ObjectiveTo explore the effects of transmembrane protein 16B （TMEM16B） on brain edema and blood-brain barrier after cerebral ischemia-reperfusion injury in rats. MethodsTMEM16B overexpression and knockdown was performed by adeno-associated virus （AAV）， and then adult male Sprague-Dawley rats were subjected to middle cerebral artery occlusion （MCAO）. Rats were randomly divided into Sham group， MCAO group， AAV no-load group， TMEM16B overexpression group and TMEM16B knockdown group. Modified neurological severity scores， adhesive removal test and cylinder test were used to evaluate neurologic function. The ultrastructure of ischemic brain tissue was observed by transmission electron microscope. Brain water content was reflected by dry wet weight ratio of brain tissue. The expressions of TMEM16B， aquaporin4 （AQP4）， Claudin5 and zonula occludens-1 （ZO-1） were investigated by immunofluorescence and Western blot. ResultsCompared with the AAV no-load group， the sensory and motor functions of rats in TMEM16B overexpression group were significantly impaired. Mitochondria were swollen； mitochondrial cristae and tight junctions disappeared. The brain water content was higher in overexpression group. The expression of TMEM16B and AQP4 increased while the expression of Claudin5 and ZO-1 decreased （all P<0.05）. Compared with the AAV no-load group， the rats in TMEM16B knockdown group showed some recovery in motor function. The mitochondrial cristae and structure were clear， and the basement membrane was partially blurred. The brain water content was lower in knockdown group. The protein levels of TMEM16B and AQP4 were lower while the levels of Claudin5 and ZO-1 were higher in TMEM16B knockdown group than in AAV no-load group （all P<0.05）. ConclusionAn increase in TMEM16B expression aggravates brain edema and blood-brain barrier damage in rats after cerebral ischemia-reperfusion injury， while a decrease in TMEM16B expression alleviates brain edema and protects the blood-brain barrier.

Objective To propose a novel method for immunohistochemically positive cell counting based on the improved YOLOv5s.Methods Regarding the small target characteristics of positive cells,a small target detection layer was added to refine feature extraction.Then,a bidirectional weighted feature pyramid network was used to replace path aggregation network(PANet)in the neck network for realizing multi-scale feature fusion.Additionally,the method used coordinate attention mechanism to make the model pay more attention to small target characteristics,and replaced the original GIoU with EIoU loss function for enhancing the detection performance.Results The model was trained on the self-built immunohistochemical image dataset.The average accuracy of the improved model was 89.3%,which was 4.0%higher than the original model and surpassed mainstream target detection models.The 5-year survival prediction model constructed with the method achieved an average accuracy of 76.8%and an average area under the curve of 0.81,demonstrating its superior prediction ability.Conclusion The proposed model can quickly detect the number of immunohistochemically positive cells and effectively assist doctors in survival prediction.

Objective To analyze the temporal trends of gout disezse burden in China from 1990 to 2021,and construct an age-period-cohort(APC)model to explore the independent effects of age,period,and birth cohort on epidemiological indicators,and predict the future burden of gout disease in China from 2022 to 2035.Methods Data on gout disease burden in China during 1990-2021 were extracted from the Global Burden of Disease(GBD)2021 database.Joinpoint regression analysis was used to assess temporal trends.The APC model was applied to evaluate the age,period and cohort effects on prevalence risk and disability-adjusted life years(DALYs).A Bayesian age-period-cohort(BAPC)model was employed to project the age-standardized prevalence rate(ASPR)and age-standardized DALY rate(ASDR)of gout in China from 2022 to 2035.Results From 1990 to 2021,the incidence,prevalence and DALYs of gout in China all increased substantially,with overall rising trends in the age-standardized incidence rate(ASIR),ASPR and ASDR.Compared with 1990,the incidence,prevalence and DALYs in 2021 increased by 160.45%,181.12%,and 175.93%,respectively,while their age-standardized rates increased by 23.74%,26.48%and 25.89%.Joinpoint regression analysis revealed that average annual percentage changes(AAPCs)of 0.73%for ASIR,0.82%for ASPR and 0.80%for ASDR during 1990-2021.In 2021,the number of cases and DALYs reached their peaks in males aged 55-59 years and females aged 65-69 years.Both prevalence and DALY rates increased steadily with age,with marked rises starting at age 30 in men and age 40 in women.Overall,males showed higher prevalence,DALYs and corresponding rates than those of females across all age groups.APC model results indicated that the age effect,period effect and cohort effects on prevalence and DALY rates presented an overall upward tread.Decomposition analysis showed that population aging contributed the most to the increase in incidence and DALYs from 1990 to 2021.BAPC projections suggested that by 2035,the ASPR and ASDR of gout in China reached 890.50 per 100,000 and 27.26 per 100,000,respectively.Conclusion The ASPR and ASDR of gout in China are projected to continue increasing from 2022 to 2035.Targeted public health strategies for high-risk populations are urgently needed to reduce the growing burden of gout.

