HI) remains a major cause of foetal brain damage presented a scarcity of effective therapeutic approaches. Dexmedetomidine (DEX) and microRNA‐140‐5p (miR‐140‐5p) have been highlighted due to its potentially significant role in the treatment of cerebral ischaemia. This study was to investigate the role by which miR‐140‐5p provides cerebral protection using DEX to treat hypoxic–ischaemic brain damage (HIBD) in neonatal rats via the Wnt/β‐catenin signalling pathway. The HIBD rat models were established and allocated into various groups with different treatment plans, and eight SD rats into sham group. The learning and memory ability of the rats was assessed. Apoptosis and pathological changes in the hippocampus CA1 region and expressions of the related genes of the Wnt/β‐catenin signalling pathway as well as the genes responsible of apoptosis were detected. Compared with the sham group, the parameters of weight, length growth, weight ratio between hemispheres, the rate of reaching standard, as well as Bcl‐2 expressions, were all increased. Furthermore, observations of increased levels of cerebral infarction volume, total mortality rate, response times, total response duration, expressions of Wnt1, β‐catenin, TCF‐4, E‐cadherin, apoptosis rate of neurons, and Bax expression were elevated. Following DEX treatment, the symptoms exhibited by HIBD rats were ameliorated. miR‐140‐5p and si‐Wnt1 were noted to attenuate the progression of HIBD. Our study demonstrates that miR‐140‐5p promotes the cerebral protective effects of DEX against HIBD in neonatal rats by targeting the Wnt1 gene through via the negative regulation of the Wnt/β‐catenin signalling pathway.Hypoxia–ischaemia ( All animal experiments were conducted under the ethical approval of the Institutional Review Board (IRB) of Key Laboratory for Biotechnology on Medicinal Plants of Jiangsu Province, School of Life Science, Jiangsu Normal University.2.2A total of 175 neonatal Sprague Dawley (SD) rats  both male and female, obtained from the Laboratory Animal Center of Key Laboratory for Biotechnology on Medicinal Plants of Jiangsu Province, School of Life Science, Jiangsu Normal University, were housed in a controlled environmental setting with regulated temperatures (20‐25°C), humidity (40%‐70%) and indoor wind speed (0.1‐0.2 m/s). HI was conducted in the neonatal rats. The specific steps of HIBD model 2.3A total of eight neonatal SD rats, aged 1 week, free of HI were randomly selected as sham group that were provided by the Laboratory Animal Center of Key Laboratory for Biotechnology on Medicinal Plants of Jiangsu Province, School of Life Science, Jiangsu Normal University. The 169 HIBD rat models were assigned randomly into seven groups, namely the model group (eight rats), DEX group , miR‐140‐5p mimic (23 rats), DEX + negative control (NC) group (23 rats), DEX + miR‐140‐5p mimic group (23 rats), DEX + miR‐140‐5p inhibitor group (23 rats), DEX + si‐Wnt1 group  and DEX + miR‐140‐5p inhibitor + si‐Wnt1 group (23 rats). The treatment administered to each group was as follows: a small incision was made to the neck region of rats in the sham group without ligation, and the rats were provided with normal air for ventilation after suturing the incision, followed by an injection of 100 μL normal saline (0.1 mg/kg). The remaining groups were all treated according to the HIBD model establishment. In the DEX group, the HIBD model rats were injected intraperitoneally with 100 μL DEX  immediately after HI. In the miR‐140‐5p mimic group, rats were injected intravenously with 50 nmol/L miR‐140‐5p mimic immediately after HI. In the DEX + NC group, rats were injected intraperitoneally with 100 μL DEX (0.1 mg/kg), injected intravenously with the mixture (100 μL) of miR‐140‐5p mimic NC  and Lipo 2000 (20 nmol/L) immediately after HI. In the DEX + miR‐140‐5p mimic group, rats were intraperitoneally injected with 100 μL DEX (0.1 mg/kg) and injected intravenously with the mixture (100 μL) of miR‐140‐5p mimic and Lipo 2000 (20 nmol/L) immediately after HI. In the DEX + miR‐140‐5p inhibitor group, rats were intraperitoneally injected with 100 μL DEX (0.1 mg/kg), injected intravenously with the mixture (100 μL) of miR‐140‐5p inhibitor  and Lipo 2000 (20 nmol/L) immediately after HI. In the DEX + si‐Wnt1 group, the rats were injected intraperitoneally with 100 μL DEX (0.1 mg/kg), injected intravenously with the mixture of si‐Wnt1 (100 μmol/L) and Lipo 2000 (20 nmol/L) immediately after HI. In the DEX + miR‐140‐5p inhibitor + si‐Wnt1 group, rats were injected intraperitoneally with 100 μL DEX (0.1 mg/kg), injected intravenously with the mixture (100 μL) of miR‐140‐5p inhibitor (50 nmol/L), si‐Wnt1 (100 μmol/L) and Lipo 2000 (20 nmol/L) immediately after HI. In accordance with the aforementioned procedure, a series of injections were administered to each group, once a day for 3 days. Weight and growth rates were recorded.2.4The respective learning and memory abilities of rats were detected using the Y maze method. After 3 days, five surviving rats were randomly selected from the sham and model groups, while 10 surviving rats were selected from other groups. While being assessed, the phenomenon that rats directly jumped to the safety zone after receiving electric shock was regarded to be the correct response, and the opposite was regarded as a wrong response. When nine correct responses occurred after 10 consecutive electric shocks, the rats were presumed to have achieved the “learnt” standard. The test was conducted again to reobserve memory ability after 24 hour. If rats did not reach the standard in the first or second test, no other statistical analyses were conducted except the rate of reaching standard. The specific standards were described as follows: (i) the rate of reaching standard: each training session was limited to 60 sequences and that learning well within 60 sequences indicating the rats had reached the standard; (ii) the response times before reaching the standard; (iii) the total response time was regarded as the sum of time in each response, which was calculated from the beginning of signal lamp to rats entering the safety zone; (iv) the response rate of active avoidance meant the percentage of response times in 5 second of the total response times; (5) the rate of correct response meant the percentage of correct response times and the total response times.2.5Three surviving rats were randomly selected from the sham and model groups, and ten surviving rats were selected from the other remaining groups . Next, the rats were decapitated and their brains were obtained to observe the morphological changes of the left hemisphere with the naked eye. The left and right hemispheres of rats were then cut open through the midline. The wet weights of the hemispheres were recorded before baking in the oven at 80°C, and after 24 hour had elapsed, the dry weights were recorded. The degree of atrophy in the left hemisphere was determined by the wet weight ratio of left hemisphere to the right hemisphere, while the degree of oedema was determined by the water content rate. The water content rate was calculated according to the following formula (%) = (wet weight – dry weight)/wet weight × 100.2.6Sections of hippocampus tissues in each group were selected and placed into a newly prepared 2% TTC solution, followed by an incubation period in water bath without light exposure at 37°C for 30 minutes. After the colour had completely developed, the sections were fixed in 10% formaldehyde for a minimum of 24 hour and stored in the dark. The normal tissues were red in colour after TTC staining, and the infarcted tissues appeared white. The fixed brain slices were photographed, and the photographs were uploaded to the computer. The images were analysed using the automatic image analysis system (IBSA 2.7). The approximate volume of the cerebral infarction was =  × 100%.2.7Three surviving rats were randomly selected from the sham and model groups, while ten surviving rats were selected from the other groups, respectively. After etherization, thoracotomy was conducted, with 4°C normal saline and 4% paraformaldehyde perfused. The rats were then decapitated and their brains were obtained, cut through the midpoint of the frontal lobe and optic chiasma, followed by an incision into the optic chiasma. The samples were stored, fixed in 10% neutral formaldehyde solution overnight and embedded in paraffin. Next, the sections were sliced sequentially, and HE staining was conducted to observe cytomorphology under a light microscopy. The remaining slices were reserved for further use. The procedures for HE staining were conducted according to the following procedure. The sections were dewaxed twice in xylene (5‐15 min for each time) and dehydrated in anhydrous ethanol  twice. After rinsing using tap water for 3 min, the sections were stained with haematoxylin for 8 minute. After rinsing using tap water for 5 min, the sections were stained using eosin solution for a period of 2 minute, and then routinely dehydrated, cleaned and mounted in neutral balsam. The cytomorphological changes were observed under a microscope with high magnification.2.8The section samples in each group were collected. Nissl staining was conducted after the sections were dewaxed and rehydrated. The sections were stained using 0.1% toluidine blue at 60°C for 1 min and then washed under running water. Next, the sections were dehydrated and dissimilated in 95% ethanol under a microscope, followed by dehydration, permeabilizing and sealing. Numbers of neuron (per unit) in the hippocampal CA1 region were counted under a light microscope (×400).2.92O2 at room temperature and washed with PBS three times. Converting peroxidase solution was then added to the sections and incubated at 37°C for 30 minute, followed by three PBS washes. The sections were developed by diaminobenzidine and washed again with PBS. Later the nuclei in sections were recoloured by haematoxylin and sections were dehydrated by gradient ethanol, followed by clearing with xylene and sealing with neutral gum. Cells with a brown nucleus were considered to be positive cells (which were observed under a light microscope), and the nuclei of the positive cells as well as the morphology of the cytoplasm were observed and recorded.The sections of rat hippocampus tissues in each group were collected and TUNEL staining was conducted based on the instructions of the TUNEL kit . Paraffin‐embedded sections were baked at 60°C for 15 minute. The sections were dewaxed with xylene and then rehydrated using gradient ethanol. The sections were treated with proteinase K for 10 min and washed with PBS three times. TUNEL reacted solution was then prepared (TdT: mixture of nucleic acid = 1:9) and added to the sections for incubation at 37°C for 1 hour. The sections were treated with methanol containing 