GutAlive® and Zymo DNA/RNA Shield®, and processed at four different time points. Shotgun metagenomics was used to evaluate the influence of the device and the processing timing on the microbial populations to unravel the potential fluctuations on the composition and diversity of the fecal microbiome and the metabolic pathways profiling. Additionally, RT-qPCR was used to quantify bacterial cell viability for downstream applications of microbiota samples beyond metagenomics.Five stool samples from different donors were collected using two different sampling devices, GutAlive® enables bacterial cell viability overtime preserving DNA integrity, obtaining high-quantity and high-quality DNA to perform microbiome analysis using shotgun metagenomics. Based on the taxonomic profiling, metabolic pathways analysis, phylogeny and metagenome-assembled genomes, GutAlive® displayed greater performance without significant variability over time, showcasing the stabilization of the microbiome preserving the original composition and diversity. Indeed, this DNA stabilization is enabled with the preservation of bacterial viability on an anaerobic environment inside of the sampling device, without the addition of any reagents that interact directly with sample.Our results show that GutAlive® an user-friendly kit for self-collection of biological samples, suitable for microbiome analysis, diagnostics, fecal microbiota transplant and bacterial isolation, maintaining the stability and bacterial viability over time, preserving the original composition and diversity of the microbiome.All the above makes GutAlive The human body harbors a diverse community of microorganisms that live in a symbiotic relationship with the host. In particular, the gut microbiota performs essential physiological functions, such as preventing infection by various pathogens . Subject recruitment was carried out at Microviable Therapeutics SL . Exclusion criteria were having undergone any medical treatment with antibiotics or glucocorticoids in the previous 3 months. Volunteers were informed of the objectives of the study, and all samples were collected and analyzed with the fully informed, written consent of all participants involved in the study and with the approval of the Ethics Committee. Samples were anonymized using an alphanumeric code.Ethics approval (reference CEImPA: 2020-024) for this study was obtained from the Regional Ethics Committee for Clinical Research (Comité de Ética de Investigación del Principado de Asturias), in compliance with the Declaration of Helsinki. Personal data and fecal samples were collected according to the ethical guidelines of the Declaration of Helsinki. Informed consent was obtained from all donors prior to inclusion in the study.®  and DNA/RNA Shield®  kits. All the samples were shipped to Microviable's laboratory facilities at room temperature and received within 24 h of collection. The samples were kept at room temperature (15°C−20°C) and processed at 24, 48, 72, and 120 h after collection  treatment. PMA allows differentiation between intracellular DNA (from viable bacteria) and extracellular free DNA  in the subsequent PCR amplifications. Briefly, for the PMA-treated aliquot, 300 mg of feces were resuspended in 800 μl of PBS and 2 μl of PMA were added, followed by 10' incubation in darkness, 30' incubation under blue light, and 5' centrifugation at 15,000 rpm. Then, DNA extraction was performed using QIAamp® PowerFecal® Pro DNA Kit (Qiagen).For each GutAlive® (1 gram of feces in 900 μl of buffer) were processed following the manufacturer's instructions for DNA extraction with the QIAamp® PowerFecal® Pro DNA Kit using 300 μl of fecal suspension. PMA treatment was not applied to these samples to test bacterial viability, as these commercial kits are only designed to preserve DNA integrity.Samples collected in DNA/RNA ShieldAll extracted DNA samples were quantified with Nanodrop.Escherichia coli DNA for the standard curve. Experiments were performed in triplicate, with 10-fold serial dilutions ranging from 103 colony-forming units (cfu) to 107 cfu of E. coli DNA to generate the standard curves. All real-time PCR runs were performed on the HT7900 Real-Time PCR equipment (Applied Biosystems), with a standard mode default protocol. The amplification reaction was made in a final volume of 20 μl containing 1 × Power Syber® Green PCR Master Mix (Thermo Scientific), 300 nM of each oligonucleotide, and 0.5 ng of total DNA, PMA-treated or not, from each sample and collection time point, in triplicate. Equal amounts of DNA from each sample and time point were used, and the correlation with ng of DNA and grams of fecal samples was calculated.The extracted DNA was amplified using 16s rRNA real-time PCR (qPCR) to validate PCR efficiency, detect potential carryover inhibitors, and compare bacterial viability. The primers used were qPCR1369-F (5′CGGTGAATACGTTCCCGG3′) and qPCR1492-R (5′TACGGCTACCTTGTTACGACTT3′)  v2.3 software (Applied Biosystems), and the cfu was calculated from the y-intercept and slope of the standard curve, as follows: Sample cell count = [(Ct – y-intercept) / slope].®.Since PMA allows differentiation between intracellular DNA (viable bacteria) and extracellular free DNA  at the time of DNA extraction, it allows quantification of the loss of bacterial viability in the samples collected with GutAliveThe DNA samples were sequenced using shotgun metagenomics on an Illumina HiSeq 2,500 with paired-end 150 bp reads (average of 20 million reads per sample) at Eurofins Scientific . Fastq files containing the resulting reads were quality-filtered and cleaned of contaminating reads from the host using KneadData 0.10.0 with default parameters  with default parameters and the CHOCOPhlan v201901 database  were assembled using MEGAHIT v.1.2.9 , displaying no significant differences. Moreover, no significant differences were observed over the 5 days of the study. A minor reduction of 0.4 log units in bacterial viability was detected after 5 days of storage in GutAlive® at room temperature.The average difference between the measurement of total bacteria detected in GutAliveMicrobiome analysis based on shotgun metagenomics was performed at different time points to test the performance of both sample collection devices and the impact on microbiome composition, diversity, and stability over time.p > 0.05), indicating that bacterial diversity was stable over the 5 days of the study for both devices. Similar results were obtained with other alpha diversity indices, such as Chao, Simpson, Inverted Simpson, and Fisher (data not shown). The beta diversity analysis based on the Bray–Curtis dissimilarity index, coupled with principal coordinate analysis, displayed differential clustering of the samples based on the donor (Adonis p < 0.001) and the collection device (Adonis p < 0.004). No significant influence was detected based on the processing time point , despite it not being as specific as in the taxonomy   were constructed for all the samples. The completeness level of the MAGs was equivalent for both devices  , as was ctively)  and the ctively) . Notablyrned 381 .p < 0.001). Functional profiling at the level of MAGs was consistent with the above, showing no differences between the variables studied  in the microbial populations.Microbiome analysis and diagnostics require high-quality DNA to obtain a representative snapshot of the microbial community. There are many user-friendly and small kits designed for this purpose, and one example is DNA/RNA ShieldM microbiome functional analysis based on pathway identification provided consistent results across variables, with similar conclusions to the taxonomic profiling in terms of sample stability over time. The significant donor clustering observed with taxonomic profiling was not as distinct from metabolic pathways, mainly because of the broad core metabolic functions shared among different bacterial taxa  can be found below: BioProject ID: PRJNA909041 (to be released if accepted).The studies involving human participants were reviewed and approved by Comité de Ética de la Investigación del Principado de Asturias (reference: CEImPA 2020-024). The patients/participants provided their written informed consent to participate in this study.IM performed the bioinformatics and statistical analyses. DB performed the laboratory experiments. NM-Á and CH-C supervised the project. All authors approved the final version of the manuscript.


Marine animal biomass is expected to decrease in the 21st century due to climate driven changes in ocean environmental conditions. Previous studies suggest that the magnitude of the decline in primary production on apex predators could be amplified through the trophodynamics of marine food webs, leading to larger decreases in the biomass of predators relative to the decrease in primary production, a mechanism called trophic amplification. We compared relative changes in producer and consumer biomass or production in the global ocean to assess the extent of trophic amplification. We used simulations from nine marine ecosystem models (MEMs) from the Fisheries and Marine Ecosystem Models Intercomparison Project forced by two Earth System Models under the high greenhouse gas emissions Shared Socioeconomic Pathways (SSP5-8.5) and a scenario of no fishing. Globally, total consumer biomass is projected to decrease by 16.7 ± 9.5% more than net primary production (NPP) by 2090–2099 relative to 1995–2014, with substantial variations among MEMs and regions. Total consumer biomass is projected to decrease almost everywhere in the ocean (80% of the world’s oceans) in the model ensemble. In 40% of the world’s oceans, consumer biomass was projected to decrease more than NPP. Additionally, in another 36% of the world’s oceans consumer biomass is expected to decrease even as projected NPP increases. By analysing the biomass response within food webs in available MEMs, we found that model parameters and structures contributed to more complex responses than a consistent amplification of climate impacts of higher trophic levels. Our study provides additional insights into the ecological mechanisms that will impact marine ecosystems, thereby informing model and scenario development. Greenhouse gas emissions from anthropogenic activities have disrupted the natural carbon cycle , impactist century ..22].These six biomass response types were used to qualitatively compare the various MEMs and extract a common signal over all MEMs. We identified the 1° x 1° cells of the global ocean where the majority (7 out of 9 for IPSL and 6 out of 7 for GFDL) of the MEMs analysed in this study projected the same biomass response type by the 2090–2099 decade relative to the reference period, under SSP5-8.5 scenario.Ri, to quantitatively measure trophic amplification. Ri is calculated by dividing the relative change in total consumer biomass by the relative change in NPP in each cell i between the 2090–2099 decade and the reference period:We defined a metric, A value greater than 1 indicates an amplification of NPP changes; a value between 0 and 1 describes an attenuation of NPP changes and a negative value indicates an inversion .Ri. The objective was to test the hypothesis that a greater increase in SST would result in a greater trophic amplification and thus a greater Ri. For each MEM and ESM, the ratio R is represented as a function of the SST increase relative to the reference period 1995–2014. Then, the Ri are averaged across all cells characterised by the same SST increase, regardless of the year. Because ecological processes at play could differ when NPP is expected to increase or decrease, two separate analyses were conducted, thus splitting the cells into two subsets of data depending on the direction of change of NPP. In addition, very low changes in NPP, between -1% and +1%, and extreme values of the R ratio, the 2.5 lowest and highest percentiles, were not considered in the analysis to avoid outliers. Finally statistical tests (Shapiro and Pearson) were implemented to assess variables’ dependence to each other.Since several studies have suggested that trophic amplification arises directly or indirectly from temperature-dependent ecological processes , 25, 27,First, to investigate how trophic amplification propagates through the food web and reaches higher trophic levels, we use all Fish-MIP data available and assess whether or not MEMs demonstrate trophic amplification through the food web. Of the models that provided biomass data by cutting food web segments, biomass values were available either by size class , weight class  or trophic class (EcoTroph). The biomass values expressed by trophic level in EcoTroph were converted from trophic classes to weight classes using the conversion In a second step, in order to compare the outputs of each model, we standardised the values by calculating the relative change in biomass of each class compared to the reference