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Abstract Background Influenza outbreaks have occurred frequently these years, especially in the summer of 2022 when the number of influenza cases in southern provinces of China increased abnormally. However, the exact evidence of the driving factors involved in the prodrome period is unclear, posing great difficulties for early and accurate prediction in practical work. Methods In order to avoid the serious interference of strict prevention and control measures on the analysis of influenza influencing factors during the COVID-19 epidemic period, only the impact of meteorological and air quality factors on influenza A (H3N2) in Xiamen during the non coronavirus disease 2019 (COVID-19) period (2013/01/01-202/01/24) was analyzed using the distribution lag non-linear model. Phylogenetic analysis of influenza A (H3N2) during 2013–2022 was also performed. Influenza A (H3N2) was predicted through a random forest and long short-term memory (RF-LSTM) model via actual and forecasted meteorological and influenza A (H3N2) values. Results Twenty nine thousand four hundred thirty five influenza cases were reported in 2022, accounting for 58.54% of the total cases during 2013–2022. A (H3N2) dominated the 2022 summer epidemic season, accounting for 95.60%. The influenza cases in the summer of 2022 accounted for 83.72% of the year and 49.02% of all influenza reported from 2013 to 2022. Among them, the A (H3N2) cases in the summer of 2022 accounted for 83.90% of all A (H3N2) reported from 2013 to 2022. Daily precipitation(20–50 mm), relative humidity (70–78%), low (≤ 3 h) and high (≥ 7 h) sunshine duration, air temperature (≤ 21 °C) and O3 concentration (≤ 30 µg/m3, > 85 µg/m3) had significant cumulative effects on influenza A (H3N2) during the non-COVID-19 period. The daily values of PRE, RHU, SSD, and TEM in the prodrome period of the abnormal influenza A (H3N2) epidemic (19–22 weeks) in the summer of 2022 were significantly different from the average values of the same period from 2013 to 2019 (P

Background: The structure of age groups and social contacts of the total population influenced infection scales and hospital-bed requirements, especially influenced severe infections and deaths during the global prevalence of COVID-19. Before the end of the year 2022, Chinese government implemented the national vaccination and had built the herd immunity cross the country, and announced Twenty Measures (November 11) and Ten New Measures (December 7) for further modifications of dynamic zero-COVID polity on the Chinese mainland. With the nation-wide vaccination and modified measures background, Fuzhou COVID-19 large wave (November 19, 2022–February 9, 2023) led by Omicron BA.5.2 variant was recorded and prevailed for three months in Fujian Province. Methods: A multi-age groups susceptible-exposed-infected-hospitalized-recovered (SEIHR) COVID-19 model with social contacts was proposed in this study. The main object was to evaluate the impacts of age groups and social contacts of the total population. The idea of Least Squares method was governed to perform the data fittings of four age groups against the surveillance data from Fujian Provincial Center for Disease Control and Prevention (Fujian CDC). The next generation matrix method was used to compute basic reproduction number for the total population and for the specific age group. The tendencies of effective reproduction number of four age groups were plotted by using the Epiestim R package and the SEIHR model for in-depth discussions. The sensitivity analysis by using sensitivity index and partial rank correlation coefficients values (PRCC values) were operated to reveal the differences of age groups against the main parameters. Results: The main epidemiological features such as basic reproduction number, effective reproduction number and sensitivity analysis were extensively discussed for multi-age groups SEIHR model in this study. Firstly, by using of the next generation matrix method, basic reproduction number R0 of the total population was estimated as 1.57 using parameter values of four age groups of Fuzhou COVID-19 large wave. Given age group k, the values of R0k (age group k to age group k), the values of R0k (an infected of age group k to the total population) and the values of R^0k (an infected of the total population to age group k) were also estimated, in which the explorations of the impacts of age groups revealed that the relationship R0k>R0k>R^0k was valid. Then, the fluctuating tendencies of effective reproduction number Rt were demonstrated by using two approaches (the surveillance data and the SEIHR model) for Fuzhou COVID-19 large wave, during which high-risk group (G4 group) mainly contributed the infection scale due to high susceptibility to infection and high risks to basic diseases. Further, the sensitivity analysis using two approaches (the sensitivity index and the PRCC values) revealed that susceptibility to infection of age groups played the vital roles, while the numerical simulation showed that infection scale varied with the changes of social contacts of age groups. The results of this study claimed that the high-risk group out of the total population was concerned by the local government with the highest susceptibility to infection against COVID-19. Conclusions: This study verified that the partition structure of age groups of the total population, the susceptibility to infection of age groups, the social contacts among age groups were the important contributors of infection scale. The less social contacts and adequate hospital beds for high-risk group were profitable to control the spread of COVID-19. To avoid the emergence of medical runs against new variant in the future, the policymakers from local government were suggested to decline social contacts when hospital beds were limited.