Objective To analyze the temporal trends of gout disezse burden in China from 1990 to 2021,and construct an age-period-cohort(APC)model to explore the independent effects of age,period,and birth cohort on epidemiological indicators,and predict the future burden of gout disease in China from 2022 to 2035.Methods Data on gout disease burden in China during 1990-2021 were extracted from the Global Burden of Disease(GBD)2021 database.Joinpoint regression analysis was used to assess temporal trends.The APC model was applied to evaluate the age,period and cohort effects on prevalence risk and disability-adjusted life years(DALYs).A Bayesian age-period-cohort(BAPC)model was employed to project the age-standardized prevalence rate(ASPR)and age-standardized DALY rate(ASDR)of gout in China from 2022 to 2035.Results From 1990 to 2021,the incidence,prevalence and DALYs of gout in China all increased substantially,with overall rising trends in the age-standardized incidence rate(ASIR),ASPR and ASDR.Compared with 1990,the incidence,prevalence and DALYs in 2021 increased by 160.45%,181.12%,and 175.93%,respectively,while their age-standardized rates increased by 23.74%,26.48%and 25.89%.Joinpoint regression analysis revealed that average annual percentage changes(AAPCs)of 0.73%for ASIR,0.82%for ASPR and 0.80%for ASDR during 1990-2021.In 2021,the number of cases and DALYs reached their peaks in males aged 55-59 years and females aged 65-69 years.Both prevalence and DALY rates increased steadily with age,with marked rises starting at age 30 in men and age 40 in women.Overall,males showed higher prevalence,DALYs and corresponding rates than those of females across all age groups.APC model results indicated that the age effect,period effect and cohort effects on prevalence and DALY rates presented an overall upward tread.Decomposition analysis showed that population aging contributed the most to the increase in incidence and DALYs from 1990 to 2021.BAPC projections suggested that by 2035,the ASPR and ASDR of gout in China reached 890.50 per 100,000 and 27.26 per 100,000,respectively.Conclusion The ASPR and ASDR of gout in China are projected to continue increasing from 2022 to 2035.Targeted public health strategies for high-risk populations are urgently needed to reduce the growing burden of gout.

Objective To propose a novel method for immunohistochemically positive cell counting based on the improved YOLOv5s.Methods Regarding the small target characteristics of positive cells,a small target detection layer was added to refine feature extraction.Then,a bidirectional weighted feature pyramid network was used to replace path aggregation network(PANet)in the neck network for realizing multi-scale feature fusion.Additionally,the method used coordinate attention mechanism to make the model pay more attention to small target characteristics,and replaced the original GIoU with EIoU loss function for enhancing the detection performance.Results The model was trained on the self-built immunohistochemical image dataset.The average accuracy of the improved model was 89.3%,which was 4.0%higher than the original model and surpassed mainstream target detection models.The 5-year survival prediction model constructed with the method achieved an average accuracy of 76.8%and an average area under the curve of 0.81,demonstrating its superior prediction ability.Conclusion The proposed model can quickly detect the number of immunohistochemically positive cells and effectively assist doctors in survival prediction.

Glucagon-like peptide-1 (GLP-1) is a 30-amino acid peptide hormone that is mainly expressed in the intestine and hypothalamus. In recent years, basic and clinical studies have shown that GLP-1 is closely related to lipid metabolism, and it can participate in lipid metabolism by inhibiting fat synthesis, promoting fat differentiation, enhancing cholesterol metabolism, and promoting adipose browning. GLP-1 plays a key role in the occurrence and development of metabolic diseases such as obesity, nonalcoholic fatty liver disease, and atherosclerosis by regulating lipid metabolism. It is expected to become a new target for the treatment of metabolic disorders. The effects of GLP-1 and dual agonists on lipid metabolism also provide a more complete treatment plan for metabolic diseases. This article reviews the recent research progress of GLP-1 in lipid metabolism.