period 1995–2014 similarly as for total consumer biomass:We thus obtained relative consumer biomass change for 6 weight classes for APECOSM, DBPM, EcoTroph and Macroecological , 4 weight classes for BOATS , 3 length classes for EcoOcean , and 2 length classes for FEISTY .Finally, in a complementary analysis , respectively . On a glProjections based on GFDL showed a global amplification, with a larger decrease in total consumer biomass of 16.9%, 16.5%, 13.0%, 10.0%, and 8.0% for five MEMs  compared to NPP projections, by the end of the 21st century. Amplification is barely observed in EcoOcean and DBEM, since they projected a drop in total consumer biomass similar to that in NPP .Under IPSL, MEM projections showed an inversion of the changes between NPP and total consumer biomass with major differences in magnitude. While the lower trophic level biomass driven models, EcoTroph, ZooMSS, APECOSM, DBPM, FEISTY, DBEM, and EcoOcean, projected decreases of 15.0%, 13.7%, 12.8%, 12.1%, 11.4%, 11.4%, and 9.7% in total consumer biomass, respectively, the lower trophic level production driven models, Macroecological and BOATS, projected much larger decreases of 30.0% and 23.3% in total consumer biomass, respectively .MEM projections that are driven by GFDL reveal spatial contrasts in the responses of biomass compared to the climate-induced change in NPP . For allUnder IPSL forcing, total consumer biomass is projected to decrease by 76% ± 10.5% of the ocean surface (across nine MEMs). This biomass decrease is in majority dominated by a negative inversion response 46%, . Where NR) is dependent on biomass response types and MEMs , yet not on ESMs   and increases two and a half more than NPP increases . Where the NPP climate signal is negatively or positively attenuated through the food web, biomass decreases or increases by half of NPP (R = 0.5±0.1) in both cases. Where the NPP climate signal is inverted (negatively or positively), consumer biomass changes twice as much as NPP changes in the opposite direction (R = -2.2 ± 0.7). Finally, MEMs forced by lower trophic level biomass project lower magnitude R than lower trophic level production-forced MEMs (mainly DBPM and EcoOcean).The magnitude of the biomass changes to NPP changes ratio (05) Figs . Where ae.g., exhibit the same biomass response type for at least six models, between 40°S and 50°N latitude. Disagreement among MEMs most often occurs in the polar regions (above 70°N and below 60°S under IPSL and GFDL forcing). Models are globally in agreement across the major parts of the ocean, in 66% and 70% of the ocean’s waters for GFDL  for at least seven models  in the areas where biomass is projected to decrease with 38% and 25% of the ocean surface area corresponding to negative inversion and negative amplification, respectively . FurtherIn the case of an expected decrease in NPP, biomass response types are correlated with the magnitude of warming under GFDL and IPSL  Figs  and S7A.R, the ratio of change is greater than 1). ZooMSS, Macroecological, BOATS and EcoTroph project trophic amplification even where no increase in SST is projected, and amplification increases as warming intensifies. Thus, the decrease in biomass for these models is about two and a half to four times that of NPP when SST increases by more than 2°C. In contrast, EcoOcean, FEISTY, and DBEM, project an attenuation of the NPP signal when SST does not change or increase a little  is observed, with an effect of the warming intensity which offsets the increase in NPP and induces a decrease in total consumer biomass (R>1) in response to temperature increases.In the case of an expected increase in NPP (NPP change > 1%), a negative inversion . All MEMs we considered are forced by temperature , primary producers (production/biomass depending on MEMs) and/or secondary producers .Moreover, seven out of nine of the MEMs we considered are forced by temperature and primary and/or secondary producers , 35, witAs shown in Despite differences in the total consumer biomass responses to changes in NPP, six MEMs exhibit agreeing temporal and spatial patterns from the most complex one, APECOSM, to models of intermediate complexity, FEISTY, ZooMSS and BOATS, to the simplest ones, Macroecological and EcoTroph. Although there are many differences between these MEMs, our study suggested that temperature-driven processes may be pivotal drivers to explain trophic amplification patterns. While temperature has a direct and negative impact on biomass in EcoTroph and Macroecological, interactions of multiple temperature-dependent processes drive the responses of APECOSM, FEISTY, ZooMSS and BOATS. Within these four models, different rates such as growth, mortality and basal metabolism can be affected by temperature. Taken together, these processes generally cause a warming-induced decrease in biomass. For example, in FEISTY, basal metabolism increases faster than ingestion with temperature increases, so warming reduces the scope for growth, leading to a decrease in biomass . In BOATIn contrast to the six aforementioned models, DBEM, DBPM and EcoOcean differ in terms of amplification  and spatial pattern in biomass response to change in NPP Figs  and S4. Although we found a high degree of consistency between the MEMs when examining trophic amplification at the scale of total consumer biomass, breaking down the phenomenon at different size classes (indicative of trophic levels) for each MEM revealed different responses to warming waters. These responses can be partly explained by the representation of the structure and functioning of the food web in each MEM.In EcoTroph, the projections show a continuous and progressive increase in magnitude of changes in biomass (either negative amplification or inversion) when moving up the food web. In this MEM, warming-induced changes in transfer efficiency result in a cumulative decrease in production from one trophic level to the next due to larger energy losses between each . This reFEISTY projected a global amplification of biomass decreases from small (<30cm) to large organisms (>90cm). Metabolic demands increase with both temperature and size such that the reduction in the scope for growth with increasing temperature is worse for large, predatory fishes in FEISTY. This relationship drives the strong negative amplification and its propagation through the food web.DBPM projections show a negative amplification of changes in biomass from small (1-10g) to large (10-100kg) organisms. This reflects the interplay of food and temperature dependent growth and mortality under a combination of warming and declines in phytoplankton at the base of the size-structured food-web. Warming increases feeding rates which translates into faster feeding and higher predation mortality rates, leaving less food available to maintain biomass of larger predators that feed on increasingly large prey. However, the largest organisms considered (>100kg) do not follow this pattern and show a similar change in biomass to that of the smallest organisms (1-10g). The biomass of these largest organisms is low compared to that of the other size classes due to the overriding effects of senescence mortality at very large sizes in the model.On the contrary, APECOSM, BOATS, and Macroecological projections do not show different magnitudes of change between consumer classes since the multiple impacts of warming may either compensate for each other or be independent of size.EcoOcean projected a global dampening of biomass decreases from small (<30cm) to large organisms (>90cm). This repression of climate signals could emerge from the interactions between faster reproducing but less mobile smaller organisms and slower reproducing but faster moving organisms. The resilience of the EcoOcean food web, with higher trophic level groups generally better able to relocate away from warming areas, dampens trophic amplification.In our study, we focused on the changes in NPP relative to consumer biomass. Yet, the gross production of the living biomass is also a key parameter of ecosystem functioning and one of the main drivers of their resilience , 29. In Thus, an approach based on the productivity of ecosystems and not their biomass results in less trophic amplification, which suggests a smaller impact on fisheries potential catches.In our study, we find that depending on low trophic level forcing in each MEM (low trophic level biomass vs. low trophic level production), the magnitude of biomass changes relative to primary productivity is very different , which ii.e., phytoplankton and zooplankton) biomass is projected to decrease under IPSL while NPP is projected to increase.. One of the mechanisms driving this inversion  is when temperature-driven phytoplankton metabolic costs exceed what can be met with the increase in NPP. Under GFDL, the decrease in phytoplankton and zooplankton biomass is projected to be slightly stronger than that of NPP.This study highlighted the relative changes in consumer biomass compared to changes in NPP to investigate trophic amplification. Hence, we compared a production (NPP) to a biomass (consumer biomass). In a supplementary analysis, we explored the changes of low trophic level drivers of each MEM  which have a very different spatial structure and temporal trend  comparedAs shown in The main response to the global decrease in NPP is a negative amplification, resulting in a more pronounced decrease in consumer biomass. Our study suggests that this response may be due to the way temperature influences different physiological and ecological processes in each ecosystem model. When an increase in NPP is projected, the temperature can offset the positive impact by attenuating or reversing the changes in biomass, leading to smaller increases or even decreases in biomass corresponding to positive attenuation or negative inversion respectively. Disaggregation of trophic amplification across different sizes within the food web highlighted a greater decrease in biomass of larger, high trophic levels on average. As a result, there are believable parts of the ocean for which we may see a large climate-induced decline in fishable biomass despite no change or increasing NPP.S1 Table(TIF)Click here for additional data file.S2 Table(TIF)Click here for additional data file.S1 FigShown are global ensemble projections at a 1 × 1 degree resolution. mean 2090s individual model projections of total consumer biomass relative change over the reference period (1995_2014) under: (a) GFDL-SSP5-8.5; and (b) IPSL-SSP5-8.5.(TIF)Click here for additional data file.S2 Figmean changes in Sea surface temperature (a) and net primary production (b) in the 2090s relative to the reference period 1995_2014, under SSP5-8.5 for IPSL.(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(a) Spatial distribution of the total consumer biomass response types, for the nine considered MEMs, forced with IPSL-SSP5-8.5 modelling and (b) magnitude of the different types of change for the seven considered MEMS forced with IPSL-SSP5-8.5 Each response type is estimated at a 1 × 1 degree resolution, from the change in consumer biomass and NPP expected at the end of the century, relatively to the reference period.(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 FigResponse types correspond to at least seven out of nine models in agreement for the coloured cells while grey cells indicate where less than seven models project the same type of response and white cells where no data were available. The percentage numbers in the pie chart correspond to the relative surface areas.(TIF)Click here for additional data file.S7 FigWhere NPP is expected to decrease (a), or increase (b) under IPSL-SSP5-8.5 combination. Grey lines separate biomass response types. (1), (2) and (3) refer to positive inversion, negative attenuation and negative amplification, respectively, while (4), (5) and (6) refer to positive inversion, negative attenuation and negative amplification, respectively.(TIF)Click here for additional data file.S8 FigUnder EcoTroph simulation (first row) FEISTY simulation (second row) and BOATS simulation (third row). The Left and the right plots correspond to GFDL-SSP5-8.5 and IPSL-SSP5-8.5 forcing, respectively. Blue, and red lines correspond to the total consumer biomass change and total consumer production change, respectively.(TIF)Click here for additional data file.S9 FigEnsemble projections of low trophic level drivers change and total consumer biomass changes, relative to 1995–2014, under SSP5-8.5 and for all the considered MEMs with the GFDL-SSP5-8.5 forcing (a) and with the IPSL-SSP5-8.5 forcing (b). For temporal trends, all values are relative to the standardised reference period of 1995–2014. Vertical grey line indicates the last year of the historical period. Full lines correspond to MEMs’ total consumer biomass change and lines with diamonds to MEMs’ low trophic level changes.(TIF)Click here for additional data file.


