The values of the pollution load index (PLI) were more than one (>1), indicating progressive deterioration of the sediment quality. The enrichment factor (EF) for all the studied metals suggests their enrichments in sediments of the Badovci Lake. Most of the elements were found in the residual fraction strongly bonded to the crystalline component. Pb, Mn, and Cu were bound in the organic and exchangeable components. The extent of pollution by heavy metals in sediments of the Badovci Lake implies that the environmental condition is relatively stable, and attention should be paid to metals bonded in the extractable and organic phases. It is recommended to periodically monitor water and sediment quality.The concentrations of thirteen metals  were analyzed in waters and sediments of the Badovci Lake. The total metal concentrations in the water followed the descending order: Fe > Al > Mn > Cu > Ba > Zn > As > Ni > Pb > V > Co > Cd > Cr, and the total metal content in the sediments also followed the descending order: Fe > Al > Mn > Ni > Cr > Pb > Ba > Zn > V > Cu > As > Co > Cd. According to EC 98/83, Al, Fe, and Mn at some sampling sites exceeded safety limits for drinking water, whereas other elements were at acceptable levels. The total content of Cr, Cu, Ni, Pb, and V in the sediments exceeded the target values of the New Dutch List. Using pollution indicators such as the contamination factor (CF) and geoaccumulation index (I The quality of sediments influences the water quality in the aquatic environment. Pollution of the aquatic environment by heavy metals has acquired increasing attention due to endurance in the sediment, toxicity, and organic accumulation that can influence human health and ecosystems . Accordi2, the average depth is 29 m, and the surface elevation is 655 m [Recent work is focused on water and sediment quality of the Badovci Lake, Republic of Kosovo . Badovciis 655 m . The Badis 655 m .Samples were collected from ten different locations S1–S10 during April 2018 in the Badovci Lake. Ten samples of sediments were taken from the bottom sediments and ten samples of water from one meter above the bottom sediments . The samμm pore size, evaporated, and then acid solution digested according to USEPA 1994 method 200.7 [The chemicals used for the preparation of the solution were of analytical grade, while water was redistilled. The Teflon vessel and polypropylene containers were cleaned, soaked in 5% nitric acid for 24 h, and then rinsed with redistilled water. For metal analysis, previously, an acidified water sample was filtered through a Whatman filter paper with a 0.45 od 200.7 . Accordiod 200.7 , the seqR2 > 0.999. The multielement standard solution 11355‐ICP, IV Merck, was used to prepare standard solutions of 1.0, 5.0, and 10.0 μg/L as a tunning solution covering a wide range of masses of the elements. For each measure, a run included blank samples. The samples were analyzed in triplicate to eliminate any specific error.Heavy metal samples were analyzed by ICP-AES with a Varian 715-ES ICP optical emission spectrometer equipped with a CETAC ultrasonic nebulizer (ICP/U-5000AT+) for better sensitivity at the Institute of Chemistry, Methodius University, Skopje, North Macedonia. Preparing the calibration curve, the multielement standard was used with geo (index of geoaccumulation).The data selection in the interpretation of the geochemical results plays an important role , 23. ThrAccording to the study in , 25, an The contamination factor and pollution load index are also used to evaluate the status of heavy metal contamination , 27. Congeo). According to the study in [geo is calculated asn, Bn expresses background data of the toxic metal n, and 1.5 is a factor of possible lithological changes [geo values can be interpreted as follows: Igeo ≤ 0, unpolluted; 0 ≤ Igeo ≤ 1, unpolluted to moderately polluted; 1 ≤ Igeo ≤ 2, moderately polluted; 2 ≤ Igeo ≤ 3, moderately to heavily polluted; 3 ≤ Igeo ≤ 4, heavily polluted; 4 ≤ Igeo ≤ 5, heavily to extremely polluted; and 5 ≤ Igeo, extremely polluted.An assessment degree of contamination by the toxic metals could be determined by the geoacumulation index , Fe (63–87%), Ni (63–80%), V (75–83%), Cr (73–84%), Ba (16–74%), Zn (35–69%), and Cu (56–58%). Pb dominated in the Fe-Mn oxide fraction (22–40%) and in the organic fraction (33–39). Manganese dominated in the descending order of Fe-Mn oxide (31–48%), carbonate (19–24%), and exchangeable (3–23%) fractions .These results suggested that Al, Fe, Ni, V, and Cr in the sediment of the Badovci Lake had the strongest associations with crystalline sedimentary components and were likely to reflect the geological characteristics. The concentration of Mn connected with fractions F1, F2, and F3 was dominant and under changing environmental conditions, considering the geological structure of soil , 32; Mn geo values showed the decreasing order of Ni > Pb > Cr > V > Cu > Ba > Zn > As. The sediment in the Badovci Lake indicates the