Abstract Objective At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue. Methods Daily data on influencing factors and influenza in Xiamen were divided into three parts: overall period (phase AB), non-COVID-19 epidemic period (phase A), and COVID-19 epidemic period (phase B). The association between influencing factors and influenza was analysed using generalized additive models (GAMs). The excess risk (ER) was used to represent the percentage change in influenza as the interquartile interval (IQR) of meteorology factors increases. The 7-day average daily influenza cases were predicted using the combination of bi-directional long short memory (Bi-LSTM) and random forest (RF) through multi-step rolling input of the daily multifactor values of the previous 7-day. Results In periods A and AB, air temperature below 22 °C was a risk factor for influenza. However, in phase B, temperature showed a U-shaped effect on it. Relative humidity had a more significant cumulative effect on influenza in phase AB than in phase A (peak: accumulate 14d, AB: ER = 281.54, 95% CI = 245.47 ~ 321.37; A: ER = 120.48, 95% CI = 100.37 ~ 142.60). Compared to other age groups, children aged 4–12 were more affected by pressure, precipitation, sunshine, and day light, while those aged ≥ 13 were more affected by the accumulation of humidity over multiple days. The accuracy of predicting influenza was highest in phase A and lowest in phase B. Conclusions The varying degrees of intervention measures adopted during different phases led to significant differences in the impact of meteorology factors on influenza and in the influenza prediction. In association studies of respiratory infectious diseases, especially influenza, and environmental factors, it is advisable to exclude periods with more external interventions to reduce interference with environmental factors and influenza related research, or to refine the model to accommodate the alterations brought about by intervention measures. In addition, the RF-Bi-LSTM model has good predictive performance for influenza.

The epidemiological characteristics and distributions of two epidemics in Fujian Province of Southeast China were attributed to the complex interactions among variant, host, and non-pharmaceutical interventions (NPIs). All reported cases in the Putian epidemic (September 8–October 2, 2021, Delta variant B.1.617.2) and Fuzhou epidemic (October 22–November 18, 2022, Omicron variant BA.5.2) were classified by sex, age group, occupation, and location in this study. Using surveillance data from the Fujian Provincial Center for Disease Control and Prevention, we established a virus-oriented SVEIR (Susceptible–Vaccinated–Exposed–Infected–Recovered) model to investigate the dynamic evolution features of these two variants and the effects of NPIs. The optimal simulations were carried out with variants and scenario investigations. The scenario investigations showed that NPIs significantly reduced the transmission risk and infection scales of COVID-19, and that the Omicron variant was more infectious than the Delta variant. Moreover, the dynamic investigations revealed the increasing tendencies from Delta to Omicron, such as the basic reproduction number, infection rate, percentage of high-risk cases, and the growth rate. Decreasing tendencies were also identified, such as the average recovery period, the awareness delay, and the percentage of symptomatic cases. This study highlighted that NPIs played critical roles in successfully containing the two epidemics. Such interventions are strongly recommended to public health policymakers.