Management of the inferior parathyroid gland using total thyroidectomy (TT) with central lymph node dissection (CLND) is still controversial. Therefore, we evaluated the safety and effectiveness of single inferior parathyroid autotransplantation.The clinical data of patients with papillary thyroid carcinoma (PTC) who underwent TT with bilateral CLND from January 2018 to December 2020 were collected. Quality of life (QoL) was assessed using the Chinese version of the EORTC QLQ-C30 and THYCA-QOL. The patients were divided into an autotransplantation group and a preservation group according to whether a single inferior parathyroid gland was transplanted. The incidence of permanent hypoparathyroidism, the number of resected central lymph nodes (CLNs), the rate of recurrence reoperation, the rate of radioactive iodine (RAI) treatment, and the QoL score were compared between the two groups.P = 0.532). The median number of resected CLNs was 12 (8–17) and 10 (6–14) in the autotransplantation and preservation groups, respectively (P = 0.015). No reoperations were performed for patients with CLN recurrence, and the rates of lateral lymph node (LLN) recurrence reoperation were 2.0% (2/99) and 3.6% (7/197) in the autotransplantation and preservation groups, respectively (P = 0.473). The RAI treatment rates were 12.1% (12/99) and 22.3% (44/197) in the autotransplantation and preservation groups, respectively (P = 0.034). A total of 276 questionnaires were recovered, including 84 in the autotransplantation group and 192 in the preservation group. The QoL of the two groups of patients is similar (P > 0.05).A total of 296 patients were included in the study; there were 99 patients in the autotransplantation group and 197 in the preservation group. The incidence of permanent hypoparathyroidism was 3.0% (3/99) and 4.6% (9/197) in the autotransplantation and preservation groups, respectively (Single inferior parathyroid autotransplantation during thyroidectomy can be used to prevent permanent hypoparathyroidism and can enable more extensive CLND. In recent years, the incidence of papillary thyroid carcinoma (PTC) has increased substantially, and it has become the most common malignant tumor of the endocrine system in the world –3. HypopAt present, the management of parathyroid glands, especially inferior parathyroid glands, during thyroid surgery is controversial. In this study, PTC patients who underwent total thyroidectomy (TT) and bilateral CLND were included. The effect of single inferior parathyroid autotransplantation on permanent hypoparathyroidism, the number of resected central lymph nodes (CLNs), recurrence reoperation, radioactive iodine (RAI) treatment, and postoperative QoL were retrospectively analyzed to provide a reference for the safety and effectiveness of parathyroid autotransplantation during thyroid surgery.The clinical data of PTC patients who underwent TT with bilateral CLND for the first time in the Department of Thyroid Surgery of the First Affiliated Hospital of Kunming Medical University from January 2018 to December 2020 were retrospectively analyzed. Patients who had single inferior parathyroid autotransplantation and patients who had no parathyroid autotransplantation were included. All patients did not take calcium supplements and vitamin D supplements for a long time before operation. The exclusion criteria included the following: (1) patients aged < 18 years, (2) patients whose preoperative serum parathyroid hormone (PTH) and calcium levels were abnormal, (3) the presence of parathyroid tissue on the thyroid gland during histopathological examination, (4) incomplete follow-up data, and (5) patients who were lost to follow-up. The patients were divided into two groups: the autotransplantation group (single inferior parathyroid autotransplantation) and the preservation group (without autotransplantation). This study was approved by the Ethics Committee of the First Affiliated Hospital of Kunming Medical University (2017 Lun Shen L No. 17).The QoL questionnaires were distributed to the enrolled patients through WeChat, including the Chinese versions of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 (EORTC QLQ-C30) and the Thyroid Cancer-specific Quality of Life (THYCA-QoL).In the initial cohort of 316 patients, 2 patients died of causes unrelated to thyroid cancer at the last follow-up, and 18 patients were lost to follow-up and were excluded from the cohort, with a loss rate of 5.7%. A total of 296 patients were enrolled, including 99 in the autotransplantation group and 197 in the preservation group. A total of 296 questionnaires were distributed; 276 questionnaires were recovered, including 84 questionnaires from the autotransplantation group and 192 questionnaires from the preservation group, with a recovery rate of 93.2%.All surgeries were performed by experienced surgeons (>100 thyroidectomies/year) . LateralDuring thyroidectomy, the superior and inferior parathyroid glands were tried to be preserved in situ. The thymus is normally reserved unless it is invaded by tumor. When a parathyroid gland was nonviable or resected unintentionally, it is cut into pieces and autotransplanted into the sternocleidomastoid muscle after confirmation by intraoperative frozen biopsy.During postoperative hospitalization, patients received daily intravenous calcium supplementation . After discharge, patients were given oral calcium carbonate (600–3600 mg/day) and calcitriol (0.25 μg/day), which could be discontinued after 1 month if there was no symptomatic hypocalcemia. If the postoperative PTH is lower than the normal level for more than 6 months, the patient was diagnosed with permanent hypoparathyroidism . The PTHRAI is not routinely recommended for patients with low risk of recurrence, RAI should be considered for patients with intermediate risk of recurrence, RAI should be routinely recommended for patients with high risk of recurrence, and whether to perform RAI according to the postoperative disease status .U test. Categorical variables were compared using chi-square tests or Fisher exact test. The QoL scores for the EORTC QLQ-C30 and THYCA-QoL scales were calculated according to the official scoring manual of EORTC [P < 0.05 was considered to be statistically significant.IBM SPSS version 26.0 was used for all statistical analyses. Continuous variables with a nonnormal distribution are expressed as the median (interquartile range (IQR)) and were analyzed using the Mann‒Whitney of EORTC . P < 0.0P > 0.05). Table A total of 296 patients with PTC, namely, 54 males 18.2%) and 242 females (81.8%), underwent TT with bilateral CLND. The median age was 43.0 (35.0–49.0) years. LLND was performed in 78 patients (26.4%). There was no significant difference in sex, age, BMI, preoperative PTH, and LLND between the autotransplantation group and the preservation group (8.2% and P = 0.015). Although the number of CLNMs was slightly higher in the autotransplantation group than in the preservation group, the difference was not statistically significant (P > 0.05). There was no significant differences between the two groups in tumor size, multifocality, ETE, T stage, N stage, TNM stage and initial recurrence risk stratification (P > 0.05).The pathological characteristics of the 296 patients are shown in Table P =0.739). There were no reoperations performed for patients with CLN recurrence. There was no statistical difference between the two groups in reoperation of lateral lymph node (LLN) recurrence (P =0.473). The incidence of permanent hypothyroidism in the autotransplantation group and the preservation group was 3.0% and 4.6%, respectively, with no significant difference (P = 0.532). The RAI treatment rate in the preservation group was higher than that in the autotransplantation group, and the difference was statistically significant (22.3% vs. 12.1%) (P < 0.05).The clinical outcomes of the two groups are shown in Table P > 0.05). Table A total of 296 questionnaires were distributed; 276 questionnaires were finally recovered, including 84 questionnaires from the autotransplantation group and 192 questionnaires from the preservation group. The functioning scales and symptom scale scores of the EORTC QLQ-C30 and THYCA-QoL were similar between the two groups . We consider that parathyroid autotransplantation may not increase the incidence of permanent hypoparathyroidism. Therefore, when the inferior parathyroid gland cannot be preserved in situ during thyroidectomy, timely parathyroid autotransplantation is a better choice. Although our research results recommend the use of parathyroid autotransplantation to prevent permanent hypoparathyroidism, more high-quality research is needed to provide evidence for routine parathyroid autotransplantation in light of ethical problems.At present, it is still controversial that parathyroid autotransplantation can prevent permanent hypoparathyroidism after thyroid surgery. Zedenius et al.  proposedP = 0.015). Although the number of CLNMs in the autotransplantation group was slightly higher than that in the preservation group, there was no significant difference. This may mean that intraoperative inferior parathyroid gland autotransplantation can enable comprehensive CLND. Studies have shown that a higher number of resected CLNs at the time of primary surgery in PTC are associated with a lower rate of recurrence [P＜0.001). It is difficult to distinguish the inferior parathyroid gland from enlarged lymph nodes. To avoid damage to the parathyroid gland, “berry picking” CNLD may be performed, in which a complete nodal group within the compartment is not removed; however, this may be the most important reason for CNL recurrence. In our study, no CLN recurrence was observed in the two groups, and only 9 patients had LLN recurrence reoperation, with no significant difference (P > 0.05). Therefore, we cannot evaluate the effect of autotransplantation of an inferior parathyroid gland on the recurrence rate of CLN. We will expand the sample size and further investigate this issue after long-term follow-up in subsequent studies.In this study, we found that the number of resected CLNs in the autotransplantation group was significantly higher than that in the preservation group . However, in this study, the proportion of patients with intermediate and high risk of recurrence after surgery in the two groups is similar. This may be related to higher Tg levels and a lower excellent response rate in dynamic recurrence risk assessment in the preservation group. Research shows that the risk of disease recurrence increases with the increase of postoperative Tg [Heaton et al.  showed tative Tg , 39. ObvIn a questionnaire survey on the QoL of 252 patients with permanent hypoparathyroidism, nearly two-thirds of the patients believed that their hypocalcemia symptoms interfered with their work and life and that their health status was generally poor despite regularly taking calcium therapy . The BütThere were several limitations to this study. First, this study was a single-center retrospective study, and selection bias may have been unavoidable. Second, the number of intraoperative identified parathyroid glands and the number of parathyroid glands retained in situ could not be effectively collected, which led to the difference in complications between the two groups of patients due to the inconsistent number of parathyroid glands. Finally, the absence of continuous Tg values in patients after surgery could not effectively evaluate the specific reasons for RAI treatment in patients in the two groups. Therefore, further larger and prospective studies are needed.In conclusion, single inferior parathyroid gland autotransplantation is associated with higher number of CLNs resected, without demonstrating differences in permanent hypoparathyroidism or structural central neck recurrence. Larger, randomized studies are warranted to better examine the potential benefits and risks of elective parathyroid autotransplantation.