Objectives: This study aimed to explore the influence of depression severity, disease course, treatment status, and other factors on cognitive function in adolescents with depressive disorders. Methods: Participants who met the inclusion criteria were enrolled in the study. Sociodemographic data of each participant were recorded, including age, sex, and family history of mental disorders. Zung’s Self-Rating Depression Scale was used to assess depression status in adolescents. Moreover, P300 and mismatch negativity (MMN) were used to objectively evaluate the participants’ cognitive function. Results: Only 26.8% of the adolescents with depression received standard antidepressant treatment. The latencies of N2 (267.80±23.34 ms), P3 (357.71±32.09 ms), and MMN (212.10±15.61 ms) in the adolescent depression group were longer than those in the healthy control group (p<0.01). Further analysis revealed that the latency of MMN was extended with increased levels of depression in adolescents.The MMN latency was short in participants with depression receiving standardized treatment. Furthermore, the latency of MMN was positively correlated with the severity and duration of depression (correlation coefficients were 0.465 and 0.479, respectively) (p<0.01). Conclusion Receiving standardized treatment and shortening the course of depression can reduce cognitive impairment in adolescents with depression.

Objective:To explore the effects of early-life maternal deprivation on depressive-like behavior and neurogenesis in the granular layer of hippocampus in adolescent rats (6-7 weeks old).Methods:Neonatal rats were randomly divided into maternal deprivation group and control group, with 3 litters in each group.Rats in the maternal deprivation group were given maternal deprivation from 1 to 14 days after birth and rats in the control group were caged with the mother rats and raised normally.The body weight of rats at 5-6 weeks old was recorded and the increased body weight was calculated.When the rats were 6 weeks old, the sucrose preference test was carried out.Then the rats were killed and immunofluorescence histochemistry was applied to compare the expression of Ki67 and Nestin positive cells in the dentate gyrus of hippocampus.SPSS 22.0 software was used for statistical analysis.The data of the two groups were tested to conform to the normal distribution, and then t-test was carried out. Results:There was significant difference in body weight growth between the two groups at the age of 5-6 weeks.Compared with the control group, rats in the maternal deprivation group had lower body weight growth ((20.57±2.19) g, (30.57±1.25) g, t=3.96, P<0.01)) and lower sucrose preference rate((58.38±53.14)%, (73.88±3.67)%, t=3.21, P<0.01). The results of immunofluorescence showed that the number of Ki67 positive cells in the granular layer of hippocampus in the maternal deprivation group was less than that in the control group ((5.13±0.31), (7.60±0.38), t=5.09, P<0.01), and the number of Nestin immunofluorescence positive cells was more than that in the control group ((16.65±0.79), (7.64±0.70), t=8.51, P<0.01). The Nestin immunofluorescence positive cells in the maternal deprivation group had more protrusions and branches, and the morphology was similar to astrocytes, while the immunofluorescence positive cells in the control group had fewer protrusions, and the cell body was oval. Conclusions:Early-life maternal deprivation leads to depressive-like behavior in adolescent rats, which may be associated with the decrease of neurogenesis and activation of astrocytes in the dentate gyrus of the hippocampus.