3% H2.10−ΔΔCt method was used to indicate the relative expression multiples of mRNA.Total RNA was extracted from a set of sections of brain sample and 293FT cells using the single‐step TRIzol method. RNase‐free water was used to dissolve RNA, and ND‐1000 ultraviolet–visible spectrophotometer  was applied to measure optical destiny (OD) value, in an attempt to determine the quality of the total RNA and to adjust the RNA concentration. The extracted RNA was reversely transcribed, and the reaction conditions were as follows: incubation at 70°C for 10 minute, ice bath for 2 minute, incubation at 42°C for 60 minute, at 70°C for 10 minute. The reversely transcribed cDNA were temporarily stored in a refrigerator at −20°C. The RT‐qPCR prime sequences are displayed in Table 2.11Hippocampus sections of rats in each group or of the proteins extracted from the 293FT cells were selected, followed by the addition of 1× sodium dodecyl sulphate (SDS) lysate. The protein extraction was heated at 100°C for 5 minute. Next, 20 μL samples were uploaded onto 12% polyacrylamide gel for electrophoresis. β‐catenin protein was extracted from cytoplasm and karyon according to instructions of Nuclear and Cytoplasmic Protein Extraction kit . After transferred to membrane, Tris‐buffered saline with Tween 20 (TBST) containing 5% bovine serum albumin (BSA) was supplemented to block proteins for 1 hour on a decolourizing shaking table. Following the removal of the blocking solution, the membrane was placed into a plastic groove, prepared with 5% BSA, Bcl‐2 antibody , Bax antibody , cleaved caspase‐3 antibody , Wnt1 protein antibody , β‐catenin protein antibody , E‐cadherin protein antibody , TCF‐4 protein antibody  and β‐actin  were supplemented and placed in a refrigerator at 4°C overnight with oscillation, during which the transferring surface was upward. The next day, TBST was used 3 times for washing purposes (10 minute per wash). Diluted secondary antibodies  were added for an incubation period of 4‐6 hour at 4°C according to the same procedures. After the membranes were washed using TBST three times (15 minute per wash), chemiluminescence reagents: solution A and solution B , were mixed at a ratio of 1:1. Following this, the membranes were completely immersed in the chromogenic reagent for 1 minute. After an extra chromogenic reagent was blotted using filter paper, the membranes were placed evenly into a cassette to avoid bubble formation. The sections were placed onto the membrane devoid of light. The membranes were pressed for 30 seconds — 40 minute and adjusted according to the lightness. X‐ray film was taken out to develop for 20 seconds and then precipitated for 20 seconds, followed by washing and photographing. β‐catenin protein concentration of endochylema and karyon was measured by bicinchoninic acid (BCA) protein assay kit . Other protein bands of Western blot analysis were analysed by means of gel electrophoresis imaging system  on relative optical density (OD), and β‐actin protein was used to correct the differences in protein loading and/or transfer between the groups.2.122O2 for 15 minute and washed three times with 0.01 mol/L PBS (10 minute for each time) again. The sections were then incubated in 0.3% Triton X‐100 solution for 30 minute, and then the primary antibody β‐catenin was added into sections , followed by incubating at 37°C for 2 hour. The sections were then allowed to stand in 4°C for 16‐24 hour and washed three times with 0.01 mol/L PBS (10 minute for each time). The secondary fluorescent antibody labelled by horseradish peroxidase (HRP) (ab6721) was added into the sections and then incubated at 37°C for 2 hour while the remaining sections stood at room temperature for 2‐4 hour. The sections were then washed three times with 0.01 mol/L PBS (10 minute for each time). After being fixed with 50% glycerol, the sections were observed under a fluorescence microscope and photographed. All groups had their respective corresponding negative control group, and sections in negative control groups were not added to the primary antibody. The remaining steps were the same; however, there was no positive control for each group.A set of rat hippocampus sections was collected and frozen (20 pm). The sections were floated in 4% paraformaldehyde fixation and then washed three times with 0.01 mol/L PBS (10 minute for each time). The sections were treated with 0.3% H2.13Wnt1 was determined as the potential target gene of miR‐140‐5p, based on the target gene prediction analysis using the TargetScan prediction algorithm  in addition to other existing researches.2.144 cells per well). When the cells had grown to between 80% and 90% confluency, they were transfected using Lipo 2000 reagent . The template DNA  and Lipo 2000 (0.5 μL/well) were mixed with 25 μL culture medium, respectively. After being left standing for a period of 15 minute, the mixture was added to the plate. The, cells were then assigned into six groups: the Wntl‐3′UTR‐WT (blank control + WT), Wntl‐3′UTR‐MT (blank control + MT), miR‐140‐5p mimic + Wnt1‐WT, mimic control + Wnt1‐WT, miR‐140‐5p mimic + Wnt1‐MT, mimic control + Wnt1‐WT groups. According to the cell grouping, corresponding miR‐140‐5p, plasmid vector, lipo2000 and internal control plasmid pRL‐TK were mixed gently and incubated at room temperature for 20 minute. The aforementioned mixture was added to the culture wells containing both cells and the medium, followed by gentle mixing. After this, the culture plate was placed in an incubator for incubation at 37°C. After transfection for 6 hour, the culture medium in the wells were discarded and replaced by serum‐containing MEM for further culture. After the cells had been transfected for 48 hour, dual‐luciferase reporter gene assay kit  was conducted to measure the luciferase activity. After the cells had been transfected for 48 hour, the former medium was removed, and cells were washed twice using phosphate‐buffered saline (PBS). The cells in each well were then added with 100 μL passive lysis buffer (PLB) and shaken gently for 15 minute at room temperature. The cell lysates were subsequently collected. Using the program, 2 seconds was set for pre‐reading and 10 seconds was set for reading values, in which the volume of sample for uploading LARII and Stop&Glo Reagent was 100 μL. Next the prepared LARII and Stop&Glo Reagent were added into the luminescent tube or plate (20 μL of each sample) for detection by a bioluminescence fluorescence detector .Cultured 293FT cells were seeded into a 96‐well culture plate (4 × 102.15q test) test, and the comparisons between two groups without variance homogeneity were analysed by Tamhane's T2 test. A value of P < .05 indicated that the difference was of statistical significance.The SPSS 21.0 statistical software  was applied to analyse the statistical data of the study. All the data were tested for normality. Measurement data were presented as mean ± standard deviation and tested for homogeneity of variances. The comparisons among multiple groups were conducted by the one‐way analysis of variance (ANOVA), while the comparisons between two groups with variance homogeneity were analysed by means of the least significant difference (LSD) or SNK (33.1P < .05); however in the DEX + miR‐140‐5p mimic, no obvious difference from the sham group was observed (P > .05). The miR‐140‐5p expression of rats in the DEX, miR‐140‐5p mimic, DEX + NC, DEX + miR‐140‐5p mimic and DEX + si‐Wnt1 groups was higher than that in the model group . In comparison with the DEX group, the miR‐140‐5p expression of rats was reduced in the DEX + miR‐140‐5p inhibitor group; however, increased levels in the DEX + miR‐140‐5p mimic group were recorded . The mRNA and protein expressions of Wnt1, β‐catenin, TCF‐4 and E‐cadherin of rats in the model, DEX, miR‐140‐5p mimic, DEX + NC, DEX + miR‐140‐5p inhibitor and DEX + miR‐140‐5p inhibitor + si‐Wnt1 groups were all higher than those in the sham group , but not significantly different in the DEX + si‐Wnt1, DEX + miR‐140‐5p mimic groups from the sham group . Compared with the model group, mRNA and protein expressions of Wnt1, β‐catenin, TCF‐4 and E‐cadherin of the rats were decreased in the DEX, miR‐140‐5p mimic, DEX + NC, DEX + miR‐140‐5p mimic, DEX + si‐Wnt1 and DEX + miR‐140‐5p inhibitor + si‐Wnt1 groups . In comparison with the DEX group, mRNA and protein expressions of Wnt1, β‐catenin, TCF‐4 and E‐cadherin of rats were reduced in the DEX + miR‐140‐5p mimic and DEX + si‐Wnt1 groups, but those were increased in the DEX + miR‐140‐5p inhibitor group . Overexpressed miR‐140‐5p was demonstrated to be capable of suppressing the Wnt/β‐catenin signalling pathway in HIBD rats.mRNA and protein expressions of Wnt/β‐catenin signalling pathway‐related genes of rats in each group are shown in Figure 3.2P < .05). In the DEX, miR‐140‐5p mimic, DEX + NC, DEX + miR‐140‐5p mimic, DEX + si‐Wnt1 and DEX + miR‐140‐5p inhibitor + si‐Wnt1 groups, the weight and length growth of rats was significantly increased when compared to the model group . Compared with the DEX group, the weight and length growth of rats was elevated in the DEX + si‐Wnt1 and DEX + miR‐140‐5p mimic groups; however, reductions were observed in the DEX + miR‐140‐5p inhibitor group . In comparison with the sham group, the indicators of rats, there were significant differences in the DEX + si‐Wnt1 and DEX + miR‐140‐5p mimic groups (P > .05). There was no obvious difference in relation to the indicators of rats between the model and DEX + miR‐140‐5p inhibitor groups (P > .05). The indicators of rats did not differ significantly among the DEX, DEX + NC, DEX + miR‐140‐5p inhibitor + si‐Wnt1 groups (P > .05).The parameters after treatment such as the growth conditions of weight and the length of rats in different groups are illustrated in Table 3.3P > .05), while the rate of reaching standard was found to be lower in all the other groups when compared with the sham group (P < .05). After two tests, no significant difference in total response times and duration, active avoidance and correct response rates was identified among the DEX + si‐Wnt1 and DEX + miR‐140‐5p mimic and sham groups . Rats in the sham group recorded significantly less total response times and duration than those in the model, DEX, miR‐140‐5p mimic, DEX + NC, DEX + miR‐140‐5p inhibitor and DEX + miR‐140‐5p inhibitor + si‐Wnt1 groups, while the active avoidance rate and correct response rate were higher than those in the other five groups . Compared with the model group, the total response times and duration of rats decreased after treated with DEX or miR‐140‐5p mimic, with the active avoidance and correct response rates all displaying increased levels , while miR‐140‐5p was shown to negatively influence the efficacy of si‐Wnt1 among HIBD rats   Table .3.4P < .05). There was no significant difference regarding