ALMS1 gene. AS may lead to abnormal ciliary formation and function. AS affects metabolism, and symptomatology includes type 2 diabetes mellitus (T2DM), obesity, hypogonadism and gynecomastia in males, progressive bilateral sensorineural hearing loss, cardiomyopathy, nonalcoholic fatty liver disease (NAFLD), cirrhosis, and chronic progressive kidney disease. The onset of the above symptoms may vary significantly. The ophthalmic manifestation is early onset cone-rod dystrophy that starts as progressive vision loss, photophobia, and nystagmus in the first months of life. An accurate diagnosis may enable specialists to facilitate a significantly positive effect in the everyday life of a patient. Genetic counseling may also be recommended for these patients. Diagnosis was confirmed by DNA testing, thus highlighting its necessity in everyday practice.A case of a patient with the Alström syndrome (AS) that was misdiagnosed as Leber's hereditary optic neuropathy or retinitis pigmentosa for 13 years is presented. AS is a rare genetic disorder caused by mutations in the  ALMS1 (Alström syndrome 1) gene [The Alström syndrome (AS), first reported in 1959, is an autosomal recessive genetic disorder caused by mutations in the  1) gene , 3.ALMS1 gene, and as an autosomal recessive condition, both copies of the gene are required to be affected [ALMS1 protein is found in cilia and is a major component of ciliary anatomy; therefore, AS is essentially a ciliopathy, as mutations in both ALMS1 genes lead to abnormal ciliary formation and function [AS is caused by mutations in the affected . The ALMfunction . It has function .T2DM) and obesity, hypogonadism and gynecomastia in males, progressive bilateral sensorineural hearing loss, cardiomyopathy, nonalcoholic fatty liver disease (NAFLD), cirrhosis, and chronic progressive kidney disease [AS constitutes a multisystem condition with variable presentation regarding severity, systems affected, and affected family members . Symptom disease . A numbeT2DM and obesity and liver function tests for cirrhosis), urinalysis, and organ-specific tests  [No treatment is currently available for AS. Management should include detection and monitoring of its complications using primarily blood tests  . Geneticnopathy) .A 5-month-old female infant presented with horizontal and rotational nystagmus of pendular characteristics, occasionally rebounding, which disappeared with fixation on near objects. Nystagmus was noted to be more intense on the hyperopic left eye. Fundoscopic examination was normal bilaterally at the time. Electroencephalography was characterized by sharp theta waves, nystagmus episodes, and slow delta waves during head movements originating from the parietal and occipital lobes. Imaging of the brain with computed tomography (CT) and magnetic resonance (MRI) was normal.12, glucose profile , liver function tests, and complete blood count were normal except for vitamin D insufficiency (low levels of vitamin D3) at various time points. Abdominal ultrasound did not reveal abnormalities in the liver, common bile duct, portal vein, gallbladder, pancreas, spleen, or kidneys. Visual acuity was light perception in both eyes and had different diagnoses from different hospitals (Leber's hereditary optic neuropathy and retinitis pigmentosa).Later in life, the patient was diagnosed with T2DM and obesity as well. Within a 5-year period (2016 to 2021), her metabolic profile, including vitamin BThe patient presented in our department in 2021. She was 17 years old at the time. Best corrected visual acuity (BCVA) was light perception in both eyes. BCVA was light perception in both eyes reportedly since the age of 13. OCT scans of the macular region displayed significant photoreceptor layer disruption in both eyes with minimal preservation of this layer around the central fovea . FluoresFollowing the initial diagnosis, genetic DNA testing established the diagnosis of the Alström syndrome. DNA was isolated from whole peripheral blood, and simultaneous molecular control of the complete coding sequence of 125 genes of known function involved in hereditary retinopathies by the Next Generation Sequencing method  was performed.∗15) and c.(9542+1_9543-1)_(9784+1_9785-1) del involving exons 8 and 11, respectively, of the ALMS1 gene (Alström's syndrome) were detected (MIM 606844). Mutations in the ALMS1 gene are responsible for the Alström syndrome (MIM 203800).Clinical diagnosis was potential congenital malformation according to Leber's hereditary optic neuropathy. Molecular analysis was positive for pathogenic mutations. The heterozygous genetic variants c.4156dup, p.(Thr1386AsnfsALMS1 protein synthesis (protein truncating mutations). One of them, c.4156dup, p.(Thr1386Asnfs∗15) has previously been reported in the international literature but with a different second mutation, while the second mutation reported here, which is recommended for the loss of exon 11 of the gene, has not been previously reported in similar published papers. Consequently, the combination of the two mutations has not been previously reported in the relevant literature.The mutations identified in the genetic material of our patient are predicted to be pathogenic as they cause the premature termination of ∗15) and c.(9542+1_9543-1)_(9784+1_9785-1) del of the ALMS1 gene in the genetic material of our patient is most likely the cause of her condition which according to her genetic diagnosis is the Alström syndrome, which belongs to the group of ciliopathies and is inherited with an autosomal recessive type of inheritance. It should be noted that the phenotypic manifestations of the Alström syndrome can appear at various times in a patient's life. They may also vary greatly both between families and between members of the same family [The presence of the detected mutations c.4156dup, p.Thr1386Asnfs6Asnfs∗15ALMS1 gene mutations. As carriers of an autosomal recessive hereditary disease mutation, these children will be asymptomatic, provided the father is not also a carrier of a pathogenic mutation of the same gene.All the children of our patient must necessarily be heterozygous carriers of one of the two detected VUS) in the RP1 gene . This genetic variant is registered in the international database of clinical genetic data ClinVar with the code 1025517 in the context of clinical control without clarification while it has not been reported in relation to the diseases associated with the RP1 gene in the international literature. Mutations in the RP1 gene are responsible for autosomal recessive and dominant retinitis pigmentosa 1 . The detected mutation is located in exon 2, and based on the available data, it would be associated with the recessive form of the condition for which the presence of two mutations is required. No second or potential pathogen was identified pathogenic mutation in the RP1 gene in our patient. Moreover, there are 2 heterozygotes in the gnomAD international control population genetic databases  which means that this genetic deletion cannot cause severe disease at least in childhood. Consequently, it is not considered possible that this genetic variant is associated with the clinical status of the patient, especially in the light of the identification of the two pathogenic mutations in the ALMS1 gene.Our patient is additionally heterozygous for the genetic variant c.367>T, p.(Arg123Cys) of unclear clinical significance _(9784+1_9785-1) del of exons 8 and 11 of the syndrome . The secAlthough there is no current treatment available for AS, accurate diagnosis is essential. The patient presented here was not correctly diagnosed for 13 years. She was given different diagnoses  and was informed that no symptomatic treatment was available for her condition. The value of DNA testing is apparent in similar cases. An accurate diagnosis using appropriate tests  may enable specialists to bring about a positive effect in the everyday life of a patient .Genetic counseling may also be recommended for patients with the Alström syndrome. Diagnosis was confirmed by DNA testing, thus highlighting its necessity in similar cases in everyday practice.