A vector–host model of dengue with multiple stages and independent fluctuations is investigated in this paper. Firstly, the existence and uniqueness of the positive solution are shown by contradiction. When the death rates of aquatic mosquitoes, adult mosquitoes, and human beings respectively control the intensities of white noises, and if R0s>1, then the persistence in the mean for both infective mosquitoes and infective human beings is derived. When R0s>1 is valid, the existence of stationary distribution is derived through constructing several appropriate Lyapunov functions. If the intensities of white noises are controlled and φ<0 is valid, then the extinction for both infective mosquitoes and infective human beings is obtained by applying the comparison theorem and ergodic theorem. Further, the main findings are verified through numerical simulations by using the positive preserving truncated Euler–Maruyama method (PPTEM). Moreover, several numerical simulations on the infection scale of dengue in Fuzhou City were conducted using surveillance data. The main results indicate that the decrease in the transfer proportion from aquatic mosquitoes to adult mosquitoes reduces the infection scale of infective human beings with dengue virus, and the death rates of aquatic mosquitoes and adult mosquitoes affect the value of the critical threshold R0s. Further, the controls of the death rates of mosquitoes are the effective routes by the decision-makers of the Chinese mainland against the spread of dengue.

The differences of SARS-CoV-2 variants brought the changes of transmission characteristics and clinical manifestations during the prevalence of COVID-19. In order to explore the evolution mechanisms of SARS-CoV-2 variants and the impacts of variant evolution, the classic SIR (Susceptible-Infected-Recovered) compartment model was modified to a generalized SVEIR (Susceptible-Vaccinated-Exposed-Infected-Recovered) compartment model with age-group and varying variants in this study. By using of the SVEIR model and least squares method, the optimal fittings against the surveillance data from Fujian Provincial Center for Disease Control and Prevention were performed for the five epidemics of Fujian Province. The main epidemiological characteristics such as basic reproduction number, effective reproduction number, sensitivity analysis, and cross-variant scenario investigations were extensively investigated during dynamic zero-COVID policy. The study results showed that the infectivities of the variants became fast from wild strain to the Delta variant, further to the Omicron variant. Meanwhile, the cross-variant investigations showed that the average incubation periods were shortened, and that the infection scales quickly enhanced. Further, the risk estimations with the new variants were performed without implements of the non-pharmaceutical interventions, based on the dominant variants XBB.1.9.1 and EG.5. The results of the risk estimations suggested that non-pharmaceutical interventions were necessary on the Chinese mainland for controlling severe infections and deaths, and also that the regular variant monitors were still workable against the aggressive variant evolution and the emergency of new transmission risks in the future.

Abstract Background This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence. Method A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions. Results Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low ( 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate. Conclusion This study’s LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.

BackgroundOther infectious diarrhea (OID) continues to pose a significant public health threat to all age groups in Fujian Province. There is a need for an in-depth analysis to understand the epidemiological pattern of OID and its associated risk factors in the region. ObjectiveIn this study, we aimed to describe the overall epidemic characteristics and spatiotemporal pattern of OID in Fujian Province from 2005 to 2021 and explore the linkage between sociodemographic and environmental factors and the occurrence of OID within the study area. MethodsNotification data for OID in Fujian were extracted from the China Information System for Disease Control and Prevention. The spatiotemporal pattern of OID was analyzed using Moran index and Kulldorff scan statistics. The seasonality of and short-term impact of meteorological factors on OID were examined using an additive decomposition model and a generalized additive model. Geographical weighted regression and generalized linear mixed model were used to identify potential risk factors. ResultsA total of 388,636 OID cases were recorded in Fujian Province from January 2005 to December 2021, with an average annual incidence of 60.3 (SD 16.7) per 100,000 population. Children aged