the weight ratio between the left hemisphere and the right hemisphere as well as regarding the water content in the left hemisphere among the sham group and the DEX + si‐Wnt1 or DEX + miR‐140‐5p mimic groups . The DEX, miR‐140‐5p mimic, DEX + NC, DEX + miR‐140‐5p mimic, DEX + si‐Wnt1 and DEX + miR‐140‐5p inhibitor + si‐Wnt1 groups all displayed a greater weight ratio of the left hemisphere in comparison to the right hemisphere, but lower water content of the left hemisphere than the model group . In comparison with the DEX group, the weight ratio between the left hemisphere and right hemisphere was increased, with the water content of left hemisphere exhibiting decreases in the DEX + miR‐140‐5p mimic and DEX + si‐Wnt1 groups; however, a contrasting trend was observed in the DEX + miR‐140‐5p inhibitor group  . In the DEX, miR‐140‐5p mimic, DEX + NC and DEX + miR‐140‐5p inhibitor + si‐Wnt1 groups, cerebral infarction volume was smaller than that in the model group, but still larger than that in the DEX + si‐Wnt1, DEX + miR‐140‐5p mimic groups . There was no statistical significance detected in relation to the cerebral infarction volume between the DEX + miR‐140‐5p inhibitor and model groups . There was no significant difference between the DEX + si‐Wnt1 and DEX + miR‐140‐5p mimic groups (P > .05). After HIBD rats were treated with DEX, miR‐140‐5p mimic and si‐Wnt1, the cell apoptosis rate was decreased; however, the miR‐140‐5p inhibitor was shown to reverse the protection of si‐Wnt1 against HIBD rats.TUNEL staining results Figure A indicatCompared with the sham group, there were no significant differences detected in regard to the expression of Bcl‐2 and Bax, caspase‐3 mRNA levels and cleaved caspase‐3 expression between the DEX + si‐Wnt1 and DEX + miR‐140‐5p mimic groups, with decreases in the expression of Bcl‐2, as well as the expression of Bax, caspase‐3 mRNA levels and cleaved caspase‐3 expression increased in other groups. After being treated with DEX and si‐Wnt1, the expression of Bcl‐2 increased, while expressions of Bax and mRNA level of caspase‐3 decreased in HIBD rats, with miR‐140‐5p inhibitor exhibiting its capability in the reversal of the protection of si‐Wnt1 against HIBD rats , while observations of the mutant groups revealed there to be no significant differences , when compared with the mimics control + Wnt1‐WT and blank control + Wnt1‐WT groups  (Figure The result of RT‐qPCR and Western blot analysis suggested that the overexpression of miR‐140‐5p decreased the mRNA and protein expression of Wnt1, while the underexpression of miR‐140‐5p increased the mRNA and protein expression of Wnt1 in 293FT cells  Figure . miR‐1404As a severe life‐threatening disease, HIBD largely contributes to incidences of permanent brain damage among infants, frequently resulting in disabilities of a high modality.During this study, a key finding revealed that miR‐140‐5p targeted and inhibited the Wnt1 gene, highlighting the negative regulation of the Wnt/β‐catenin signalling pathway. When compared with the model group, the expressions of miR‐140‐5p were increased in the DEX, DEX + NC, DEX + miR‐140‐5p mimic and DEX + si‐Wnt1 groups. A rat model of HIBD was successfully established according to the prescribed method in our procedure, which demonstrated that the neuroprotective effect in treating HIBD could be achieved via placenta‐derived mesenchymal stem cells through relieving oxidative stress.In addition, our study showed that the combined action of DEX and miR‐140‐5p has protective effects on HIBD in neonatal rat brains, which could reduce neuronal apoptosis in hippocampus of rats. Although DEX or miR‐140‐5p mimic alone also exhibited protection against hypoxic–ischaemic brain damage, the DEX + miR‐140‐5p mimic group showed more significant difference in the above figures. As a potent and selective alpha‐2 agonist, DEX has a neuroprotective effect on hippocampus and dentate gyrus, similar to that in this study.In conclusion, by targeting Wnt1 through the negative regulation of the Wnt/β‐catenin signalling pathway, miR‐140‐5p abated neuron apoptosis in the hippocampus and resulted in reductions in the death rate of neonatal rats with HIBD; more importantly, miR‐140‐5p was shown to enhance the cerebral protection role of DEX from HIBD in neonatal rats. Although these results provide validation, the research findings translating into clinical application still need to be verified. Moreover, the specific and inner mechanism of DEX influencing HIBD is not yet defined. Further empirical researches are needed to transfer the therapeutic values of miR‐140‐5p to clinical care in the treatment of HIBD.The authors have declared that no competing interests exist.


Toll-like receptors (TLRs) play key roles in cerebral ischemia and reperfusion injury by inducing the production of inflammatory mediators, such as interleukins (ILs) and tumor necrosis factor-alpha (TNF-α). According to recent studies, ursolic acid (UA) regulates TLR signaling and exhibits notable anti-inflammatory properties. In the present study, we explored the mechanism by which UA regulates inflammation in the rat middle cerebral artery occlusion and reperfusion (MCAO/R) model. The MCAO/R model was induced in male Sprague–Dawley rats . UA was administered intragastrically at 0.5, 24, and 47 h after reperfusion. The direct high mobility group box 1 (HMGB1) inhibitor glycyrrhizin (GL) was injected intravenously after 0.5 h of ischemia as a positive control. The degree of brain damage was estimated using the neurological deficit score, infarct volume, histopathological changes, and neuronal apoptosis. We assessed IL-1β, TNF-α, and IL-6 levels to evaluate post-ischemic inflammation. HMGB1 and TLR4 expression and phosphorylation of nuclear factor kappa-light-chain-enhancer of activated B cell (NFκB) were also examined to explore the underlying mechanism. UA (10 and 20 mg/kg) treatment significantly decreased the neurological deficit scores, infarct volume, apoptotic cells, and IL-1β, TNF-α, and IL-6 concentrations. The infarct area ratio was reduced by (33.07 ± 1.74), (27.05 ± 1.13), (27.49 ± 1.87), and (39.74 ± 2.14)% in the 10 and 20 mg/kg UA, GL, and control groups, respectively. Furthermore, UA (10 and 20 mg/kg) treatment significantly decreased HMGB1 release and the TLR4 level and inactivated NFκB signaling. Thus, the effects of intragastric administration of 20 mg/kg of UA and 10 mg/kg of GL were similar. We provide novel evidence that UA reduces inflammatory cytokine production to protect the brain from cerebral ischemia and reperfusion injury possibly through the HMGB1/TLR4/NFκB signaling pathway. Ischemic stroke, which occurs as a result of the sudden occlusion of a blood vessel by a thrombus or embolism, is a common cause of death and disability worldwide . CurrentToll-like receptor 4 (TLR4) plays a key role in cerebral ischemia and reperfusion injury by inducing the production of inflammatory mediators, such as interleukins (ILs) and tumor necrosis factor-alpha (TNF-α) , 8. TLR4Ursolic acid (UA: 3b-hydroxy-urs-12-ene-28-oic acid), a natural pentacyclic triterpenoid, has been reported to exhibit biological activities in the brain, including anti-oxidative, anti-tumor, anti-rheumatic, anti-viral, and anti-inflammatory effects . HoweverIn the present study, we used the rat middle cerebral artery occlusion and reperfusion (MCAO/R) model with UA and GL to examine the mechanism by which UA regulates the inflammation response induced by ischemia and reperfusion. We investigated whether UA reduced inflammatory cytokine production to protect the brain from cerebral ischemia and reperfusion injury possibly though the HMGB1/TLR4/NFκB signaling pathway.ad libitum. Rats weighing between 250 and 280 g were randomly divided into five groups. (1) In the sham group (n = 18), rats underwent the same surgical procedures as rats in the MCAO/R group without filament insertion and received the vehicle. (2) In the control group (n = 18), rats underwent the MCAO/R surgical procedures and received vehicles both intragastrically (i.g.) and intravenously (i.v.) when the other treatment groups were administered UA or GL. (3) In the low-dose UA (L-UA) group (n = 18), 10 mg/kg UA  in distilled water containing 0.5% Tween-80 (ddH2O/0.5% Tween-80) was administered by oral gavage at 0.5, 24, and 47 h after reperfusion, according to previous studies clarifying the oral absorption rate and drug action time (n = 18) was administered 20 mg/kg UA. (5) The GL group rats (n = 18) were i.v. administered 10 mg/kg of GL  in a volume of 0.5 ml of distilled water containing 0.5% Tween-80 (ddH2O/0.5% Tween-80) via the tail vein 0.5 h after ischemia and before reperfusion as a positive control , the UK Animals Scientific Procedures Act 1986 or the European Communities Council Directive of 24 November 1986 (86/609/EEC) and the “Guiding Principles in the Use of Animals in Toxicology,” adopted by the Society of Toxicology in 1989. All procedures used in animal experiments were approved by the Institutional Animal Care and Use Committee of China Medical University. Ninety male Sprague–Dawley rats were purchased from the Liaoning Changsheng Biotechnology Company . These rats were housed under a 12-h light/12-h day cycle with free access to food and water ion time –17. (4) roup n = , rats un control .According to previous studies clarifying the oral absorption rate and drug action time, UA-administered mice had a lethal dose 50 of 60 mg/kg and a rat-to-mouse dosing ratio of 6.3/9.1. The final dose of UA was 5, 10, and 20 mg/kg. Since UA is insoluble in water, 0.1% Tween-80 is used as a solubilizer and 0.1% Tween-80 is used to dilute UA to 1 mg/ml, and the pH is adjusted to 7.4 to avoid the acid and alkali caused by the drug stimulate.Forty-eight hours after reperfusion, 18 rats in each group were randomly divided into three groups by a researcher who was unaware of the neurological deficits in these rats. Six rats were decapitated to obtain fresh brain tissue samples for biochemical analyses. The ischemic cortex, which was defined as the penumbra, was collected for ELISA and western blotting analyses based on methods modified from Jiang et al. . The braThe success rate of model preparation in this experiment was 83.3%. No neurological impairment was observed in 3 of them, 3 with score of 4, 2 died in surgery, 14 with subarachnoid hemorrhage. Subarachnoid hemorrhage accounted for 77.8% of excluded factors, as the main excluded factor.Surgical procedures for MCAO/R were performed in rats using the intracranial suture method, as previously described . Brieflyn = 18 in each group). The scoring criteria for neurological deficits have been described previously by Longa et al.  with 2,3,5-triphenyltetrazolium chloride , as previously described in detail  solution for Nissl staining. Images were captured using