Phytoremediation of petroleum hydrocarbons in subarctic regions relies on the successful establishment of plants that stimulate petroleum-degrading microorganisms, which can be challenging due to the extreme climate, limited nutrients, and difficulties in maintaining sites in remote locations. A long-term phytoremediation experiment was initiated in Alaska in 1995 with the introduction of grasses and/or fertilizer to petroleum hydrocarbon (PHC)-contaminated soils that were subsequently left unmanaged. In 2011, the PHC concentrations were below detection limits in all soils tested and the originally planted grasses had been replaced by volunteer plant species that had colonized the site. Here, we sought to understand how the original treatments influenced the structure of prokaryotic communities associated with plant species that colonized the soils and to assess the interactions between the rhizospheric and endophytic communities of the colonizing vegetation 20 years after the experiment was established. Metataxonomic analysis performed using 16S rRNA gene sequencing revealed that the original type of contaminated soil and phytoremediation strategy influenced the structure of both rhizospheric and endophytic communities of colonizing plants, even 20 years after the treatments were applied and following the disappearance of the originally planted grasses. Our findings demonstrate that the choice of initial phytoremediation strategy drove the succession of microorganisms associated with the colonizing vegetation. The outcome of this study provides new insight into the establishment of plant-associated microbial communities during secondary succession of subarctic areas previously contaminated by PHCs and indicates that the strategies for restoring these ecosystems influence the plant-associated microbiota in the long term.IMPORTANCE Subarctic ecosystems provide key services to local communities, yet they are threatened by pollution caused by spills and disposal of petroleum waste. Finding solutions for the remediation and restoration of subarctic soils is valuable for reasons related to human and ecosystem health, as well as environmental justice. This study provides novel insight into the long-term succession of soil and plant-associated microbiota in subarctic soils that had been historically contaminated with different sources of PHCs and subjected to distinct phytoremediation strategies. We provide evidence that even after the successful removal of PHCs and the occurrence of secondary succession, the fingerprint of the original source of contamination and the initial choice of remediation strategy can be detected as a microbial legacy in the rhizosphere, roots, and shoots of volunteer vegetation even 2 decades after the contamination had occurred. Such information needs to be borne in mind when designing and applying restoration approaches for PHC-contaminated soils in subarctic ecosystems. Subarctic regions are often impacted by fuel and oil spills associated with petroleum extraction, transportation, storage, and use –5. This –Phytoremediation, the use of plants and plant-associated microorganisms to remediate contaminated areas, can be a cost-effective and sustainable strategy for the ecological restoration of PHC-polluted sites , 10. Unl12–18–(Re)introduction of plants to contaminated areas has been shown to promote ecosystem recovery by increasing soil health through the improvement of soil structure and the accumulation of organic carbon and limiting nutrients, such as nitrogen or phosphorus , 22. Mic23–In this study, we built upon a long-term phytoremediation experiment with PHC-contaminated soils established in Fairbanks, Alaska, in 1995 , 31–33. 31–Picea glauca , Epilobium angustifolium , Achillea millefolium , Salix spp. , Poa spp. , Populus balsamifera , Shepherdia canadensis , Betula neoalaskana , and Trifolium hybridum ) were assessed by Shannon and Simpson alpha diversity indices ; the prokaryotic diversity decreased in the order of rhizosphere, root endosphere, shoot endosphere. Nonmetric multidimensional scaling (NMDS) using weighted UniFrac distances revealed that the communities in the rhizosphere and shoot endosphere clustered separately  in the rhizosphere, while the original phytoremediation strategy significantly influenced the prokaryotic diversity in the root endosphere . In the shoot endosphere, both the original soil type and original phytoremediation strategy were significantly associated with prokaryotic diversity . Fertilization was not found to be significantly associated with prokaryotic diversity in any studied habitat . Additionally, the interaction between the original type of soil and the phytoremediation strategy was found to be significantly associated with the structure of prokaryotic communities in all three habitats studied: the rhizosphere and root and shoot endospheres   was found to be significantly associated with the prokaryotic diversity (MANOVA]) .The dominant effect of the original soil type on prokaryotic communities in all studied habitats was further demonstrated by redundancy analysis (RDA). Prokaryotic communities in both soil types formed distinct clusters in the ordination space and were separated by the first RDA axis . AdditioP > 0.05). It should be noted that since the experimental plots were not uniformly vegetated due to distinct patterns of secondary succession  . The samples of crude oil-contaminated soil and diesel-contaminated soil were analyzed separately, as there was a significant interaction between original soil type and phytoremediation strategy  that were significantly enriched  in subarctic Alaska. The studied soils were originally subjected, in 1995, to different phytoremediation strategies; specifically, they were sown with one or two grass species or were left unplanted , with or without fertilization (−F and +F) , 31, 33.Overall, the rhizospheres, root endospheres, and shoot endospheres of the colonizing plants differed from each other in both the structure and diversity of prokaryotic communities, demonstrating an effect of plant physiology on all microbial habitats studied . Such a 36–43–Lolium multiflorum and Festuca rubra) could have persisted in PHC-contaminated soils to later inhabit the colonizing plants, which could explain the significant association of the prokaryotic communities with the phytoremediation strategies found herein  on the diversity and structure of prokaryotic communities associated with colonizing host plants ; Fig. S1d herein  and 6. Sd herein –53. We tFlavobacterium, Bacillus, Pseudomonas, Corynebacterium, Staphylococcus, Burkholderia, Kocuria, Micrococcus, and Streptococcus  led to the significant enrichment of specific bacterial ASVs associated with colonizing plants. Accordingly, as there was a significant interaction between the original soil type and the phytoremediation strategy , the enrtococcus , 54. Furtococcus –58. It sococcus –, which mFinally, fertilization has previously been shown to stimulate the bioremediation of polluted soils through the enhancement of plant growth and microbial degradation of pollutants –62. Such–Lolium multiflorum and Festuca rubra) as phytoremediation agents. Even after almost 20 years and the disappearance of the initially planted grasses, the interaction of the original soil type and phytoremediation strategy, rather than the current host plant, were among the drivers of prokaryotic communities associated with plants that had successfully colonized the site. Furthermore, each phytoremediation strategy led to the significant enrichment of distinct bacterial ASVs in the rhizosphere and endospheres of colonizing plants, which may have initially helped the colonizing plants to survive and adapt to PHC-contaminated soils. Nevertheless, the reader should be once again reminded that the original treatments influenced patterns of secondary succession at the research site  (+P) or a 1:1 mixture of annual ryegrass and Arctared fescue (Festuca rubra) (+2P) or left unplanted (−P) and were additionally treated with commercially available mineral fertilizer, which was surface applied at approximately 620 g/m2 of N, P, and K (granular 20-20-10) (+F), or left unfertilized (−F)  located in Fairbanks, Alaska, in 1995. The soils used in the experiment included crude oil-contaminated soil collected from a gravel pad at a pump station on the Trans-Alaska pipeline and diesel-contaminated soil collected during the removal of an underground storage tank. In addition to the difference in the original source of PHCs (crude oil or diesel), the soils differed in structure: the crude oil-contaminated soil was gravel with a large grain size, while the diesel-contaminated soil was finer in texture with more organic matter . For thazed (−F) , 31, 33.Picea glauca , Epilobium angustifolium , Achillea millefolium , Salix spp. , Poa spp. , Populus balsamifera , Shepherdia canadensis , Betula neoalaskana , and Trifolium hybridum . Since the site gradually turned from experimental to observational and the experimental plots were not colonized uniformly  in all treatment plots . The ploniformly , not all4 to remove sodium hypochlorite. Twenty-microliter amounts of the final wash solutions were spread on plate count agar (PCA) plates and incubated at 25°C for 2 days to confirm sterility. The plant and soil samples were stored at −80°C prior to further analyses.After the removal of bulk soil, the rhizosphere soil samples were collected by shaking off the soil directly adhering to the roots of harvested plants. The roots and shoots were subsequently surface sterilized by immersion in 2% sodium hypochlorite for 15 min. Immediately after surface sterilization, plant samples were rinsed with sterile deionized water three times and finally washed with 10 mM MgSOTo isolate metagenomic DNA, 0.5 g of the rhizosphere soil and 0.1 to 0.7 g (depending on the quantity harvested) of surface-sterilized roots and shoots were used. Prior to DNA isolation, plant samples were subjected to repeated freezing at −80°C and thawing at 25°C. All plant and rhizosphere soil samples were then homogenized by using the FastDNA spin kit for soil  and the FastPrep instrument for 40 s at 30 m/s followed by 3 min at 15 m/s or for 80 s at 12 m/s, respectively. The DNA was extracted using the same kit following the manufacturer’s instructions and then purified and concentrated using the Genomic DNA Clean and Concentrator-10 kit .GTGYCAGCMGCNGCGG-3′; Sigma-Aldrich, USA) and 926 reverse primer  were used to target the V4-V5 hypervariable region of the 16S rRNA gene  and plastid genes  , 0.02 U μL−1 of Kapa HiFi hot start ready mix , 0.3 μM 515 forward primer, 0.3 μM 1068 reverse primer , 1 μM template DNA (~15 ng), and PCR-grade water   . The theThe resulting PCR products were purified using SPRI magnetic beads , and further library preparation and sequencing analysis on an Illumina MiSeq instrument were performed at the Institute of Arctic Biology Genomics Core Laboratory at the University of Alaska Fairbanks, USA. The concentrations of amplicons were normalized to 1 to 2 ng/μL using a SequalPrep kit , and the samples corresponding to each plate were pooled and either not diluted or diluted 1.5-fold and 3-fold. These three technical replicates of the amplicon libraries were then subjected to 8-cycle PCR to add specific Illumina adapters and sequenced.The sequence data were analyzed using the DADA2 pipeline  and R soR2 values were calculated for all individual variables using the adonis function, and subsequently, the order of the variables in the final PERMANOVA model was sorted in descending order based on their respective R2 values. Redundancy analysis (RDA) based on centered log ratio-transformed data (Padj < 0.01) as implemented in the DESeq2 package (2 fold changes.Further multivariate statistical analyses of sequence data were conducted using the packages phyloseq , vegan , DESeq2 med data  was then package . The funPRJNA771088.The unprocessed FASTQ files for all samples were deposited in SRA under BioProject accession number