Abstract Background Influenza epidemics pose a threat to human health. It has been reported that meteorological factors (MFs) are associated with influenza. This study aimed to explore the similarities and differences between the influences of more comprehensive MFs on influenza in cities with different economic, geographical and climatic characteristics in Fujian Province. Then, the information was used to predict the daily number of cases of influenza in various cities based on MFs to provide bases for early warning systems and outbreak prevention. Method Distributed lag nonlinear models (DLNMs) were used to analyse the influence of MFs on influenza in different regions of Fujian Province from 2010 to 2021. Long short-term memory (LSTM) was used to train and model daily cases of influenza in 2010–2018, 2010–2019, and 2010–2020 based on meteorological daily values. Daily cases of influenza in 2019, 2020 and 2021 were predicted. The root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to quantify the accuracy of model predictions. Results The cumulative effect of low and high values of air pressure (PRS), air temperature (TEM), air temperature difference (TEMD) and sunshine duration (SSD) on the risk of influenza was obvious. Low ( 112 hPa) PRS were associated with a higher risk of influenza in women, children aged 0 to 12 years, and rural populations. Low ( 23 °C) TEM were risk factors for influenza in four cities. Wind speed (WIN) had a more significant effect on the risk of influenza in the ≥ 60-year-old group. Low ( 80%) relative humidity (RHU) in Fuzhou and Xiamen had a significant effect on influenza. When PRS was between 1005–1015 hPa, RHU > 60%, PRE was low, TEM was between 10–20 °C, and WIN was low, the interaction between different MFs and influenza was most obvious. The RMSE, MAE, MAPE, and SMAPE evaluation indices of the predictions in 2019, 2020 and 2021 were low, and the prediction accuracy was high. Conclusion All eight MFs studied had an impact on influenza in four cities, but there were similarities and differences. The LSTM model, combined with these eight MFs, was highly accurate in predicting the daily cases of influenza. These MFs and prediction models could be incorporated into the influenza early warning and prediction system of each city and used as a reference to formulate prevention strategies for relevant departments.

BackgroundSchizophrenia (SCZ) is a serious chronic mental disorder. Our previous case–control genetic association study has shown that microRNA-137 (miR-137) may only protect females against SCZ. Since estrogen, an important female sex hormone, exerts neuroprotective effects, the relationship between estrogen and miR-137 in the pathophysiology of SCZ was further studied in this study.MethodsGenotyping of single-nucleotide polymorphism rs1625579 of miR-137 gene in 1,004 SCZ patients and 896 healthy controls was conducted using the iMLDR assay. The effect of estradiol (E2) on the miR-137 expression was evaluated on the human mammary adenocarcinoma cell line (MCF-7) and the mouse hippocampal neuron cell line (HT22). The relationships between serum E2, prolactin (PRL), and peripheral blood miR-137 were investigated in 41 SCZ patients and 43 healthy controls. The miR-137 and other reference miRNAs were detected by real-time fluorescent quantitative reverse transcription-PCR.ResultsBased on the well-known SNP rs1625579, the distributions of protective genotypes and alleles of the miR-137 gene were not different between patients and healthy controls but were marginally significantly lower in female patients. E2 upregulated the expression of miR-137 to 2.83 and 1.81 times in MCF-7 and HT22 cells, respectively. Both serum E2 and blood miR-137 were significantly decreased or downregulated in SCZ patients, but they lacked expected positive correlations with each other in both patients and controls. When stratified by sex, blood miR-137 was negatively correlated with serum E2 in female patients. On the other hand, serum PRL was significantly increased in SCZ patients, and the female patients had the highest serum PRL level and a negative correlation between serum PRL and blood miR-137.ConclusionThe plausible SCZ-protective effect of miR-137 may be female specific, of which the underlying mechanism may be that E2 upregulates the expression of miR-137. This protective mechanism may also be abrogated by elevated PRL in female patients. These preliminary findings suggest a new genetic/environmental interaction mechanism for E2/miR-137 to protect normal females against SCZ and a novel E2/PRL/miR-137-related pathophysiology of female SCZ, implying some new antipsychotic ways for female patients in future.