a light microscope (at 400× magnification). In the Nissl-stained sections, only intact neurons were counted.A TUNEL assay was performed according to the manufacturer’s instructions . Sections were incubated with rabbit anti-NeuN antibody  in PBS/0.2% TX-100 and then incubated with the TUNEL reaction mixture to verify the neuronal identity of the TUNEL-positive cells. Finally, 4′,6-diamidino-2-phenylindole (DAPI) was added. The total number of TUNEL-positive neurons was counted by an investigator who was blinded to the study protocol.n = 6 per group), which were sectioned and deparaffinized. The sections were incubated with an anti-HMGB1 monoclonal antibody  and an anti-TLR4 monoclonal antibody . Binding was detected using the streptavidin-peroxidase kit . The positive cells were identified, counted, and analyzed in the sections with the ImageJ software.Immunohistochemical staining of HMGB1 and TLR4 was performed using paraffin-embedded brain samples from each animal (n = 6 per group) that had been sectioned and deparaffinized. Sections were incubated with primary antibodies  and then with secondary antibodies labeled with fluorescent dyes . Photomicrographs were quantified performed by converting the images to gray scale, inverting their color, and quantifying the staining intensity in each field with ImageJ software.Immunofluorescence staining of the microglial marker Iba-1 and the astrocytic marker GFAP were performed using paraffin-embedded brain samples of rats  according to the manufacturer’s instructions.Cytosolic and nuclear proteins from the ischemic cortex were prepared with the Nuclear/Cytosol Fractionation Kit  for western blotting analysis. As previously described in detail, the protein samples were separated using 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis and then transferred to a polyvinylidene fluoride membrane . The membrane was incubated with the following antibodies: anti-HMGB1 antibody , anti-TLR4 antibody , anti-NFκB p65 antibody , anti-IκB antibody , anti-phospho-IκB antibody , and phospho-NFκB p65 antibody . To confirm equal loading, we used an anti-GAPDH antibody  and an anti-lamin A antibody . The density of each band was quantified using ImageJ.P < 0.05 was defined as statistically significant. The neurological deficit scores among the different groups were compared using the Kruskal–Wallis test. When the Kruskal–Wallis test showed a significant difference, the Dunn’s multiple comparisons test was applied. Given the simple size of six animals per group, actual power was performed with the G*Power 3.1.9.2 software at 5% significance level. We got a power greater than 0.9.All data are expressed as mean ± SD and analyzed with one-way analysis of variance using SPSS20.0. After 48 h of reperfusion, neurological deficit scores were significantly increased in the control group Figure B. The UAThe infarct volume was assessed 48 h after reperfusion by TTC staining. No obvious infarction could be observed in the sham group. The rats treated with UA (10 and 20 mg/kg) and GL displayed obvious reductions in the ratio of the infarct volume compared with the control group   and GL significantly alleviated the damage in the rat brains. UA and GL increased the number of intact neurons, and the number of injured neurons with cell shrinkage decreased compared with that in the control group  and GL-treated groups compared with the control group Figure .The concentrations of IL-1β, TNF-α, and IL-6 in the ischemic cortex of the control group were significantly higher than those in the sham group after 48 h of reperfusion. The concentrations of IL-1β, TNF-α, and IL-6 were significantly reduced after UA (10 and 20 mg/kg) and GL treatment compared with those in the control group  and GL significantly reduced the Iba-1 expression, whereas UA did not change the GFAP expression Figures  and 8.We examined the effect of UA on the NFκB pathway to identify the mechanism by which UA regulates inflammatory cytokines. Western blot analyses revealed significantly higher levels of phospho-IκB and phospho-NFκB p65 in the control group than in the sham group. The UA (10 and 20 mg/kg) and GL treatments significantly decreased the levels of the phospho-IκB and phospho-NFκB p65 proteins. No obvious differences in the levels of total NFκB p65 and total IκB proteins were observed among the experimental groups. We confirmed that UA and GL inhibited NFκB activation, because the cytosolic and nuclear fractions showed decreased NFκB p65 translocation from the cytosol to the nucleus in the UA- and GL-treated rats compared with those in the untreated rats Figure .Ursolic acid, a pentacyclic terpenoid, exhibits extraordinary neuroprotective properties with anti-inflammatory effects during early brain injury for SAH , 32. In Cerebral ischemia and reperfusion injury result from the rapid and explosive reoxygenation induced by the inflammatory response, which is tightly associated with inflammatory mediators such as IL-1β, TNF-α, and IL-6. In fact, inflammatory cytokines are involved in aggravating brain infarction both in humans and in experimental stroke models , 34–36. Toll-like receptors are a family of pattern recognition receptors that represent key elements in the initiation and progression of inflammatory cytokine production in response to ischemia and reperfusion injury . TLR4 isFurthermore, our results showed that UA and GL had similar effects on HMGB1/TLR4/NFκB expression and the reduction of the neurological deficit scores, infarct volume, and apoptosis in penumbral neurons. We used GL, which is the most studied small-molecule inhibitor of HMGB1, as a positive control drug , 48. GL However, this study provided only suggestive data. Thus, the mechanism by which UA affected HMGB1 release from the core that led to TLR4-mediated signal transduction was unclear. Further mechanistic studies investigating how UA inhibits of HMGB1/TLR4/NFκB activation are required.In conclusion, UA attenuated inflammatory cytokine production to protect the brain against cerebral ischemia and reperfusion injury in a rat model possibly through HMGB1/TLR4/NFκB signaling pathway activation. Based on these findings, UA may be useful as a potential effective adjunct to therapy for ischemic brain injury prior to reperfusion.All experimental protocols involving animals were performed according to the guidelines of the National Institutes of Health Guide for the Care and Use of Laboratory Animals , the UK Animals Scientific Procedures Act 1986 or the European Communities Council Directive of 24 November 1986 (86/609/EEC). All agreements for animal experiment were approved by the Institutional Animal Care and Use Committee of China medical university and the “Guiding Principles in the Use of Animals in Toxicology,” adopted by the Society of Toxicology in 1989.Y-ZW helped establish the animal model, collect and analyze samples, and write the manuscript. Y-ZW and LL helped perform the Western blotting, immunohistochemistry experiments and review the manuscript. Y-ZW and S-MD helped design the study, establish the animal model, perform data analysis, and write the manuscript. FL helped with drafting the work or revising it critically for important intellectual content. Z-YH contributed to study planning, data analysis and review of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Secondary shifts develop in post-translational phosphorylation of sarcomeric proteins in multiple animal models of inherited cardiomyopathy. These signaling alterations together with the primary mutation are predicted to contribute to the overall cardiac phenotype. As a result, identification and integration of post-translational myofilament signaling responses are identified as priorities for gaining insights into sarcomeric cardiomyopathies. However, significant questions remain about the nature and contribution of post-translational phosphorylation to structural remodeling and cardiac dysfunction in animal models and human patients. This perspective essay discusses specific goals for filling critical gaps about post-translational signaling in response to these inherited mutations, especially within sarcomeric proteins. The discussion focuses primarily on pre-clinical analysis of animal models and defines challenges and future directions in this field. Animal models expressing individual mutations have provided insight into the human disease and a better understanding of myofilament force transduction mechanisms  and identification of underlying gene mutations continues to expand  responsible for the initiation of Ca2+ remodeling in these patients. IC-linked mutations may initiate changes in local myofilament signaling network(s) and the myofilament post-translational modification (PTM) pattern helps to maintain cardiac performance prior to any changes in Ca2+ handling. Evidence is accumulating that myofilament residues targeted by signaling pathways can initiate additional “secondary” or “adaptive” changes in the phosphorylation of other myofilament residues to modulate function  signaling provides direct support for a role of local myofilament signaling in IC-linked changes in cardiac function. Several IC-linked mutations directly influence myofilament phosphorylation and/or β-AR signal transduction, as illustrated by representative cTnI mutations. Protein kinase A (PKA)-induced cTnI-S23/24 phosphorylation significantly contributes to the positive β-AR-induced lusitropic response , endothelin, and catecholamine activation of α-adrenergic receptors (Dorn and Force, 2+ handling modifications described earlier.Many of the potential signaling networks associated with myofilaments have the ability to produce a range of outputs via multi-layer signaling cascades capable of targeting both kinases and phosphatases, multiple sarcomeric protein targets, and multiple residues targeted within a single myofilament protein Figure . The com2+ wave frequencies in cardiomyocytes expressing gain-of-function SHP-2/PTPN11 mutations, which are linked to Noonan's syndrome (Uhlén et al., While a secondary reduction in myofilament phosphorylation can coincide with cardiac dysfunction (Bayliss et al., IC-linked mutations also may trigger a secondary signaling response, which may arise from structural changes imposed by a mutation. This secondary response could involve one or more signaling pathways known to target myofilament proteins Figure . HoweverOther approaches, such as proteomic analysis of myofilament proteins during heart failure, also hint at dynamic PTM modulation within myofilaments. Heart failure is associated with altered phosphorylation residues in several contractile proteins (see Table in vivo genetic model work is critical for achieving these goals. As a result, significant advancements are likely to depend on an unusually high level of cooperativity and resource sharing among