We performed single-cell RNA-sequencing (RNA-Seq) of human fetal and adult RPE–choroidal tissues, profiling in detail all the cell types and elucidating cell type-specific proliferation, differentiation and immunomodulation events that occur up to midgestation. Our data demonstrate that progression from the fetal to adult state is characterized by an increase in expression of genes involved in the oxidative stress response and detoxification from heavy metals, suggesting a better defence against oxidative stress in the adult RPE–choroid tissue. Single-cell comparative transcriptional analysis between a patient with intermediate AMD and an unaffected subject revealed a reduction in the number of RPE cells and melanocytes in the macular region of the AMD patient. Together these findings may suggest a macular loss of RPE cells and melanocytes in the AMD patients, but given the complex processing of tissues required for single-cell RNA-Seq that is prone to technical artefacts, these findings need to be validated by additional techniques in a larger number of AMD patients and controls.Age-related macular degeneration (AMD) is the most prevalent cause of blindness in the developed world. Vision loss in the advanced stages of the disease is caused by atrophy of retinal photoreceptors, overlying retinal pigment epithelium (RPE) and choroidal endothelial cells. The molecular events that underline the development of these cell types from  Age-related macular degeneration (AMD) is a progressive degenerative disease of the macular region of the retina, leading to decline and eventual loss of central vision . It is tThe choroid is a highly specialized systemic vascular network, lying just beneath the RPE, between Bruch’s membrane and the sclera. It is composed of three layers: the inner most layer (choriocapillaris), middle (Sattler’s) and outer layer . The choriocapillaris is a continuous layer of fenestrated capillaries with surrounding pericytes organized in a lobular arrangement and is the principal blood supply to the RPE and photoreceptors. A key characteristic of CECs is the presence of multiple fenestrations, allowing for directional flow of oxygen and nutrients by diffusion from the choroid to the RPE and to the inner segments of photoreceptors , and thein utero. Two studies have shown an association between choroidal health and birth weight, body length and gestational age (The development of the choroid in humans starts from the fourth week post conception (PCW) . Fate maonal age ,10, demoAs with most tissues, the choroid undergoes various changes during the ageing process, with these being more severe in patients with AMD. One of these changes is reflected in a decrease in choroidal thickness with age, with the choroid underneath the fovea reducing by ~3 μm per year in healthy adults (age 21–85 years old) . In wet To date, most gene expression studies of choroid and RPE have used mRNA or protein samples from pooled RPE–choroid as these two tissues are difficult to separate. Furthermore, the cellular diversity within the choroid has prevented assignment of unique gene expression signatures to individual cell types. The advances made in the single-cell gene expression and chromatin accessibility studies have provided a powerful approach to study gene expression patterns in adult RPE and choroid ,17; howeet al. , bipolar (characterized by expression of VSX1) and amacrine cells (characterized by expression of TFAP2A), iris pigmented epithelium , ciliary muscle , red blood cells  and corneal epithelium (characterized by expression of KRT12) was observed in some of the samples, but not all. Several neural crest-derived cell clusters representing fibroblasts, pericytes and periocular mesenchyme were observed in all samples , cluster 9 expressing typical smooth muscle cell markers , cluster 11 expressing periocular mesenchyme markers  and cluster 4 expressing at high level nerve associated fibroblast markers [SFRP4, PI16  as well as a ciliary muscle cluster 24, with high expression of PAX6, WNT2B and muscle related markers such as MYL9, MYH11 and DES. Immune cells, including T and B cells (clusters 22 and 23), mast cells (cluster 21) and macrophages  were recovered from the tissue, along with Schwann cells (cluster 14) and numerous CECs which formed two-closely related clusters 13 and 19, characterized by high expression of VWF, ICAM2 and CD34. Similar to separate samples, retinal cells including rod photoreceptor precursors (cluster 16) and ganglion cells (cluster 17) were identified in the integrated analysis alongside abundant red blood cells .The reads from the four fetal samples 12, 16, 20, 21 PCW) were integrated using the harmony batch correction . The cel, 21 PCW ectively . We werey  and those involved in complex I  analysis was carried out to identify the genes whose expression changes significantly during the development of the RPE–choroid tissue. To this end, genes expressed in the RPE, CECs, pericytes, smooth muscle, Schwann cells and melanocytes were compared between the earlier (12 and 16 PCW) and later (20 and 21 PCW) stages of development of the second trimester , Fig. 2. and RGR , encodin and RGR , demonst and RGR , which g and RGR  , but also some genes encoding HLA-class II proteins (HLA-DRA and HLA-DMA), suggesting activation of antigen presentation pathway at midgestation. CECs transplantation in AMD patients has not been carried out, however given the expression of both HLA-class I and II peptides on fetal CECs, appropriate strategies must be designed to avoid transplanted cell rejection because of host’s immune response.The human choriocapillaris expresses human leukocyte antigen (HLA)-class I self-peptides at high levels , howeverIt is known that the choroid is richly innervated by parasympathetic, sympathetic and trigeminal sensory nerve fibres that regulate choroidal blood flow in birds and mammals . SchwannPMEL and TYRP1, demonstrates the presence of differentiating melanoblasts as early as 12 PCW of human choroid development  displayed high expression of IGFBP5 and the closely situated pericytes (cluster 1) were distinguished by high expression of ANGPT1 and ACTN1. Immune cells were characterized by high expression of CD69 in T cells (cluster 7), CD79A in B cells (cluster 15), KIT in mast cells (cluster 16) and CD68 and CD14 in macrophages (clusters 6 and 8). The CECs (cluster 12) were easily identified by the high expression of VWF, whereas smooth muscle cells (clusters 10 and 14) were defined on the basis of high expression of ACTA2 and TAGLN. Two clusters of Schwann cells (PLP1: those were defined as myelinating (cluster 17) and non-myelinating (cluster 5) by the high expression of MPZ and SCN7A, respectively  were amongst the top 10–20 most upregulated genes in the adult RPE, CECs, Schwann cells, melanocytes, pericytes and smooth muscle cells  and HLA-class II , suggesting an upregulation of antigen presentation and processing pathway in the adult RPE, CECs, Schwann cells, melanocytes and smooth muscle cells.To assess changes in gene expression profile between fetal and adult RPE–choroid tissues we performed DEG analysis . Strikinle cells , which cterparts , indicatID1, ID4, ANGPT1, HES1, CD34, PDGFRA, PDGFRB, CD34, KDR) and extracellular matrix genes  in the fetal CECs and pericytes, respectively, when compared with the adult equivalent cells  and antigen processing and presentation  in the adult CECs and pericytes. Such replicability of findings indicates that gene expression changes between the fetal and adult cell types identified in the RPE–choroidal tissue are reflective of underlying biological differences during fetal, postnatal and ageing process.A recently published study  comparinnt cells . Other ket al. was assessed in the pericytes and RPE cell clusters  to be significantly upregulated in macular RPE cells of the AMD patient when compared with unaffected subject . The cadherins are calcium-dependent cell adhesion proteins, involved in the structural and functional organization of cells in various tissues. The integrity of RPE cells is maintained in part by adherens junctions, which are composed of cadherin homodimers and p120-, β-, and α-catenins linking to actin filaments. Mutations in four cadherin, namely CDHR1, CDH23, PCDH15, CDH3, the latter being the dominant RPE cadherin, have been identified as causes of inherited retinal degeneration  was also enriched in the CECs of early atrophic AMD donors studied by Voigt et al. (average log2 fold change 0.47). Matrix Gla protein encoding gene (MGP) was also the highest differentially expressed and upregulated gene in melanocytes of AMD patients in both studies. Although our DEG lists contained less genes this could be because of the fact that only one AMD patient was compared with one unaffected subject in our study versus 12 AMD versus 10 controls in the Voigt et al. study. In addition, our DEG lists were filtered for P adjust < 0.05, whereas this or FDR filtering was not applied to the published comparative study , others are in the process of terminal differentiation  or acquiring features  that will define their characteristics in the adult.in vitro model, Kim et al. have shown that oxidative stress first affects retina in a persistent manner  in a larger number of AMD patients and controls.Previous studies in the field have compared gene expression at the single-cell level between AMD patients and unaffected subjects ,60. HoweRGCC and SPARCL1) that induce or respond to apoptosis in endothelial cells of AMD patients  until 21–22 PCW. In addition, the sample collection post-mortem time was different between each sample, which could lead to retention of ribosomal RNA; however we did not see large variances between the samples with regard to ribosomal content, except the macular and peripheral samples taken from the intermediate AMD patient, which were processed within 1.5 h post eye retrieval and showed the lowest ribosomal content. Given the scarcity of RPE and choroid tissues from patients with intermediate AMD, we were unable to perform validation studies at the protein level.In summary our data provide a rich resource for the community to investigate gene expression within each of these cell types present in the RPE–choroid tissue during development as well as the healthy adult and AMD disease setting. The raw and processed data from this analysis has been deposited to gene expression omnibus (accession number GSE210543) to enable other researchers in the field to use and expand on these data and to gain new insights that would facilitate the treatment of AMD.. RPE and choroid tissues from whole eyecups of five adult donor tissues and four fetal samples from 12 to 21 PCW terminations were used for scRNA-Seq. The eye globes were dissected in Hank's Balanced Salt Solution (HBSS) immediately after collecting tissue. Two further eye samples from one person with intermediate AMD and one unaffected subject were obtained through exenteration procedures for advanced facial cancers performed at Royal Victoria Infirmary (RVI), Newcastle . The human fetal material was provided by the Joint MRC/Wellcome Trust (MR/R006237/1) Human Developmental Biology Resource  under etSupplementary Material, Table S1The excised RPE and choroid tissues were dissociated to single cells using a multi tissue dissociation kit (Miltenyi Biotec). Dissociation time varied from 15 to 45 min depending on the age of the tissue and a gentleMACS Dissociator (Miltenyi Biotec) was used to aid dissociation of the adult tissue. Cell count and viability were monitored using a Tali Image-Based Cytometer and Viability Kit (Thermo Fisher Scientific). For scRNA-Seq, cells were captured, and libraries generated using the Chromium Single Cell 3′ Library & Gel Bead Kit, version 3 (10× Genomics). scRNA-Seq libraries were sequenced to 50 000 reads per cell on an Illumina NovaSeq 6000.CellRanger version 3.01 was used to de-multiplex samples, align to the human genome (GRCh38) and create gene expression matrix. Quality of the samples was assessed and any cells with fewer than 1000 reads, 500 genes or 20% mitochondrial reads were removed before further analysis . DoubleFThe integrated datasets were clustered using a resolution of 0.6. Marker genes were identified using the Seurat FindMarkers function. Using these marker genes, clusters were then assigned cell types and visualized using UMAP. Differentially expressed genes were generated using the FindMarkers function. A core analysis from Qiagen IPA was used to investigate pathways associated with the differentially expressed genes.Conflict of Interest statement. The authors declare no conflict of interest.Figure_S1_ddad007Click here for additional data file.Figure_S2_ddad007Click here for additional data file.Figure_S3_ddad007Click here for additional data file.Figure_S4_ddad007Click here for additional data file.Figure_S5_ddad007Click here for additional data file.Figure_S6_ddad007Click here for additional data file.Figure_S7_ddad007Click here for additional data file.Figure_S8_ddad007Click here for additional data file.Table_S1_ddad007Click here for additional data file.Table_S2_ddad007Click here for additional data file.Table_S3_ddad007Click here for additional data file.Table_S4_ddad007Click here for additional data file.Table_S5_ddad007Click here for additional data file.Table_S6_ddad007Click here for additional data file.Supplementary_information_final_for_submission_ddad007Click here for additional data file.