Gestational diabetes mellitus (GDM) is a metabolic disease with an increasing annual incidence. Our previous observational study found that pregnant women with gestational diabetes had mild cognitive decline, which may be related to methylglyoxal (MGO). This study aimed to investigate whether labor pain aggravates the increase in MGO and explored the protective effect of epidural analgesia on metabolism in pregnant women with GDM based on solid-phase microextraction gas chromatography/mass spectrometry (SPME/GC-MS). Pregnant women with GDM were divided into a natural birth group (ND group, n = 30) and epidural analgesia group (PD group, n = 30). After fasting for ≥ 10 h overnight, venous blood samples were collected pre- and post-delivery to detect MGO, interleukin-6 (IL-6), and 8-epi-prostaglandin F2 alpha (8-iso-PGF2α) by ELISA. Serum samples were analyzed for volatile organic compounds (VOCs) using SPME-GC-MS. MGO, IL-6, and 8-iso-PGF2α levels in the ND group increased significantly post-delivery (P < 0.05) and were significantly higher in this group than the levels in the PD group (P < 0.05). Compared to the PD group, VOCs in the ND group increased significantly post-delivery. Further results indicated that propionic acid may be associated with metabolic disorders in pregnant women with GDM. Epidural analgesia can effectively improve the metabolism and immune function in pregnant women with GDM.

Background: Ferroptosis has a vital role in sepsis, but the mechanism is not known. Understanding the mechanism of ferroptosis during sepsis will aid in developing improved therapeutic strategies.Methods: We used the Gene Expression Omnibus database and FerrDb database to obtain ferroptosis-related differentially expressed genes (DEGs) between sepsis patients and healthy volunteers (HVs). Analyses of PPI networks, functional enrichment, as well as use of the MCODE algorithm were used to identify key ferroptosis-related DEGs. Expression of key ferroptosis-related DEGs was verified using: GSE57065 and GSE65682 datasets; rats in which ferroptosis was induced with erastin; sepsis-induced acute lung injury (siALI) rats. The effects of acupoint catgut embedding (ACE) on ferroptosis and expression of key ferroptosis-related DEGs in the lungs of siALI rats were also observed. A Cox proportional hazard model was used to verify the effect of key ferroptosis-related DEGs on the survival of sepsis patients. Cytoscape was used to construct ceRNA networks and gene–transcription factor networks.Results: Between sepsis patients and HVs, we identified 33 ferroptosis-related DEGs. According to analyses of PPI networks and the MCODE algorithm, we obtained four modules, of which the most significant module contained nine ferroptosis-related DEGs. Functional-enrichment analyses showed that four of the nine DEGs were enriched in the MAPK signaling pathway: MAPK14, VEGFA, TGFBR1, and DUSP1. We verified expression of these four genes in GSE57065 and GSE65682 datasets and ferroptosis rats. In addition, expression of these four genes and that of the oxidative-stress indicators GSSG and MDA was upregulated, and glutathione peroxidase-4 (GPX4) expression was downregulated, in siALI rats, but ACE reversed these changes. The Cox proportional hazard model showed that survival of sepsis patients in the high-risk group was shorter than that in the low-risk group. We found that the XIST−hsa-let-7b-5p−TGFBR1/DUSP1 ceRNA network and transcription factor E2F1 may be important regulators of these four DEGs.Conclusion: Our results suggest that MAPK14, VEGFA, TGFBR1, and DUSP1 may be key regulatory targets of ferroptosis in sepsis, and that ACE pretreatment may be antioxidant treatment for sepsis and alleviate ferroptosis. These findings provide a basis for further ferroptosis-related study in sepsis and provide new targets for its treatment.