investigators.Future work needs to establish the circuitry, physiological functions, and temporal response of local myofilament modulatory signaling, and test whether this local modulation is an early or longer-term contributor to IC-linked remodeling and/or dysfunction. A parallel, translational goal for this work is the development of diagnostic tools, improved clinical management, and therapies to prevent and/or delay disease progression in IC patients. The integration of computational modeling, myofilament, cellular, and The author confirms being the sole contributor of this work and approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Primary cilia are hair-like organelles and play crucial roles in vertebrate development, organogenesis, health, and many genetic disorders. A primary cilium is a mechano-sensory organelle that responds to mechanical stimuli in the micro-environment. A cilium is also a chemosensor that senses chemical signals surrounding a cell. The overall function of a cilium is therefore to act as a communication hub to transfer extracellular signals into intracellular responses. Although intracellular calcium has been one of the most studied signaling messengers that transmit extracellular signals into the cells, calcium signaling by various ion channels remains a topic of interest in the field. This may be due to a broad spectrum of cilia functions that are dependent on or independent of utilizing calcium as a second messenger. We therefore revisit and discuss the calcium-dependent and calcium-independent ciliary signaling pathways of Hedgehog, Wnt, PDGFR, Notch, TGF-β, mTOR, OFD1 autophagy, and other GPCR-associated signaling. All of these signaling pathways play crucial roles in various cellular processes, such as in organ and embryonic development, cardiac functioning, planar cell polarity, transactivation, differentiation, the cell cycle, apoptosis, tissue homeostasis, and the immune response. Cilia are slender microtubule-based sensory organelles that protrude from the apical membrane in many cell types; approximately 800 ciliary proteins have been identified ,2. TheseThe cilium structure contains the microtubule-based cytoskeleton core unit called the axoneme . The axoA general classification of cilia structure is very complex because of the discoveries of a “9+4” axoneme in Hensen’s node of a rabbit and a “3+0” axonemal structure in a protozoan ,20. None2+) signaling has been recognized for decades but had only focused on the motile cilia . A. A2+) si systems ,30,31. Utems [2+ ,30,31.2+ can occur without a significant alteration in ciliary Ca2+, arguing against the hypothesis that channels in the primary cilium would have a significant effect on global cytoplasmic Ca2+ [2+ propagating to the ciliary base without affecting the cytoplasmic Ca2+ level. It is proposed that the cilium can become rapidly infused with tiny amounts of Ca2+ that flow in from the cell body  used in the experiments. When changes in ciliary calcium were observed, G-GECO1.0, which has a Kd value of 749 nM, was utilized in these studies [2+ in response to the same stimulus.As previously discussed , the dif+ models A,B may b studies ,29,30,31 studies . Nonethe2+ signaling. Each transient receptor potential cation channel subfamily member, namely TRPC1, TRPP2, TRPP3, and TRPV4 can be modulated by chemical (Ca2+ and receptor ligands), physical (voltage and temperature), and mechanical stimuli  [Aside from PC2, a primary cilium also contains other transient receptor potential (TRP) channels ,37,38,39-stress) ,37,38,39-stress) ,41. In a-stress) ,42,43,442+ regulation [2+ influx. This influx is inhibited by high internal Ca2+ concentrations. Moreover, the C-terminal EF-hands and coiled-coil domains of TRPP3 do not contribute to Ca2+-induced potentiation and inactivation. It is proposed that the unique characteristics of the TRPP3 enable Ca2+ to enter but not leave the small ciliary compartments [A recent study shows that TRPP3, also known as the PKD2-L1 polycystin ion channel, has atypical Cagulation . This startments .l-type Ca2+ channel also modulates the cystic kidney phenotype, hydrocephalus, and left–right organization defects [2+ channel knockdown in a zebrafish model facilitates the formation of these ciliopathic phenotypes and the l-type Ca2+ channel presence in renal epithelial cilium is confirmed by immunocytochemistry. This confirms the importance of Ca2+ signaling in the chemosensory roles of the primary cilium. This l-type Ca2+ channel expression also regulates through Wnt signaling [Apart from TRP channels, the  defects ,46. L-tyThe evolutionarily conserved Hedgehog (Hh) signaling plays an essential role in many aspects of vertebrate embryonic development and stem cell maintenance. Hh signaling is disrupted in a spectrum of human tumors, such as in basal cell carcinoma and medulloblastoma. Although the precise intracellular process of Hh signaling has slightly diverged between species, involving different mechanisms and protein components, primary cilia have an essential role in Hh signaling in both vertebrate and invertebrate development ,48. The The cilium has been suggested to function as the Hh transduction hub . Hh signIn the presence of an Hh ligand B, PTCH1 Another important pathway required for ciliary protein transportation for normal regulation is Wnt pathways  and lymphocyte enhancer factor (Lef), which interacts with various transcriptional suppressors, such as Groucho.Drosophila [2+) pathway is believed to be an important initiator for non-canonical Wnt signaling. DSH is recruited to the plasma membrane to prevent the proteasomal degradation of DSH in a β-catenin-independent manner. Several studies have implicated many other ciliary and basal body proteins in the regulation of Wnt signaling [The non-canonical Wnt pathway is less well characterized. It is thought that the non-canonical pathway is independent of β-catenin, but dependent on DSH B. The noosophila . The mecignaling ,72,73,74ignaling . Inversiignaling ,75. Howe2+ activity is present [2+ influx regardless of their ability to signal through β-catenin as long as they can bind to PKD1 and PKD1/TRPP2 is present in the target cell.A recent study shows that several Wnt ligands can bind to PKD1 and that PKD1/TRPP2 can mediate the effects of other Wnts, not only in the kidney but also in other organs where Wnt/Ca present . This stPlatelet-derived growth factor (PDGF) and their receptors (PDGFRs) play pivotal roles in cell survival, growth control, proliferation, cell migration, embryonic development, and the maintenance of tissue growth. The PDGFR is a receptor tyrosine kinase that mediates signaling events through primary cilia . Abnorma2+ and a short intracellular portion. There is evidence suggesting that some Notch signaling may depend on primary cilium for the signal transduction [Notch signaling plays a key role in various aspects of patterning and cell fate choice in neurogenesis and maintenance of adult tissue growth and development. Notch signaling balances progenitor cells with differentiated neurons; it regulates the binary cell fate choice of progenitors as they segregate into different neuronal subtypes ,87,88,89sduction ,91.The Notch3 receptor interacts with Presenilin-2, an enzyme responsible for Notch cleavage and activation . The NotTransforming growth factor beta (TGF-β) plays major roles in bone development and maintenance metabolism by regulating cellular proliferation, differentiation, matrix deposition, and cell migration ,97. TGF-Mammalian target of rapamycin (mTOR) is a serine/threonine protein kinase functioning as a central regulator in cellular metabolism, cell growth, cell proliferation, cell cycle progression, and survival. mTOR associates with different proteins to form the rapamycin-sensitive (mTORC1) and -insensitive (mTORC2) complexes, respectively. mTOR signaling and primary cilia are associated with the hyper-proliferation of renal tubular cells, which induces the formation of renal cysts . The mosAutophagy is a process by which cells break down cytoplasmic contents. Oral facial digital syndrome 1 (OFD1) localizes to centrioles and centriolar satellites A. At theAnother study explores the effect of ciliogenesis on autophagy . StarvatG-protein coupled receptors (GPCRs) constitute a substantially diverse family of seven-transmembrane receptors that transmit specific signals to the cell through G proteins that regulate the activity of various cellular and physiological signaling networks. GPCRs also localize to rodent neuronal cilia, including the serotonin subtype-6 receptor (5-HTr6), somatostatin receptor3 (SSTR3), and melanin-concentrating hormone receptor 1 (MCHR1) ,114,115.The elusive role of ciliary kisspeptin receptor (KISS1R) in the onset of puberty and adult reproductive function is observed in gonadotropin-releasing hormone (GnRH) neurons . While tOverexpression of the serotonin subtype-6 receptor (5-HT6) in cilium induces abnormally long cilia in rat brains that also have significantly reduced dendritic branches . This ef2+ release in response to ATP [Another GPCR that has been associated with cilia signaling is dopamine receptors. Dopamine is a circulating hormone that plays a fundamental role as a neurotransmitter. Dopamine has been implicated in hypertension in humans and animal models. The roles and functions of dopamine vary in different tissues in the body. The renovascular dopaminergic system is involved in renal blood flow and blood pressure control, but its specific regulation is not yet known. Genetic screening for abnormal cilia length has identified a family of class-A dopamine binding GPCRs that are involved in the regulation of cilia and flagella length . One of e to ATP .It is important to note that while the cellular and molecular pathways described above have been studied in the context of cilia structure or function, there is evidence that some pathways could also take place independent of cilia. For example, it has been suggested that cilia are not involved in Wnt signaling ,125. WheIn summary, the primary cilium remains a vital organelle responsible for integrative signaling from extracellular signals in order to exert the physiological functions within a cell. It would certainly be a generalization to assume that a single type of cilium would serve most of the needs of every cell. The key to our understanding of cilia function in cellular signaling might be to differentiate the roles of cilia in different cells, tissues, and organs. Although abnormal ciliary proteins cause ciliopathies, which present a diversity of clinical features, we do not have a clear idea as to how the cilia signaling compartment plays an ultimate role in the pathogenesis. There needs to be further research on primary cilia as this neglected organelle continues to disclose its internal mysteries.