Objective:To analyze the antibody levels and dynamic changes in patients infected with 2019-novel coronavirus(2019-nCoV).Methods:The average age of 72 corona virus disease 2019 (COVID-19) patients was (45.53±16.74)years(median age:47 year), including (44.88±17.09) years(median age:46 year) for 38 males and (46.32±16.52)years (median age:46 year) for 34 females in Loudi City, Hunan Province. There is no significant difference in genders between the severe and mild groups (χ2=0.916, P>0.05). There is a significant difference in the age between the severe and mild groups ( F=3.315, P<0.05). The blood samples of 72 discharged patients were collected and the consistence of IgM and IgG antibodies were detected by chemiluminescence method. SPSS25.0 was used for gender, age, case type and antibody analysis of variance, χ 2 test and other analysis. Results:The average time of the serum samples collection of 72 patients was (34.89±9.02)days (median time: 34 days) from onset of COVID-19, and (14.53±8.35) days (median time: 14 days) from discharge. The positive rate of IgM or IgG was 97.22% (70/72), and the positive rate of IgM and IgG was 48.61% (35/72) and 97.22% (70/72) respectively. Serum COVID-19 antibodies were detected in 72 patients from 1st to 40th days after discharge. The average concentration of IgM in 1-7 days, 8-14 days, 15-21 days, 22-28 days, above 29 days were 21.91(7.07-52.84)AU/ml, 14.16(6.19-32.88)AU/ml, 11.36(6.65-42.15)AU/ml, 8.15(3.66-30.12)AU/ml, 2.98(0.46-6.37)AU/ml. There was no significant difference in the time of IgM antibody concentration ( H= 8.439, P>0.05). The average concentrations of IgG in 1-7 days, 8-14 days, 15-21 days, 22-28 days, 29 days and above were 169.90 (92.06-190.91) AU/ml, 163.89 (91.19-208.02) AU/ml, 173.31 (95.06-191.28) AU/ml, 122.84 (103.19-188.34) AU/ml, 101.98 (43.75-175.30) AU/ml, respectively, ( H=2.232, P>0.05). The IgM becomes negative after the 3rd week of discharge and decreases rapidly with time. The IgG concentration higher than IgM during the same period, and keep at high level without any change, and decrease in the fourth week. Among them, 5 cases developed "re-infection" within 1-3 weeks after discharge, and the rate of "re-infection" was 6.94% (5/72 cases). Conclusions:After the COVID-19 patients are discharged from the hospital, the level of antibodies produced varies greatly among individuals, but the overall changes in antibodies have a certain pattern. It is recommended to strengthen the antibody monitoring during hospitalization and after discharge from the hospital to reduce the "re-infection" rate and potential risk of infection.

Objective:To analyze the antibody levels and dynamic changes in patients infected with 2019-novel coronavirus(2019-nCoV).Methods:The average age of 72 corona virus disease 2019 (COVID-19) patients was (45.53±16.74)years(median age:47 year), including (44.88±17.09) years(median age:46 year) for 38 males and (46.32±16.52)years (median age:46 year) for 34 females in Loudi City, Hunan Province. There is no significant difference in genders between the severe and mild groups (χ2=0.916, P>0.05). There is a significant difference in the age between the severe and mild groups ( F=3.315, P<0.05). The blood samples of 72 discharged patients were collected and the consistence of IgM and IgG antibodies were detected by chemiluminescence method. SPSS25.0 was used for gender, age, case type and antibody analysis of variance, χ 2 test and other analysis. Results:The average time of the serum samples collection of 72 patients was (34.89±9.02)days (median time: 34 days) from onset of COVID-19, and (14.53±8.35) days (median time: 14 days) from discharge. The positive rate of IgM or IgG was 97.22% (70/72), and the positive rate of IgM and IgG was 48.61% (35/72) and 97.22% (70/72) respectively. Serum COVID-19 antibodies were detected in 72 patients from 1st to 40th days after discharge. The average concentration of IgM in 1-7 days, 8-14 days, 15-21 days, 22-28 days, above 29 days were 21.91(7.07-52.84)AU/ml, 14.16(6.19-32.88)AU/ml, 11.36(6.65-42.15)AU/ml, 8.15(3.66-30.12)AU/ml, 2.98(0.46-6.37)AU/ml. There was no significant difference in the time of IgM antibody concentration ( H= 8.439, P>0.05). The average concentrations of IgG in 1-7 days, 8-14 days, 15-21 days, 22-28 days, 29 days and above were 169.90 (92.06-190.91) AU/ml, 163.89 (91.19-208.02) AU/ml, 173.31 (95.06-191.28) AU/ml, 122.84 (103.19-188.34) AU/ml, 101.98 (43.75-175.30) AU/ml, respectively, ( H=2.232, P>0.05). The IgM becomes negative after the 3rd week of discharge and decreases rapidly with time. The IgG concentration higher than IgM during the same period, and keep at high level without any change, and decrease in the fourth week. Among them, 5 cases developed "re-infection" within 1-3 weeks after discharge, and the rate of "re-infection" was 6.94% (5/72 cases). Conclusions:After the COVID-19 patients are discharged from the hospital, the level of antibodies produced varies greatly among individuals, but the overall changes in antibodies have a certain pattern. It is recommended to strengthen the antibody monitoring during hospitalization and after discharge from the hospital to reduce the "re-infection" rate and potential risk of infection.