.67, 2.94) g/L, P=0.004; MRI group: 13.7 (8.1,29.1) mg/g vs 11.6 (7.1,21.1) mg/g, P=0.032]. Factors including age, bone marrow fibrosis, splenomegaly, T cell polarization, use of cyclosporine A, liver aminotransferase, and hepatitis antibody positive had no obvious effect on iron metabolism. Conclusion: There was a positive correlation between liver iron concentration and ASF in patients with MDS, whereas there was no significant correlation between cardiac iron concentration and ASF. Iron metabolism was affected by transfusion dependence, EPO concentration, and RS.
Ferritins ; Humans ; Iron ; Iron Overload ; Liver/metabolism* ; Myelodysplastic Syndromes/therapy* ; Primary Myelofibrosis ; Retrospective Studies ; Splenomegaly

Objective: To determine the spatiotemporal distribution of Schistosoma (S.) japonicum infections in humans, livestock, and Oncomelania (O.) hupensis across the endemic foci of China. Methods: Based on multi-stage continuous downscaling of sentinel monitoring, county-based schistosomiasis surveillance data were captured from the national schistosomiasis surveillance sites of China from 2005 to 2019. The data included S. japonicum infections in humans, livestock, and O. hupensis. The spatiotemporal trends for schistosomiasis were detected using a Joinpoint regression model, with a standard deviational ellipse (SDE) tool, which determined the central tendency and dispersion in the spatial distribution of schistosomiasis. Further, more spatiotemporal clusters of S. japonicum infections in humans, livestock, and O. hupensis were evaluated by the Poisson model. Results: The prevalence of S. japonicum human infections decreased from 2.06% to zero based on data of the national schistosomiasis surveillance sites of China from 2005 to 2019, with a reduction from 9.42% to zero for the prevalence of S. japonicum infections in livestock, and from 0.26% to zero for the prevalence of S. japonicum infections in O. hupensis. Analysis using an SDE tool showed that schistosomiasis-affected regions were reduced yearly from 2005 to 2014 in the endemic provinces of Hunan, Hubei, Jiangxi, and Anhui, as well as in the Poyang and Dongting Lake regions. Poisson model revealed 11 clusters of S. japonicum human infections, six clusters of S. japonicum infections in livestock, and nine clusters of S. japonicum infections in O. hupensis. The clusters of human infection were highly consistent with clusters of S. japonicum infections in livestock and O. hupensis. They were in the 5 provinces of Hunan, Hubei, Jiangxi, Anhui, and Jiangsu, as well as along the middle and lower reaches of the Yangtze River. Humans, livestock, and O. hupensis infections with S. japonicum were mainly concentrated in the north of the Hunan Province, south of the Hubei Province, north of the Jiangxi Province, and southwestern portion of Anhui Province. In the 2 mountainous provinces of Sichuan and Yunnan, human, livestock, and O. hupensis infections with S. japonicum were mainly concentrated in the northwestern portion of the Yunnan Province, the Daliangshan area in the south of Sichuan Province, and the hilly regions in the middle of Sichuan Province. Conclusions: A remarkable decline in the disease prevalence of S. japonicum infection was observed in endemic schistosomiasis in China between 2005 and 2019. However, there remains a long-term risk of transmission in local areas, with the highest-risk areas primarily in Poyang Lake and Dongting Lake regions, requiring to focus on vigilance against the rebound of the epidemic. Development of high-sensitivity detection methods and integrating the transmission links such as human and livestock infection, wild animal infection, and O. hupensis into the surveillance-response system will ensure the elimination of schistosomiasis in China by 2030.

OBJECTIVE: To predict the trends for fine-scale spread of Oncomelania hupensis based on supervised machine learning models in Shanghai Municipality, so as to provide insights into precision O. hupensis snail control. METHODS: Based on 2016 O. hupensis snail survey data in Shanghai Municipality and climatic, geographical, vegetation and socioeconomic data relating to O. hupensis snail distribution, seven supervised machine learning models were created to predict the risk of snail spread in Shanghai, including decision tree, random forest, generalized boosted model, support vector machine, naive Bayes, k-nearest neighbor and C5.0. The performance of seven models for predicting snail spread was evaluated with the area under the receiver operating characteristic curve (AUC), F1-score and accuracy, and optimal models were selected to identify the environmental variables affecting snail spread and predict the areas at risk of snail spread in Shanghai Municipality. RESULTS: Seven supervised machine learning models were successfully created to predict the risk of snail spread in Shanghai Municipality, and random forest (AUC = 0.901, F1-score = 0.840, ACC = 0.797) and generalized boosted model (AUC= 0.889, F1-score = 0.869, ACC = 0.835) showed higher predictive performance than other models. Random forest analysis showed that the three most important climatic variables contributing to snail spread in Shanghai included aridity (11.87%), ≥ 0 °C annual accumulated temperature (10.19%), moisture index (10.18%) and average annual precipitation (9.86%), the two most important vegetation variables included the vegetation index of the first quarter (8.30%) and vegetation index of the second quarter (7.69%). Snails were more likely to spread at aridity of < 0.87, ≥ 0 °C annual accumulated temperature of 5 550 to 5 675 °C, moisture index of > 39% and average annual precipitation of > 1 180 mm, and with the vegetation index of the first quarter of > 0.4 and the vegetation index of the first quarter of > 0.6. According to the water resource developments and township administrative maps, the areas at risk of snail spread were mainly predicted in 10 townships/subdistricts, covering the Xipian, Dongpian and Tainan sections of southern Shanghai. CONCLUSIONS Supervised machine learning models are effective to predict the risk of fine-scale O. hupensis snail spread and identify the environmental determinants relating to snail spread. The areas at risk of O. hupensis snail spread are mainly located in southwestern Songjiang District, northwestern Jinshan District and southeastern Qingpu District of Shanghai Municipality.
Animals ; Bayes Theorem ; China/epidemiology* ; Ecosystem ; Gastropoda ; Supervised Machine Learning