Central to the study of populations, and therefore to the analysis of the development of countries undergoing major transitions, is the calculation of fertility patterns and their dependence on different variables such as age, education, and socio-economic status. Most epidemiological research on these matters rely on the often unjustified assumption of  linearity, or alternatively makes a parametric assumption (e.g. for age-patterns).We consider nonlinearity of fertility in the covariates by combining an established nonlinear parametric model for fertility over age with nonlinear modelling of fertility over other covariates. For the latter, we use the semi-parametric method of Gaussian process regression which is a popular methodology in many fields including machine learning, computer science, and systems biology. We applied the method to data from the Agincourt Health and Socio-Demographic Surveillance System, annual census rounds performed on a poor rural region of South Africa since 1992, to analyse fertility patterns over age and socio-economic status.We capture a previously established age-pattern of fertility, whilst being able to more robustly model the relationship between fertility and socio-economic status without unjustified a priori assumptions of linearity. Peak fertility over age is shown to be increasing over time, as well as for adolescents but not for those later in life for whom fertility is generally decreasing over time.Combining Gaussian process regression with nonlinear parametric modelling of fertility over age allowed for the incorporation of further covariates into the analysis without needing to assume a linear relationship. This enabled us to provide further insights into the fertility patterns of the Agincourt study area, in particular the interaction between age and socio-economic status. The measurement of fertility rates and their relationships to socioeconomic variables are essential to the analysis of the population dynamics of that society. For South Africa, whose history of Apartheid has resulted in a very socio-economically diverse population, the ability to examine trends and patterns in fertility is even more important when trying to assess the development of the country. In the last few decades the country has experienced a number of health and demographic shifts including the HIV pandemic, the rise in prevalence of noncommunicable disease , and theMost of this research, as is typical in epidemiology, has relied on established statistical analysis methods of parametric and generalised linear regression, despite more recent innovation in statistical analysis in recent years. Fertility rates are often only examined empirically, leaving the conclusions drawn vulnerable to noise that could exist within the data –15. LineHere we present more general methods for examining the relationship between fertility and various covariates, focusing on age and socio-economic status, by combining a standard nonlinear parametric model of fertility rates over age with the use of Gaussian process regression to bring in further covariates that we do not have well-established models for. In using a parametric model over age, we make sure to capture the nonlinear relationship shown to exist between fertility and age in other work , 17. GauFor our analysis we used data from the Agincourt Health and Socio-Demographic Surveillance System (HDSS), run by the Medical Research Council/University of the Witwatersrand Rural Public Health and Health Transitions (Agincourt) Research Unit. Details on its methodology have been published elsewhere , 19.In brief, the Agincourt HDSS is an annual update round of the baseline census performed in 1992. In each round demographic data is collected including births, deaths, and migration. Health information is collected at regular bases and since 2006 a new system allows the linkage of census data with morbidity data at the existing Primary Health System in the study area. Originally it covered 57,600 people in 8900 households in 20 villages . The areWe created a database out of the Agincourt HDSS selecting women who were living in a household in the HDSS dataset during the years that socio-economic status was collected . The inclusion criteria for each year were individuals of all ages who had a recorded date of birth and no date of death proceeding the selected year, and belonged to a household that supplied enough information to calculate an absolute socio-economic status (SES) index. The total sample size was 224,643, where an observation was defined as a woman in an individual year who meets all inclusion criteria. Some women were counted as multiple observations due to appearing in the census dataset in multiple years. Though we did consider the inclusion of other covariates such as education (measured in number of years of education achieved), our analysis focused on fertility  over age (measured in years) and SES  were found by using the maximum likelihood method as described in Rasmussen and Williams [The method is called semi-parametric as we do not get a parametric relationship between the dependent and independent variables as a result, but instead parameters are used to define the covariance function. There are many different covariance functions to choose from, but a standard choice that we used in this analysis is the squared exponential covariance function, which results in a smooth and continuous relationship between our dependent and independent variables, and is defined asWilliams .x and how far the covariance of x. Therefore these parameters essentially dictate both the magnitude of variation and frequency of fluctuations of x, without dictating a precise parametric form for y and our estimates By fitting these parameters to the data, we allow the data to inform both the magnitude of the variance of One way to think of this method of combining Gaussian process regression with parametric regression is that the Gaussian process regression smooths over the other covariates the parameters of our model for the role of age. Fitting Gaussian distributions of functions to the estimates of We guarded against overfitting of the Gaussian process through use of a smoothing prior for the length parameter for SES, a gamma distribution with shape parameter 6 and scale parameter 0.25. Wider and thinner priors were also tried to see what effect the prior choice had on the results, but little to no differences were found.N observations. Due to the treatment of the problem as a regression, and therefore having to bin the data, goodness-of-fit tests to the unbinned data could not be performed. Instead we performed Kolmogorov–Smirnov (KS) tests comparing the fitted marginal fertility probabilities over age to the empirical marginal fertility rates over age [In order to find which combination of covariates would be best to include in the model, as well as to decide on how many quantiles should be used for binning as described in the previous section, a combination of cross validation and goodness-of-fit tests were used. To measure the predictive performance of each possible model choice tenfold cross validation was used, where the performance was measured by their Briers score 6\documenover age . Model cover age . It was The resulting fitted forms for fertility rate over age and SES for a range of years between 2001 and 2011 can be seen in Figs. The relationship between fertility rate and SES can be seen in Fig. We also performed the same analysis (using the same covariates and quantiles) on the individual refugee and non-refugee populations, achieved by splitting the dataset into refugee and non-refugee sub-datasets according to whether the individuals were marked out as refugees in the dataset or not. Significant differences have been shown in fertility levels between the Mozambican refugee population who came over to the study area in response to the civil war, and the South African population, though it has been shown that the populations have converged in recent years . The resThe combination of results does imply a linear trend of fertility over SES for this population, but this does not dispute the usefulness of incorporating further covariates other than age by Gaussian process regression as this still overcomes the need to make unfounded a priori assumptions of linearity.By combining a parametric regression of fertility rate over age with the use of Gaussian process regression to bring in further covariates such as SES, we produce an improvement in robustness to the modelling of fertility. The parametric part of our model successfully captures the well known skewed hill relationship between fertility and age that can be seen both in empirical plots of our own data shown in Figs. The semi-parametric part of our model, using Gaussian process regression over other covariates, successfully manages to model the SES pattern of fertility without simply assuming the relationship to be linear as other work has done , 3, 16. An interesting detail is that the peak of the fertility age-pattern found by our model appears to increase over time, which, though the variation is small, contradicts work done on fertility trends over time that have found a significant decline in fertility over the past several decades , 14, 25.When differentiating by refugee status, some differences are seen between refugee and non-refugee populations. However these differences are not so substantial to mark out the populations as significantly different from the overall population, perhaps due to the convergence in fertility of the two populations shown in the literature.There are limitations in the work presented here. Though a lot of effort is undergone by the Agincourt research unit to ensure the reliability of the HDSS data, as detailed elsewhere , there aFurther research to extend our method might include the following. First, to overcome the limitations mentioned, Gaussian process regression could be used for age rather than relying on a parametric model. A probabilistic classification technique such as Gaussian process classification could be used instead of regression techniques to overcome the issue of having to bin the observations together. In order to examine the apparent lack of a fertility decline in our results, and to make the research into the fertility decline more robust, nonlinear modelling techniques such as Gaussian process regression could be applied to the fertility time series of the study area. Also, other outcomes than fertility, that have also been analysed using less innovative methods, could be explored with this same technique. Finally, as mentioned before, a second fertility peak has been found by previous studies to exist in the Agincourt population, which our model is restricted from capturing . It would also be of interest to see what happens when the parametric fertility-age model used here is replaced with a double peaked model such as that proposed by Peristera and Kostaki .Though the measurement of patterns of fertility over different covariates is of great importance to the analysis of population dynamics, most research still relies on methods such as empirical calculations and linear models to do so which are open to issues such as susceptibility to noise and assuming a linear relationship without justification. Here we have presented a method to incorporate further covariates into a nonlinear parametric model of fertility over age by regressing the parameters of the model on these covariates using Gaussian process regression, which is both nonlinear and flexible. This allows us to limit our assumptions of the relationships between fertility and these covariates to simply being smooth and continuous. We successfully applied the model to data from the Agincourt health and socio-demographic surveillance system collected between 2001 and 2011, an annual census update collecting demographic  and health data on a poor rural region of South Africa, to examine how fertility varies over age and socio-economic status (SES). Our method managed to capture the expected age-pattern of fertility, and gave further insights into how fertility varies over SES in the Agincourt study area. The magnitude of the fertility variation over SES is small, essentially constant, reflecting the homogeneity of the study area population. This linearity produced by the Gaussian process regression however does not undermine the use of the method as there is no a priori reason to assume the relationship to be linear, and by relaxing the initial assumptions our model makes we have therefore substantially increased the robustness of these results. The results also show that the fertility age-pattern peak appears to increase over time, which is inconsistent with a lot of established work on the well known fertility decline in sub-Saharan Africa. Further work should therefore apply nonlinear methods to the fertility time series in the area to examine whether this is a local phenomenon or simply that the trend in the peak is not reflected in fertility as a whole. A less restrictive form for the fertility age-pattern should also be incorporated into the model to see if the second fertility peak found in the literature can be captured.