Several risk factors are related to glycemic control in patients with type 2 diabetes mellitus (T2DM), including demographics, medical conditions, negative emotions, lipid profiles, and heart rate variability . The interactions between these risk factors remain unclear. This study aimed to use machine learning methods of artificial intelligence to explore the relationships between various risk factors and glycemic control in T2DM patients. The study utilized a database from Lin et al. (2022) that included 647 T2DM patients. Regression tree analysis was conducted to identify the interactions among risk factors that contribute to glycated hemoglobin (HbA1c) values, and various machine learning methods were compared for their accuracy in classifying T2DM patients. The results of the regression tree analysis revealed that high depression scores may be a risk factor in one subgroup but not in others. When comparing different machine learning classification methods, the random forest algorithm emerged as the best-performing method with a small set of features. Specifically, the random forest algorithm achieved 84% accuracy, 95% area under the curve (AUC), 77% sensitivity, and 91% specificity. Using machine learning methods can provide significant value in accurately classifying patients with T2DM when considering depression as a risk factor. The prevalence of diabetes mellitus was 10.5% among individuals aged 20–79 years in 2021, and it is expected to increase to 12.2% by 2045. Type 2 diabetes mellitus (T2DM) accounts for the largest portion of DM cases. The global diabetes-related health expenditures were about 966 billion USD in 2021 and are estimated to reach 1054 billion USD by 2045  found thPoor glycemic control was related to sociodemographic factors , medical status , lifestyle , and complications . ResearcStudies utilizing machine learning methods to predict diagnostic outcomes for chronic diseases have become increasingly popular ,8. In thThe current state of research suggests that negative emotions (such as anxiety and depression) have not been fully considered in machine learning applications for predicting diabetes, despite evidence suggesting a connection between these factors and diabetes ,31,32. TParticipants were recruited from the Department of Internal Medicine at Kaohsiung Medical University Hospital and Kaohsiung Municipal Siaogang Hospital. A total of 647 patients aged over 20 years old and diagnosed with T2DM completed the study between 25 August 2020 and 30 May 2021. The original data used traditional statistical analysis to explore the association between cardiac autonomic activity and glycemic control in T2DM and was published by Lin et al. . There wThis study used machine learning of artificial intelligence to figure out the best prediction model for multiple risk factors in glycemic control. Patients with a pacemaker or arrhythmia were excluded since these conditions cannot correctly analyze the HRV indices. The institutional review board was approved by the ethics committee of Kaohsiung Medical University Hospital (KMUHIRB-E(I)-20200194), and informed consent was obtained from each patient before the study.Demographic data, including age, gender, and BMI, were collected. All participants were measured with the Patient Health Questionnaire-9 (PHQ-9)  and GeneSubsequently, the electrocardiography (ECG) signals were collected by using a lead II QOCA portable ECG monitoring device , which was approved by the Ministry of Health and Welfare, Taiwan (Number 005428). The five-minute ECG was measured for each patient at a sitting and resting baseline. The ECG device was connected to a Samsung Galaxy Tab A 10.1 SM-T515 , which raw ECG signals that were then uploaded to the QOCA platform .Blood samples included the lipid profiles  and HbA1c and were obtained from the electronic medical records system during the three months since the ECG had been measured. All the blood samples were measured at at least 12 h fasting.Researchers checked the ECG waveform and deleted arrhythmia and movement artifacts. The interbeat interval (IBI) data were downloaded from the QOCA platform through Python software  and then imported to the CardioPro Infiniti HRV Analysis Module , which transformed the IBI data into the time and frequency domains of HRV. The time domain of HRV included standard deviation of normal-to-normal intervals (SDNN), root mean square of the successive differences (RMSSD), number of pairs of successive NNs that differ by more than 50 ms (NN50), and percentage of NN50 (pNN50) . The fren = 495) and the normal glycemic control group .To investigate which multiple risk factors could predict HbA1c values and determine whether machine learning methods were suitable for accurately classifying poor and normal glycemic control groups, the study used the following statistical analyses. Firstly, regression tree analysis was conducted, which has been used in previous studies to identify risk factors that predict health outcomes ,41,42. ITo determine the best machine learning method for accurately classifying poor or normal glycemic control groups, we compared several classification techniques, including support vector machine (SVM), boosting, classification tree, neural network, K-nearest neighbors, and random forest. To avoid biased estimation due to unequal class sizes between groups , we balaTo compare the models, we examined their sensitivity, specificity, area under curves, and classification accuracy rate with the testing data. Sensitivity represents the proportion of correctly classified positive cases, while specificity represents the proportion of correctly classified negative cases. The AUC is the area under the receiver operating characteristic curve, which plots sensitivity against 1-specificity. We used the R packages rpart  and machn = 495; 50%) and the poor glycemic control group . The regression tree analysis identified multiple risk factors that could lead to higher values of HbA1c. The optimal fit of the model was achieved using the complexity parameter (cp) of 0.01. The starting node indicated that the average HbA1c value of the group was 7.2. The analysis suggested that patients with multiple risk factors had higher HbA1c values than those with only one risk factor. The following were the key findings:The regression tree analysis used the full 647 samples. In classification tree analysis, the balanced sample was the normal glycemic control group .Group 2: Patients with SDNN < 14 and BMI ≥ 34 had high HbA1c values (9.5%), although it was less than the first group.Group 3: Patients with almost the same conditions as the first group but with lnVLF ≥ 2.6 had less high HbA1c values (7.9%).Group 4: Patients with SDNN < 14, BMI < 34, age ≥ 54 years old, and LDL ≥ 107 had higher HbA1c values (8.6%).Group 5: Patients with multiple risk factors, including SDNN < 14, 25 ≤ BMI < 34, age ≥ 54 years old, LDL < 107, and HDL < 37 had higher HbA1c values (8.3%).Group 6: Patients with SDNN ≥ 14, TG ≥ 72, LDL ≥ 145 had high HbA1c values (8.1%).Group 7: Patients with SDNN ≥ 14, TG ≥ 72, LDL < 145, PHQ-9 score ≥ 3, and BMI ≥ 23 had HbA1c values of 7.5%.The analysis suggested that multiple risk factors interacted with each other to result in higher HbA1c values. The results showed that one risk factor alone did not necessarily lead to higher HbA1c values. Note: BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; lnVLF, natural logarithms of very low frequency; PHQ-9, Patient Health Questionnaire-9; SDNN, the standard deviation of normal-to-normal intervals; TG, triglycerides. In each node, the number denotes the predictive mean HbA1c value of individuals within that group, while the percentage represents the proportion of individuals in that node relative to the total sample size.A gap in the previous literature was the limited inclusion of depression and anxiety as predictors of diabetes. In this study, we found evidence suggesting that depression can be an important factor in certain subgroups of T2DM. Although the causality between depression and diabetes is not clear, previous studies have reported a high comorbidity rate between these two diseases ,50. WhilThe results of the regression tree in this study provide valuable information on how multiple factors interact to create subgroups within diabetes patients and can be informative for developing prevention strategies for T2DM. Traditional regression analysis is useful in identifying risk factors  but doesThe second analysis aimed to provide further insight into the comparison of different machine learning methods for predicting diabetes, as the results from previous studies were diverse ,22. Our This study has several limitations that should be considered when interpreting the results. First, the sample size of 647 patients with T2DM may be considered small for artificial intelligence machine learning analysis. Although we used balanced methods to increase the sample size of the normal glucose group to have an equal class size, the sample size was only up to 990. Previous studies have shown that machine learning can still be potentially biased with a sample size of fewer than 1000. Therefore, future studies should aim to increase the sample size to confirm the risk factors for T2DM identified in the regression tree model. Second, the data on blood glucose and lipid profiles were collected only once, which may not fully represent the long-term glycemic control for patients with T2DM. Collecting blood samples multiple times over a long period of time and exploring possible risk factors for poor glycemic control may provide more accurate results. Third, while we found that depression is a risk factor for a subgroup of T2DM patients, the causality between depression and diabetes is still not fully clear. Despite these limitations, our comparison of machine learning methods suggested that random forest is a more accurate method for analyzing small sets of features and is particularly useful when depression is included as a risk factor. Further research can extend these findings by using cross-lagging models with longitudinal data to better understand the relationship between depression and diabetes.In conclusion, our study identified key risk factors and pathways for poor glycemic control in patients with T2DM using the regression tree algorithm. The random forest approach found that multiple risk factors are important in screening, monitoring, diagnosis, and prevention of poor glycemic control, such as considering demographic data, physiology dimensions , and emotional dimensions (depression and anxiety). The multiple risk factors can also be considered as a framework for designing potential intervention programs in future studies.


In molecular biology studies, the selection of optimal reference genes is of vital importance for accurately quantifying gene expression. The purpose of the present study was to screen the most stable reference genes in different muscle tissues of New Zealand white rabbits and Yufeng yellow rabbits.HPRT1, ACTB and PPIC, while HPRT1, PPIC, and RPL13A were the most stable reference genes in muscle tissues of Yufeng yellow rabbits. However, in the longissimus dorsi muscle and the abdominal wall muscle of both varieties, the most stable reference genes were HPRT1, RPL13A, and SDHA. In the quadriceps femoris muscle, the most stable reference genes were ACTB, HPRT1, and SDHA. Furthermore, the relative abundance of MYOG, MYH3 and MSTN was used to confirm the suitability and reliability of the selected most stable reference genes and the most unstable reference gene. Results revealed the same expression patterns of these myogenic genes when normalized according to the most stable genes, while normalization against the unstable reference gene altered the observed expression patterns.Results indicated that the most stable reference genes in the muscle tissues of New Zealand white rabbits were HPRT1, PPIC and SDHA presented high stability among all examined reference genes; thus, the combined analysis of HPRT1/ PPIC/ SDHA gene provides the best reference for RT-qPCR in muscle tissues of New Zealand white rabbits and Yufeng yellow rabbits, while HPRT1 is a better choice than other reference genes when using a single reference gene to assess target gene expression. Our results provide basic data for better measuring target gene expression profiles in muscle tissues of rabbits.Taken together, our results demonstrated that the most stable reference genes varied among different muscle tissues and different breeds of rabbits. However, The online version contains supplementary material available at 10.1186/s40850-022-00159-0. With the development of molecular biology technologies, the quantification of gene expression has been performed with omics technologies at different levels. However, reverse transcription quantitative real-time PCR (RT-qPCR) remains the most commonly used method to measure the transcriptional abundance of target genes due to its high efficiency, sensitivity and specificity. The reliability of RT-qPCR depends on the normalization of mRNA abundance by using selected, stably expressed reference genes. Thus, the selection and use of stable reference genes in RT-qPCR analyses are key for determining the accurate expression patterns of genes.GNB2L1, HPRT1, RPL32, ACTB and B2M were demonstrated to be the most stable reference genes in a screen of expression patterns in human neutrophils [SF3A1, EEF1A2 and HMBS were suitable reference genes [YWHAZ, GAPDH and SDHA were suitable reference genes for bovine preimplantation embryos [Stable reference genes have been reported in many studies in humans and animals.  