Abstract Background Protein phosphatase 4 regulatory subunit 1 (PP4R1) is one of the regulatory subunits of PP4. It has been determined to be involved in the regulation of TNF-α-induced hepatic insulin resistance and gluconeogenesis. Considering the important role of PP4R1 in hepatic insulin resistance, the current study explored the expression and diagnostic value of PP4R1 in type 2 diabetes mellitus (T2DM) patients and further investigated its predictive value for the development of chronic complications. Method Hundred and five patients with T2DM and 97 healthy controls were collected. qRT-PCR was used for the measurement of serum PP4R1 mRNA level in both T2DM and control groups. The diagnostic value of PP4R1 in T2DM patients was evaluated using receiver operating characteristic (ROC) curve. Kaplan-Meier methods and Cox regression analysis were used to evaluate the predictive value of PP4R1 for the development of chronic complications in T2DM patients. Results PP4R1 was determined to be elevated in the serum of T2DM patients compared with healthy controls. Serum PP4R1 had the potential to distinguish T2DM patients from healthy controls with a sensitivity of 81.9% and specificity of 82.5%. Patients with high PP4R1 expression experienced more chronic complications events. The multivariate Cox analysis results suggested that serum PP4R1 expression was an independent factor for the occurrence of chronic complications for T2DM patients. Conclusion PP4R1 is elevated in the serum of T2DM patients, had the potential to distinguish T2DM patients from healthy controls. PP4R1 serves as a promising biomarker for predicting the risk of future chronic complications in T2DM patients.

Woodhouse-Sakati syndrome (WSS, MIM 241080) is a rare neuroendocrine disease characterized by hair loss, hypogonadism, diabetes, hearing loss, and extrapyramidal syndrome, and is usually caused by mutations in the DCAF17 gene as an inherited disease. DCAF17 plays an important role in mammalian gonadal development and infertility. So far, there have been no WSS reports in China. The patient introduced in this case is from a consanguineous family. The main symptoms of the patient were alopecia and gonadal agenesis. Other symptoms such as hearing loss, intellectual disability, and hyperglycemia were remarkable, and these symptoms are often observed in WSS patients. We found a nonsense mutation in the 11th exon of the gene DCAF17 (Refseq: NM_025000) in the patient and her younger brother, which confirmed the diagnosis of WSS. The genetic results also showed that the mutation was inherited from their healthy first-cousin parents.

Crop yield mainly depends on environment and cultivation practices that vary according to a growing environment. However, an oat (Avena sativa L.)-common vetch (Vicia sativa L.) intercrop system has not been fully developed in the agro-pastoral ecotone of Inner Mongolia, China. This study evaluated the effects of seven treatments, including five oat-common vetch intercropping patterns, sole oat, and sole vetch on yield and quality performance at different growth periods [75 days after sowing (DAS), 90 DAS, 105 DAS], on the basis of field experiments conducted in the agro-pastoral ecotone of Inner Mongolia in 2015 and 2016. The OV3:1 (oat intercropped with common vetch at seeding ratios 3:1) treatment at 105 DAS in 2016 was superior to other treatments, as it achieved the highest shoot dry matter, increasing by 24.1% and 37.1% compared to sole oat and common vetch. The crude fat (CF) contents, CF yield, and crude protein (CP) yield increased under OV3:1, and acid detergent fiber (ADF) decreased under OV3:1, compared to monoculture. The results indicate that intercropping is an efficient cropping system for the agro-pastoral ecotone of Inner Mongolia. The appropriate proportion of oat and common vetch intercropping at 3:1 and harvesting time not only increases crop yield but also improves the crop quality.