Due to alterations in their metabolic activity and decreased mitochondrial efficiency, cancer cells often show increased generation of reactive oxygen species (ROS), but at the same time, to avoid cytotoxic signaling and to facilitate tumorigenic signaling, have mechanism in place that keep ROS in check. This requires signaling molecules that convey increases in oxidative stress to signal to the nucleus to upregulate antioxidant genes. Protein kinase D1 (PKD1), the serine/threonine kinase, is one of these ROS sensors. In this mini-review, we highlight the mechanisms of how PKD1 is activated in response to oxidative stress, so far known downstream effectors, as well as the importance of PKD1-initiated signaling for development and progression of pancreatic cancer. The Warburg effect in cancer cells is the product of two factors, a return of cells to glycolytic metabolism and increased production of mitochondrial reactive oxygen species (ROS), which is due to alterations in oxidative phosphorylation . In estaN-terminal regulatory region and a C-terminal kinase domain. Main elements in the regulatory region are two cysteine-rich (C1) domains that are important for lipid binding, and a pleckstrin homology (PH) domain, needed for protein–protein and protein–lipid interactions [reviewed in Ref.  ROS levels initiate a series of tyrosine phosphorylations  in PKD1 , 10, 11,A sequential model for activation of PKD1 by ROS has been proposed Figure . This isSOD2, a gene encoding manganese superoxide dismutase (MnSOD)  signaling that promotes apoptosis  . MnSOD gferation . Besidesferation . PKD1 inferation , and sucferation . Anotherpoptosis , 22. Simpoptosis . Anotherpoptosis . PKD1-phpoptosis . Hsp27 apoptosis , 27. In poptosis . Such sipoptosis .Reactive oxygen species–PKD1 signaling has emerged to be important in the pathophysiology of neurodegenerative diseases , 31, carKRAS mutations  are initiated by acquisition of activating utations . During utations , 36–40. utations , 40, 41.utations . Oncogenutations , 43. Thiperoxide . In sum,peroxide , 45, 46.peroxide , 45.Although in normal fibroblast cells, the ROS/PKCδ/PKD1 pathway downstream of oncogenic KRas upregulates pro-inflammatory signaling (expression of interleukin-6 and interleukin-8) and may contribute to senescence , under pThe occurrence of increased oxidative stress in tumor cells requires ROS-sensing signaling to upregulate antioxidant systems to counterbalance ROS. This opens an opportunity for targeting tumor cells . In respBoth authors have made equal intellectual contributions to text and figures.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In the present paper we propose an improved class of estimators in the presence of measurement error and non-response under stratified random sampling for estimating the finite population mean. The theoretical and numerical studies reveal that the proposed class of estimators performs better than other existing estimators. In survey sampling, usually it is assumed that all the observations are correctly measured on the characteristics under study. But in practice this assumption is not met for a variety of reasons, such as non-response may occurs due to refusal of respondents to give the information or not at home or lack of interest or due to some ethical reasons. Usually measurement error and non-response are studied separately using the known auxiliary or additional information. In reality, both measurement error and non-response occur simultaneously in survey sampling. Mostly the information is not obtained from all the units during surveys, so non-response is a common problem which may creeps during a sample survey. In sampling theory the estimation of population mean of a variable of interest in the presence of non-response, when the auxiliary information available is widely debated. –5 and 66 discussIn practice, the researchers who have studied measurement error have ignored the presence of non-response. But very few of them studied both under simple random sampling. In this paper, we have proposed a class of estimators for estimating the population mean in the presence of measurement error and non-response simultaneously under stratified random sampling. The efficiency of the suggested class of estimators over the existing estimators is shown through simulation study and real data sets.N identifiable units which are partitioned into L homogeneous subgroups called strata, such that the hth strata consist of Nh units, where h = 1, 2, …, L and N consists of two mutually exclusive groups called response and non-response groups. Let Nh1 and Nh2 are the responding and non-responding unit in the hth stratum respectively. We select a sample of size nh from Nh by using simple random sampling without replacement (SRSWOR) and assume that nh1 units respond and nh2 units do not respond. We select a sub-sample of size kh, nh2 non-responding units in the hth stratum.Consider a finite population of X, Y) of the ith sample units in the hth stratum. Then the measurement errors be ChY and ChX be the coefficient of variations for the respondents and ChY(2) and ChX(2) be the coefficient of variations for the non-responding units respectively. Let ρhYX and ρhYX(2) be the population correlation coefficients between their respective subscripts respectively for responding and non-responding units, respectively.Let In this paper an improved class of estimators for estimating the population mean in the presence of measurement error and non-response is proposed under stratified random sampling. Expressions for the bias and mean square error (MSE) of the class of estimators are obtained upto first order of approximation, when both the study and the auxiliary variables suffer with a problem of non-response and measurement errors.The present paper is organized as: Section 2 gives some existing estimators of the finite population mean. In Section 3, an improved class of estimators is suggested for estimating the finite population mean by incorporating both measurement error and non-response information simultaneously. Efficiency comparison is presented in section 4. Numerical results and simulation study are presented in Section 5. Conclusion is given in Section 6.In this section we consider the following existing estimators.In stratified random sampling, the Hansen and Hurwitz (1946) estimator for population mean nh1 of responding and gh units of sub-samples from nh2 non-responding groups, respectively.Here The variance of Ch = λh2ρhYXShYShX + θhρhYX(2)ShY(2)ShX(2).The usual ratio estimator under stratified random sampling, is given bydh is the constant.The usual difference estimator under stratified random sampling, is given bydh is The minimum variance of Azeem and Hanif (2016) estimator under stratified random sampling, is given bymh1 and mh2 are constants whose values are to be determined and αh is the scalar, chosen arbitrary. For obtaining the bias and mean square error, we assume that We propose an improved general class of estimators for estimating the population mean, dealing with the problem of measurement error and non-response simultaneously in stratified random sampling. Measurement error and non-response is present on both, the study and the auxiliary variables. The suggested estimator is given byAdding nh, and then simplifying, we getDividing both sides by Similarly, we can getOn simplifying, we getFurther simplifying, and ignoring error terms greater than two, we haveUsing mh1 and mh2, we differentiate mh1 and mh2 respectively. The optimal values are given by For finding the optimal values of Substituting these optimum values in From Eqs  and 18)18), MSE(From Eqs  and 18)18), MSE(From Eqs  and 18)18), MSE(From Eqs  and 18)18), MSE, Y1 = X1 + rnorm, y1 = Y1 + rnorm, x1 = X1 + rnormX2 = rnorm, Y2 = X2 + rnorm, y2 = Y2 + rnorm, x2 = X2 + rnormX3 = rnorm, Y3 = X3 + rnorm, y3 = Y3 + rnorm, x3 = X3 + rnormX4 = rnorm, Y4 = X4 + rnorm, y4 = Y4 + rnorm, x4 = X4 + rnormNumber of Stratas = 4N1 = 1000, N2 = 1000, N3 = 1000, N4 = 1000, n1 = 200, n2 = 200, n3 = 200, n4 = 200, ρYX1 = 0.9950779, ρYX2 = 0.9926346, ρYX3 = 0.9939164, ρYX4 = 0.9896319.Population II.X1 = rnorm, Y1 = X1 + rnorm, y1 = Y1 + rnorm, x1 = X1 + rnormX2 = rnorm, Y2 = X2 + rnorm, y2 = Y2 + rnorm, x2 = X2 + rnormX3 = rnorm, Y3 = X3 + rnorm, y3 = Y3 + rnorm, x3 = X3 + rnormX4 = rnorm, Y4 = X4 + rnorm, y4 = Y4 + rnorm, x4 = X4 + rnormNumber of Stratas = 4N1 = 1000, N2 = 1200, N3 = 1300, N4 = 1500, n1 = 200, n2 = 210, n3 = 220, n4 = 215, ρYX1 = 0.9950779, ρYX2 = 0.9924618, ρYX3 = 0.9939475, ρYX4 = 0.9903624.Population III.X1 = rnorm, Y1 = X1 + rnorm, y1 = Y1 + rnorm, x1 = X1 + rnormX2 = rnorm, Y2 = X2 + rnorm, y2 = Y2 + rnorm, x2 = X2 + rnormX3 = rnorm, Y3 = X3 + rnorm, y3 = Y3 + rnorm, x3 = X3 + rnormX4 = rnorm, Y4 = X4 + rnorm, y4 = Y4 + rnorm, x4 = X4 + rnormNumber of Stratas = 4N1 = 200, N2 = 200, N3 = 200, N4 = 200, n1 = 22, n2 = 28, n3 = 22, n4 = 25, ρYX1 = 0.9447295, ρYX2 = 0.9214058, ρYX3 = 0.9529337, ρYX4 = 0.912232.Population IV. (Source: [(Source: )Y: Number of teachers, X: Number of students.Number of Stratas = 6N1 = 106, N2 = 106, N3 = 94, N4 = 171, N5 = 204, N6 = 173, n1 = 15, n2 = 15, n3 = 12, n4 = 20, n5 = 23, n6 = 15, ρYX1 = 0.8156414, ρYX2 = 0.8156414, ρYX3 = 0.9011201, ρYX4 = 0.9858761, ρYX5 = 0.7130988, ρYX6 = 0.893595.Population V. (Source: [(Source: )Y: 1983 population(in millions), X: 1982 gross national productNumber of Stratas = 5N1 = 38, N2 = 14, N3 = 11, N4 = 33, N5 = 24, n1 = 17, n2 = 6, n3 = 4, n4 = 12, n5 = 11, ρYX1 = 0.7439544, ρYX2 = 0.969956, ρYX3 = 0.9768227, ρYX4 = 0.2948897, ρYX5 = 0.9011072.Population VI. (Source: [(Source: )Y: 1983 population(in millions), X: 1980 population(in millions)Number of Stratas = 5N1 = 38, N2 = 14, N3 = 11, N4 = 33, N5 = 24, n1 = 17, n2 = 6, n3 = 4, n4 = 12, n5 = 11, ρYX1 = 0.9996193, ρYX2 = 0.9998693, ρYX3 = 0.9998858, ρYX4 = 0.9993071, ρYX5 = 0.9998059.Population VII. ([on VII. Y: Production of wheat(in tons), X: Area of wheat (in hectares)Number of Stratas = 4N1 = 47, N2 = 30, N3 = 29, N4 = 13, n1 = 15, n2 = 10, n3 = 10, n4 = 5, ρYX1 = 0.9583838, ρYX2 = 0.779071, ρYX3 = 0.8719665, ρYX4 = 0.9922591.S1 File(ZIP)Click here for additional data file.