Professor
Acupuncture ; Acupuncture Therapy ; Angina, Stable ; Combined Modality Therapy ; Humans ; Moxibustion

Objective: Several COVID-19 patients have overlapping comorbidities. The independent role of each component contributing to the risk of COVID-19 is unknown, and how some non-cardiometabolic comorbidities affect the risk of COVID-19 remains unclear. Methods: A retrospective follow-up design was adopted. A total of 1,160 laboratory-confirmed patients were enrolled from nine provinces in China. Data on comorbidities were obtained from the patients' medical records. Multivariable logistic regression models were used to estimate the odds ratio ( Results: Overall, 158 (13.6%) patients were diagnosed with severe illness and 32 (2.7%) had unfavorable outcomes. Hypertension (2.87, 1.30-6.32), type 2 diabetes (T2DM) (3.57, 2.32-5.49), cardiovascular disease (CVD) (3.78, 1.81-7.89), fatty liver disease (7.53, 1.96-28.96), hyperlipidemia (2.15, 1.26-3.67), other lung diseases (6.00, 3.01-11.96), and electrolyte imbalance (10.40, 3.00-26.10) were independently linked to increased odds of being severely ill. T2DM (6.07, 2.89-12.75), CVD (8.47, 6.03-11.89), and electrolyte imbalance (19.44, 11.47-32.96) were also strong predictors of unfavorable outcomes. Women with comorbidities were more likely to have severe disease on admission (5.46, 3.25-9.19), while men with comorbidities were more likely to have unfavorable treatment outcomes (6.58, 1.46-29.64) within two weeks. Conclusion Besides hypertension, diabetes, and CVD, fatty liver disease, hyperlipidemia, other lung diseases, and electrolyte imbalance were independent risk factors for COVID-19 severity and poor treatment outcome. Women with comorbidities were more likely to have severe disease, while men with comorbidities were more likely to have unfavorable treatment outcomes.
Adult ; Aged ; COVID-19/virology* ; China/epidemiology* ; Comorbidity ; Female ; Humans ; Male ; Middle Aged ; Retrospective Studies ; Severity of Illness Index ; Treatment Outcome

Objective: To examine the association of pre-pregnancy body mass and weight gain during pregnancy with macrosomia. Methods: From January 2015 to December 2015, a total of 20 477 pregnant women were recruited by probabilistic proportional scale sampling with simple randomization in Sichuan, Yunnan and Guizhou Provinces. Basic information of pregnant women, weight gain during pregnancy and weight of newborn were collected. A multiple logistic regression model was used to assess the association between the pre-pregnancy body mass and gestational weight gain indicators with macrosomia. Results: 20 321 mother-infant were included in the final analysis. 20 321 pregnant women were (30.09±4.10) years old and delivered at (39.20±1.29) weeks, among which 12 341 (60.73%) cases were cesarean delivery. The birth weight of 20 321 infants were (3 292.26±431.67) grams, and 970 (4.77%) were macrosomia. The multiple logistic regression model showed that after adjusting for the age of women, compared to the normal weight group in the pre-pregnancy, the overweight and obesity group elevated the risk of macrosomia, with OR (95%CI) about 1.99 (95%CI: 1.69−2.35) and 4.05 (95%CI: 3.05−5.39), respectively. After adjusting for the age, the pre-pregnancy BMI, delivery weeks, delivery mode and infant′s gender, compared to the weight-gain appropriate group, higher weight gain rate in the mid-pregnancy and excessive total gestational weight gain elevated the risk of macrosomia, with OR (95%CI) about 1.99 (95%CI: 1.66−2.39) and 1.80 (95%CI: 1.55−2.08), respectively. Conclusion The overweight before pregnancy, obesity before pregnancy, the rate of weight gain in the second trimester and the high total weight gain during pregnancy could increase the risk of macrosomia.

Objective To examine the association of pre?pregnancy body mass and weight gain during pregnancy with macrosomia. Methods From January 2015 to December 2015, a total of 20 477 pregnant women were recruited by probabilistic proportional scale sampling with simple randomization in Sichuan, Yunnan and Guizhou Provinces. Basic information of pregnant women, weight gain during pregnancy and weight of newborn were collected. A multiple logistic regression model was used to assess the association between the pre?pregnancy body mass and gestational weight gain indicators with macrosomia. Results 20 321 mother?infant were included in the final analysis. 20 321 pregnant women were (30.09 ± 4.10) years old and delivered at (39.20 ± 1.29) weeks, among which 12 341 (60.73%) cases were cesarean delivery. The birth weight of 20 321 infants were (3 292.26 ± 431.67) grams, and 970 (4.77%) were macrosomia. The multiple logistic regression model showed that after adjusting for the age of women, compared to the normal weight group in the pre?pregnancy, the overweight and obesity group elevated the risk of macrosomia, with OR (95%CI) about 1.99 (95%CI: 1.69-2.35) and 4.05 (95%CI: 3.05-5.39), respectively. After adjusting for the age, the pre?pregnancy BMI, delivery weeks, delivery mode and infant's gender, compared to the weight?gain appropriate group, higher weight gain rate in the mid?pregnancy and excessive total gestational weight gain elevated the risk of macrosomia, with OR (95%CI) about 1.99 (95%CI:1.66-2.39) and 1.80 (95%CI: 1.55-2.08), respectively. Conclusion The overweight before pregnancy, obesity before pregnancy, the rate of weight gain in the second trimester and the high total weight gain during pregnancy could increase the risk of macrosomia.