Purpose Various treatments have been investigated for Graves' ophthalmopathy (GO). We aimed to provide an overall profile of the efficacy and tolerability of various interventions for active and moderate to severe GO. Methods PubMed, Embase and the Cochrane Central Register of Controlled Trials were searched on 6 July 2018. Randomised controlled trials (RCT) investigating GO treatments were included. Two researchers independently extracted data according to a predefined form. A random effects network meta-analysis was performed using a frequentist approach. The primary outcome was efficacy, and the secondary outcome was tolerability (side effect discontinuation). Results Thirty-three studies with 1846 patients with GO were included. Orbital radiotherapy (ORT) plus intravenous glucocorticoids (IVGC) (OR 27.11; 95% CI 4.57 to 160.92), mycophenolate mofetil (MMF) (24.40, 95% CI 5.28 to 112.67), oral glucocorticoids (OGC) plus ciclosporin (20.22, 95% CI 1.60 to 255.20), IVGC plus MMF (12.08, 95% CI 2.96 to 49.35), teprotumumab (8.92, 95% CI 2.51 to 31.77), ORT plus OGC (4.88, 95% CI 1.25 to 19.06), rituximab (RTX) (4.85, 95% CI 1.18 to 19.86), somatostatin analogues (4.23, 95% CI 1.60 to 11.16), OGC plus azathioprine (AzA) (5.77, 95% CI 1.17 to 28.47) and IVGC (4.96, 95% CI 1.96 to 12.55) showed significantly better improvement than no treatment. ORT plus IVGC ranked first, followed by MMF. High heterogeneity and significant local inconsistency were observed in the RTX studies. The results of the sensitivity analyses were similar to those of the main analysis. Conclusion A robust recommendation regarding the best treatment cannot be made, because most evidence was rated as low or very low quality according to the Grading of Recommendations, Assessment, Development and Evaluations framework. Large RCTs and individual participant data meta-analyses are necessary to confirm these results and explore potential moderators. [ABSTRACT FROM AUTHOR]

This study was conducted to evaluate the effect of pyridoxine on the development of hair follicles in Rex rabbits and the underlying molecular mechanism. Two hundred 3-month-old Rex rabbits were randomly divided into 5 groups and fed diets supplemented with 0, 5, 10, 20, or 40 mg/kg pyridoxine. The hair follicle density on the dorsal skin and the gene and protein expression levels of components of the phosphoinositide 3-kinase (PI3K)/protein kinase B (PKB or Akt), Wnt, Notch and bone morphogenetic protein (BMP) signalling pathways were measured. In addition, free hair follicles were isolated from Rex rabbits and cultured with pyridoxine in vitro to measure hair shaft growth. Furthermore, dermal papilla cells (DPC) were isolated from the skin of Rex rabbits and cultured with pyridoxine in vitro to measure the gene and protein expression levels of components of the PI3K/Akt, Wnt, Notch and BMP signalling pathways. The results showed that the addition of dietary pyridoxine significantly increased the total follicle density, secondary follicle density, and secondary-to-primary ratio (S/P, P

The beef cattle rumen is a heterogenous microbial ecosystem that is necessary for the host to digest food and support growth. The importance of the rumen microbiota (RM) is also widely recognized for its critical roles in metabolism and immunity. The level of health is indicated by a dynamic RM distribution. We performed high-throughput sequencing of the bacterial 16S rRNA gene to compare microbial populations between rumens in beef cattle with or without doxycycline treatment to assess dynamic microbiotic shifts following antibiotic administration. The results of the operational taxonomic unit analysis and alpha and beta diversity calculations showed that doxycycline-treated beef cattle had lower species richness and bacterial diversity than those without doxycycline. Bacteroidetes was the predominant phylum in rumen samples without doxycycline, while Proteobacteria was the governing phylum in the presence of doxycycline. On the family level, the top three predominant populations in group qlqlwy (not treated with doxycycline) were Prevotellaceae, Lachnospiraceae, and Ruminococcaceae, compared to Xanthomonadaceae, Prevotellaceae, and Rikenellaceae in group qlhlwy (treated with doxycycline). At the genus level, the top predominant population in group qlqlwy was unidentified_Prevotellaceae. However, in group qlhlwy, the top predominant population was Stenotrophomonas. The results revealed significant RM differences in beef cattle with or without doxycycline. Oral doxycycline may induce RM composition differences, and bacterial richness may also influence corresponding changes that could guide antibiotic use in adult ruminants. This study is the first to assess microbiota distribution in beef cattle rumen after doxycycline administration.