Persistent high fertility is associated with mother and child mortality. While most regions in the world have experienced declines in fertility rates, there are conflicting views as to whether Uganda has entered a period of fertility transition. There are limited data available that explicitly detail the fertility trends and patterns in Uganda over the last four decades, from 1973 to 2011. Total fertility rate (TFR) is number of live births that a woman would have throughout her reproductive years if she were subject to the prevailing age specific fertility patterns. The current TFR for Uganda stands at 6.2 children born per woman, which is one of the highest in the region. This study therefore sought to examine whether there has been a fertility stall in Uganda using all existing Demographic Health Survey data, to provide estimates for the current fertility levels and trends in Uganda, and finally to examine the demographic and socioeconomic factors responsible for fertility levels in Uganda.This is a secondary analysis of data from five consecutive Ugandan Demographic Health Surveys (UDHS); 1988/1989, 1995, 2000/2001, 2006 and 2011. Using pooled data to estimate for fertility levels, patterns and trends, we applied a recently developed fertility estimation approach. A Poisson regression model was also used to analyze fertility differentials over the study period.Over the studied period, fertility trends and levels fluctuated from highs of 8.8 to lows of 5.7, with no specific lag over the study period. These findings suggest Uganda is at the pre-transitional stage, with indications of imminent fertility rate reductions in forthcoming years. Marital status remained a strong predictor for number of children born, even after controlling for other variables.This study suggests there is no evidence of a fertility stall in Uganda, but demonstrates an onset of fertility transition in the country. If this trend continues, Uganda will experience a low fertility rate in the future—a finding pertinent for policy makers, especially as the continent and the country focus on harnessing the demographic dividend. Persistent high fertility remains a public health concern as it impedes efforts to reduce maternal and child mortality –3. FertiBongaarts  using muDivergent views have been raised explaining stalling fertility as a new type of transition , owing tIt is still unclear as to why the fertility rate seems to have stalled in some African countries—even after having substantial fertility declines—while in others the birth rates continue to be high. Most previous studies have examined fertility stall using customized Demographic and Health Survey (DHS) tabulations, where multiple countries at a time are examined using two to three surveys , 16, 28.Bongaarts , while eThis study is based on the technique of reverse survival using information from the children of each mother. This technique provides an estimation of annual age-specific fertility rates for a period of 10 to 15 years before any survey or census. The analysis was based on information obtained from the number of children classified by the age of the mother at the time of the survey. We used the Schoumaker  method oData were sourced from all existing Uganda Demographic and Health Surveys (UDHS) conducted since 1988. The cross-sectional surveys are part of the worldwide DHS project that collects data on comparable demographic and health indicators in selected developing countries. The sample sizes of the surveyed women, aged 15–49 years in Uganda were; 4857 in 1988/89; 7070 in 1995; 7246 in 2000/01; 8531 for 2006 and 8674 in 2011 –43. ThesDHS data were used because they include birth histories of the mother, where the dates of births of all children born to a mother are reported. However, this method can be limited by adoption and misreporting of age, although it was considered to provide the best available data, when accurate age was reported.Date of birth for each respective child;Date of birth for each woman, irrespective of whether the woman had ever given birth andThe date of the survey.Information on children under the age of 10 or 15 years before the survey, classified by single year ages. Specifically:The respective age-specific fertility rates for the three years preceding the survey andTotal fertility for each of the 10 or 15 years preceding the survey.Using five DHS data sets for Uganda for the period 1988/89 to 2011, data quality was assessed for age and date of birth for the children and their respective mothers. This is important to control for age heaping and misreporting which has been reported in demographic and health surveys data .Country-specific demographic indicators were provided and estimate for age-specific fertility rates and TFRs for women for three-year period prior to the survey. Subsequently, fertility reconstruction was performed for over 38 years, using a method proposed by Schoumaker , based owhere:i is the expected number of children born to the mother in each respective time segment;μi is the length of the time or exposure;tf (age) is a function of estimated age; andg (time) is a function of calendar time.Age is a dummy that represents the 5-year age groups of the mother, and calendar time represents annual fertility variations for the respective Ugandan DHSs. The yearly total fertility estimates are presented accordingly in the Table i = this is the value of the dependent variable in observation i, α is a constant, xi is the independent or the explanatory variable in observation i. The coefficient γTo identify factors associated with fertility decline, rate ratios for the socioeconomic indicators were calculated for the two UDHSs (1988/89 and 2011) weighted data. Rate ratios were calculated with an assumption of proportionality of rates, that the age pattern of fertility is similar or constant across the different categories. Categorical variables are used as covariates in the model and age-specific fertility rates were calculated for the respective reference categories and rate ratios of the other categories and variables. These calculations were performed using the adopted tfr2 approach for all the surveys using categorical variables among all women. The rate ratios can be interpreted as ratios of TFRs .Ethical approval was granted by the Uganda National Council of Science and Technology (UNCST), and the School of Statistics and Planning Higher Degrees Ethical Research Committee. Permission was also sought from Measure DHS to access all the data required.Estimates of retrospective TFR and selected socioeconomic and demographic indicators in Uganda are presented in Table Age-specific fertility levels for the three year period preceding the DHS are presented in Fig. Retrospective annual TFR estimates for a 15-year period, for each survey are presented in Table The regression analysis shows variations in the fertility rates, ranging from a high of 8.8 in 1976 to a low of 6.2 in 2010 , 1.06 (2000/01) and a reduced effect of 0.93 in 2006.This paper aimed to analyze the rates, trends and patterns of Uganda’s fertility since 1973 and to determine whether the country’s fertility rate has stalled. This manuscript explored the stage of fertility transition Uganda has reached as well as the demographic and socioeconomic factors determining the pace of fertility transition. In doing this we used all the existing DHS data on the country. Our key finding was that for the last four decades there has been no indication of a fertility stall; fertility rates declined for a period of time then remained constant. For the study period, TFR did not depict a steady pattern but was fluctuating between highs and lows, with no specific time lag duration. The latter conforms to the divergent views and findings regarding fertility stall, particularly among sub-Saharan countries , 17, 35.This study highlights that the country is at a pre-transitional stage, with indications of imminent reductions in TFR in subsequent years. Our findings are supported by the work of Bongaarts , 17, whoThe findings of this study have important policy implications, especially in addressing these issues in a young and growing population. The country should embrace the opportunity to invest in contraceptive programs to accelerate fertility reduction. The increased investment in family planning and its effect on fertility declines have been well documented elsewhere by Westoff and Cross , in addiThe socioeconomic and demographic factors examined, including marital status, education, residence and wealth status had a significant influence on the levels of fertility. For instance, marital status remained a strong predictor, even after controlling for other variables, which explains the role of marriage on the number of children born. Women give birth to more children within a marriage compared with when they are single , 36, 37.Additionally, women living in rural areas had increased fertility rates compared with those living urban areas. The influence of residence cannot be underestimated, given the limited access to adequate health care services, information, family planning messages and education in these areas , 23.There is a need for deliberate government efforts to encourage women to delay marriage and increase uptake of modern contraception in Uganda. Education level, particularly for women, should be enhanced further—up to at least secondary education. Without political involvement to reinforce these key demographic indicators, the situation will not change and high fertility levels will take considerable time to reduce to manageable numbers.The strength of the analysis in this manuscript is that it is based on nationally representative demographic data. The rigorous measures used in the analysis were developed recently for birth history measurement and analysis . FurtherThe findings suggest there is no fertility stall in Uganda but demonstrates an onset of fertility transition where the levels are likely to continue to decline consistently. This study is relevant for policy makers, particularly at this point in time when the country is focusing on embracing the demographic dividend. This occurs when a country has accelerated economic growth resulting from rapid decline of the fertility rates, which affects population age structure. As a reduction in fertility commences, the country ought to facilitate this process with increased investment in education and family planning. This study is the first to have reconstructed fertility levels and trends over a 40-year period, and has established Uganda is commencing a period of fertility transition.


Network analysis identified TNF (tumor necrosis factor), estrogen, and TP53 (tumor protein 53) as the top of 671 upstream regulators (p < 0.001), whereas the SOX2 (SRY [sex determining region Y]-box 2) and OCT4 (octamer-binding transcription factor 4) complex was the top master regulator out of 773 master regulators associated with fertility (p < 0.001). Identification of QTL and genes in pathways that improve early pregnancy success provides critical information for genomic selection to increase fertility in cattle. The identified genes and regulators also provide insight into the complex biological mechanisms underlying pregnancy establishment in cattle.Infertility and subfertility negatively impact the economics and reproductive performance of cattle. Of note, significant pregnancy loss occurs in cattle during the first month of pregnancy, yet little is known about the genetic loci influencing pregnancy success and loss in cattle. To identify quantitative trait loci (QTL) with large effects associated with early pregnancy loss, Angus crossbred heifers were classified based on day 28 pregnancy outcomes to serial embryo transfer. A genome wide association analysis (GWAA) was conducted comparing 30 high fertility heifers with 100% success in establishing pregnancy to 55 subfertile heifers with 25% or less success. A gene set enrichment analysis SNP (GSEA-SNP) was performed to identify gene sets and leading edge genes influencing pregnancy loss. The GWAA identified 22 QTL (p < 1 x 10 Fo. Fo67]. opoiesis . IL6 haselopment .CTNNB1, GJA1, BMP4, WNT2, WNT16, and GATA3) were present in three or more gene sets (CTNNB1), also known as beta-catenin. CTNNB1 is involved in the regulation of GJA1, BMP4, and GATA3 whereas CTNNB1 has a feedback loop with WNT2 and WNT16 in the WNT (wingless) signaling pathway. CTNNB1 plays a crucial role in adherens junctions in cell-cell adhesions and is the central effector in the canonical WNT signaling pathway [WNT signaling is likely an important functional mediator of early pregnancy events in cattle, sheep, mice and humans [Out of 253 unique leading edge genes in nine gene sets associated with heifer fertility, six genes  that are directed by the master regulator SOX2-OCT4 (sex determining region Y box 2 –octamer-binding transcription factor 4) complex .−17) upstream regulator, TNF, directly regulates 66 genes associated with heifer fertility. TNF produces a cytokine protein with systemic and local functions that are elicited in response to infection and inflammation. In the uterus, the primary source of TNF is uterine macrophages and natural killer cells. TNF also stimulates nuclear factor kappa B (NFKB) to trigger inflammation [Rac1 is an upstream regulator of P38 mitogen-activated protein kinases (P38 MAPK) [Rac1 and MAP kinases are leading edge genes in this data set (The most significant (p = 3.3 × 10ammation . Rac1 is38 MAPK)  and bothdata set . Togethe−16) that controls 64 genes associated with heifer fertility. Optimal circulating levels of maternal estrogen and progesterone is critical to maintain implantation in humans and mice, as an imbalance of estrogen can render the uterus unreceptive to implantation [WNT signaling pathway [WNT signaling genes along with beta-catenin are leading edge genes in this data set (Beta-estradiol is an upstream regulator (p = 1.9 × 10antation . Estroge pathway . Many WNdata set . Previou−15). TP53 encodes an anti-apoptotic protein known as a tumor suppressor that regulates the cell cycle and DNA repair. However, TP53 also has a role in female fertility and implantation. Polymorphisms in intron 3 and exon 4 of TP53 were associated with endometriosis and post-in vitro fertilization implantation failure in women due to alterations in leukemia inhibitory factor (LIF) expression [LIF has a diverse role in embryo implantation including transformation of the endometrium to a receptive stage, stromal decidualization, uterine leukocyte migration and regulation of prostaglandin synthesis [A third upstream regulator of fertility was TP53 which controls 60 genes associated with heifer fertility . Transcriptional regulators SOX2 and OCT4 are two main components of the embryonic stem cell pluripotency circuit. These factors may also be important for endometrial regeneration, repair and remodeling during pregnancy in cattle, mouse and human [OCT4 and SOX2 was in the stromal cells in the ipsilateral horn of the uterus [The master regulator identified was the SOX2-OCT4 complex, which regulates 18 upstream regulators and 4 GWAA positional candidates nd human . Regenernd human . OCT4 ane uterus . Highly in vitro produced embryos transferred into recipient heifers. The analysis of SNP data through GWAA, GSEA-SNP and IPA revealed QTL and candidate genes that regulate establishment of early pregnancy in cattle. These approaches facilitate our understanding of the genetics and biology of endometrial receptivity in cattle. Further validation of these results in independent populations are needed before the QTLs identified from this study can serve as fertility markers for genomic selection. Validation of these markers in independent populations will also be helpful in the identification of causal mutations, which is desirable because they provide heightened accuracy when using genomic selection, particularly in multiple breeds.This study focused on the QTL and genes associated with the loss of pregnancy in cattle through failed implantation of the embryo by using Many of the gene sets and pathways identified in this study include genes already implicated in fertility in other species. Further investigation is needed to determine the role of the key upstream and master regulators that control many of the positional candidate genes identified in this study as associated with the establishment of pregnancy. The understanding of these genes, pathways and regulators is expected to help reduce early pregnancy loss in cattle resulting in increased reproductive performance.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.