embryos . These rACTB), peptidylprolyl isomerase C (PPIC), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta-2-microglobulin (B2M), succinate dehydrogenase complex flavoprotein subunit A (SDHA), hypoxanthine phosphoribosyltransferase 1 (HPRT1), ribosomal protein L13a (RPL13A) and 18S ribosomal RNA (RN18S). These reference genes are also known as “housekeeping genes” because of their important role in maintaining the basic physiological functions of cells. Among these genes, ACTB, GAPDH, HPRT1, RN18S and other reference genes have been widely used for northern blot hybridization, conventional PCR analysis and semiquantitative analyses [GAPDH and ACTB, varies in different tissues, cells, and developmental stages [To date, the most commonly used reference genes in rabbits include actin beta  were used to evaluate the stability of these candidates as reference genes [MYOG), myosin heavy chain 3 (MYH3) and myostatin (MSTN) was quantified in rabbit muscle tissues to validate the stability of the selected reference genes. The assessment of reference gene stability will be beneficial for more accurately measuring target gene expression profiles in rabbit muscle tissues.However, there is few reports about stable reference gene analysis in rabbit muscle tissues , 11. In ce genes –15. Thislongissimus dorsi muscle, abdominal wall muscle and quadriceps femoris muscle tissues were collected and frozen in liquid nitrogen until analysis.In this experiment, the rabbits were purchased from rabbit breeding grounds of Yufei, Zhengzhou, China. Five male New Zealand white rabbits  and five male Yufeng yellow rabbits  were selected. Rabbits used in the present study were randomly selected and they did not come from the same pair of parents and did not relate with each other. A randomized outbreeding protocol is used to maintain genetic diversity. Rabbits were raised in separate cages (35 cm × 45 cm × 45 cm) and kept the temperature at 20–25 °C under a 12 h: 12 h  photoperiod. All rabbits were in good health and provided ad libitum access to commercial pellet diets (probiotics and antibiotics free) and water. Rabbits were sacrificed using an overdose of isoflurane . After slaughter, the Total RNA isolation was performed by using TRIzol  according to the manufacturer’s instructions. The qualitative and quantitative assessment of the isolated RNA was performed using a spectrophotometer . RNA integrity was assessed by the electrophoretic analysis of 28S and 18S rRNA subunits . Primers . The 10 μL reaction consisted of 5 μL ChamQ™ Universal SYBR® qPCR Master Mix, 0.2 μL forward primer, 0.2 μL reverse primer, 2 μL cDNA, and 2.6 μL ddH0, 5− 1, 5− 2, 5− 3, and 5− 4) of six pooled cDNA preparations from different muscle tissues (ng/μL). To ensure the compatibility of the PCR assays, three independent serial dilutions were evaluated, which enabled us to determine the R2 values and PCR efficiency (E) of the individual assays and calculate the correlation between them.To calculate PCR efficiency, standard curves were generated from assays conducted with five-fold serial dilutions  were calculated with the eq. E = 10(− 1/slope)-1 to determine whether the candidate reference gene primers could be used for RT-qPCR analysis.Calibration curves were drawn with the logarithm of the sample concentration as the in-dependent variable and Cq as the dependent variable. IBM SPSS Statistics 26.0 software was used to calculate the calibration curves of the primers and to test the degree of fitting for the regression equation for RMYOG, MYH3 and MSTN by the 2-ΔΔCq method. Gene expression stability was assessed via one-way ANOVA (with Dunnett’s post hoc where indicated for multiple comparisons). Single-treatment comparisons were conducted with Student’s t-test. Statistical significance was defined as P < 0.05.LinRegPCR (version 2021.2)  was used2) varied from 0.970 to 1.000  and standard deviation (SD) values among the reference genes were acquired via BestKeeper program analysis Tables  and 2. RHPRT1 was the most stable reference gene in the three muscle tissues of New Zealand white rabbits, followed by PPIC and ACTB, while the most stable reference genes in Yufeng yellow rabbits were HPRT1, PPIC, and RPL13A  were the most appropriate reference genes for quantifying target gene expression in rabbit muscle tissues.To determine whether there is a significant difference in target gene expression normalized to the most stable reference genes versus the least stable reference genes, we analyzed the expression of the top three reference genes normalized to the most stable and their geometric mean as well as the unstable reference genes for -ΔΔCq algorithm was utilized to calculate the expression of genes relative to reference genes, and the results calculated with this method depend on the stable expression of reference genes; thus, screening appropriate reference genes is of great importance for accurately normalizing the data and thus obtaining reliable results. However, many studies perform RT-qPCR using reference genes without validation, selecting reference genes by referring to other investigations or arbitrarily selecting commonly used empirical reference genes. The significance of stably expressed reference genes for obtaining accurate RT-qPCR results is always underestimated. Nevertheless, there is almost no reference genes that are stably expressed in different cell types [In molecular biology studies, gene expression analysis provides a reliable theoretical basis for complex gene regulation research. The 2ll types , 18 or dll types . The reaThe geNorm, NormFinder and BestKeeper programs are frequently used tools for selecting suitable reference genes, and the stability of reference genes calculated by different programs is different to some extent. According to geNorm software compiled by Vandesompele et al. , the appHPRT1, and it may be selected when a single reference gene is used for RT-qPCR in the muscle tissues of rabbits. When multiple reference genes are selected for calibration, it is recommended that HPRT1, PPIC and SDHA be used together for normalization. HPRT1 is reported to be involved in cell metabolism; the stability of HPRT1 has been more commonly recognized, and it has often been selected as a reference gene in recent studies. The PPIA, H2AFZ, and HPRT1 genes have been proven to be the most stable reference genes in mouse oocytes and preimplantation-stage embryos under different culture conditions [HPRT1 and RPL13A have been demonstrated to be the most stable reference genes in glioma stem cells (GSCs) [GAPDH and HPRT1 were highly stable reference genes in a rabbit model of left ventricular diastolic dysfunction (LVDD) [HPRT1 presented the highest stability [This study confirmed that the stable expression of reference genes differed both among different muscle tissues of rabbits and in the same muscle tissue between different breeds of rabbits. However, our comprehensive analysis revealed that the most stable reference genes were nditions , while Hs (GSCs) . W. Nachn (LVDD) . By comptability .ACTB gene is stable only in the quadriceps femoris muscle. The ACTB gene, which encodes actin, is involved in the synthesis of the cytoskeleton structures. The metazoan actin cytoskeleton supports a wide range of contractile and transport processes [ACTB is a major protein component in striated muscle fibers and a major component in muscle filaments, which plays an important role in cell secretion, migration, cytoplasmic flow and separation, and is highly con-served among different species. Both the ACTB and HPRT1 genes are stably expressed in different tissues of pigs [ACTB shows stable expression in the feline endometrium [ACTB, GAPDH and RN18S are genes with highly variable expression and that their use as reference genes in different tissues of Capra hircus should be avoided [The expression of rocesses . Moreove of pigs , and ACTometrium . However avoided , 28.GAPDH gene is the most commonly used reference gene for correcting the expression of target genes. However, an increasing number of studies have shown that GAPDH is not a stable reference gene [ACTB and HPRT1 genes were ranked at the top two genes according to comprehensive gene expression stability analyses, while the GAPDH gene was proven to be unstable. GAPDH encodes an enzyme that catalyzes glycolysis for energy and carbon molecules [GAPDH in the T cell lineage promotes angioimmunoblastic T cell lymphoma through an NF-κB-dependent mechanism [GAPDH and RN18S did not show sufficient stability in this study, and we assume that there are great fluctuations in the expression of GAPDH and RN18S during the course of muscle development. It was speculated that the GAPDH gene participates in important oxidation processes in life activities and that its importance varies in different tissues and developmental stages.The nce gene , 29, 30.olecules . The oveechanism . The preMYOG, MYH3, and MSTN in muscles. MYH3 is a major contractile protein in muscle tissue [MYOG is a myogenic regulatory factor that plays a key role in myoblast differentiation [GAPDH and B2M) were significantly different from those that normalized by HPRT1 and SDHA. It was apparent that the expression profile showed a similar trend among the stable reference genes, and the results that we obtained were in line with expectations.To test this hypothesis, the relative gene expression values of the most stable reference genes and the most unstable reference genes were plotted on the basis of normalization against to the geometric average values of e tissue . MYOG isntiation . MSTN, antiation , 36. We HPRT1, PPIC and SDHA were the most stable reference genes, and combined analysis of these three reference genes can be used for RT-qPCR normalization in the muscle tissues of rabbits, while the HPRT1 gene is a suitable reference gene for assessing target gene expression on the basis of only one reference gene. This work assessed the stability of reference genes in muscle tissues of rabbits and will provide a solid foundation for further studies of molecular regulation in the muscle tissues of rabbits.In summary, our data indicated that the optimal reference genes differed among various muscle tissues and between different breeds of rabbits. The results of our comprehensive analysis demonstrated that Additional file 1: Suppl. Table S1. Name, accession number, sequences, amplicon length of primer pairs used in the present experiment, and references. Suppl. Table S2. Standard curve parameters for the candidate genes. Fig. S1. Agarose gel electrophoresis of target products. Marker (left): DL2000 . Marker (right): DL2000 . Fig. S2. Every gene displayed a single peak profile in the melting curve analysis. -d (RFU)/dT: Negative derivative ratio of fluorescent signal to temperature. Fig. S3. To validate the most suitable reference gene with myogenic regulatory genes. The relative expression of MSTN (A) in the longissimus dorsi muscle. The relative expression of MYOG (B) in the abdominal wall muscle. The relative expression of MSTN (C) in the quadriceps femoris muscle. The relative expression of MYOG (D), MYH3 (E), and MSTN (F) in New Zealand white rabbits. The relative expression of MYOG (G), MYH3 (H), and MSTN (I) in Yufeng yellow rabbits. * p < 0.05, ** p < 0.01. Fig. S4. Agarose gel electrophoresis of RNA. Marker: DL2000 ; 1 and 4: the longissimus dorsi muscle tissue; 2 and 5: the abdominal wall muscle tissue; 3 and 6: the quadriceps femoris muscle tissue. Fig. S5. Optimal number of reference genes for normalization under different experimental conditions. Vn/Vn + 1 indicates the pairwise variation (V) between two sequential normalization factors (NFn and NFn + 1), used to determine the optimal combination of reference genes required for accurate normalization. Values less than 0.15 suggest that another reference gene will not be required for the normalization of gene expression. Pairwise variation analysis (V) to determine the optimal number of reference genes for data normalization in three muscle tissues of New Zealand white rabbits (A), three muscle tissues of Yufeng yellow rabbits (B), the longissimus dorsi muscle tissue of New Zealand white rabbits and Yufeng yellow rabbits (C), the abdominal wall muscle tissue of New Zealand white rabbits and Yufeng yellow rabbits (D), and the quadriceps femoris muscle tissue of New Zealand white rabbits and Yufeng yellow rabbits (E).Additional file 2.