uncontaminated to moderately contaminated status, whereas the Igeo values for Ni, Pb, and Cr indicate that the sediment is moderately contaminated. The low levels of the background sample and higher concentration levels in the analyzed sediment may result in higher values of these metals.The calculated PLI values for metals in the sediment are summarized in geo values in geo values are approximately the same.Due to the highest EF values and higher values of labile fractions in the sediments, attention should be paid to Pb, Ni, As, and Cr for their mobility and bioavailability in the aquatic ecosystem. According to the study in , concentgeo showed high values. Compared to the regional lakes, the EF values were much higher in the Badovci Lake, especially very high for Pb, Ni, Cu, and Cr, which indicates anthropogenic impact as a result of mining activity. It is recommended to periodically monitor the sediment and water quality.Based on the obtained results from the analysis of waters of the Badovci Lake, it was found that the concentrations of Al, Fe, and Mn in the water exceed the limits set under Directive EC 98/83. Also, the comparison with research data made in the year 2016 in the same lake, as well as the comparison with the Batlava Lake or other similar lakes, shows an increase in the concentration of these elements  in the water. The sediment analysis showed that the total content of metals such as Pb, Cr, Cu, V, and Ni exceeded the limits according to the Dutch List guidelines. The average content of the total Pb was 167 mg/kg that is about twice the Dutch Target and Intervention Values, Cr 276 mg/kg twice and half, Cu 61.2 about twice, V 88.2 twice, and Ni 305 mg/kg about nine times. On the basis of the fractional analysis of metals, it was shown that Pb, Cu, and Mn were bonded to the exchangeable and organic fractions, and the possibility to pass in the water is evident in the cases of changing environmental factors. Pollution indicators such as EF, CF, and I


Saccharomyces cerevisiae are used for industrial production of succinic acid. Optimal process conditions for dicarboxylic‐acid yield and recovery include slow growth, low pH, and high CO2. To quantify and understand how these process parameters affect yeast physiology, this study investigates individual and combined impacts of low pH (3.0) and high CO2 (50%) on slow‐growing chemostat and retentostat cultures of the reference strain S. cerevisiae CEN.PK113‐7D. Combined exposure to low pH and high CO2 led to increased maintenance‐energy requirements and death rates in aerobic, glucose‐limited cultures. Further experiments showed that these effects were predominantly caused by low pH. Growth under ammonium‐limited, energy‐excess conditions did not aggravate or ameliorate these adverse impacts. Despite the absence of a synergistic effect of low pH and high CO2 on physiology, high CO2 strongly affected genome‐wide transcriptional responses to low pH. Interference of high CO2 with low‐pH signaling is consistent with low‐pH and high‐CO2 signals being relayed via common (MAPK) signaling pathways, notably the cell wall integrity, high‐osmolarity glycerol, and calcineurin pathways. This study highlights the need to further increase robustness of cell factories to low pH for carboxylic‐acid production, even in organisms that are already applied at industrial scale.Engineered strains of  2, and slow growth on S. cerevisiae and highlights the need to further increase robustness of cell factories to low pH for carboxylic‐acid production.This study dissects adverse physiological effects of low pH, high CO Production of the free acid prevents the need for coproduction of large quantities of gypsum  limits consumption of substrate for biomass formation, which benefits product yields. However, a trade‐off of this strategy is that the fraction of the energy substrate allocated to cellular maintenance increases with decreasing specific growth rate, thereby leaving less substrate available for energy‐dependent product formation  of S. cerevisiae can be affected by the cultivation conditions  and elevated CO2 concentrations (50% CO2). Additionally, cultures were grown under ammonium‐limited, energy‐excess conditions at low pH. Quantitative analysis of rates, yields, and culture viability was used to dissect physiological impacts of low pH and high CO2. Furthermore, transcriptome analysis was employed to elucidate regulatory responses to these conditions.Although elevated CO22.1S. cerevisiae CEN.PK113‐7D  or an in‐line mix of 50% compressed air and 50% pure CO2 . The two gas flows were precisely controlled with mass flow controllers  and mixed in a ratio of 1:1. A detailed description of preculture preparation, bioreactor operation, and medium composition is given in Supporting Information Appendix 1.Glucose‐limited chemostat and retentostat cultures were grown in 2‐L bioreactors  at a working volume of 1.4 L, essentially as described by Vos et al. . Chemica2 and O2 concentrations in the off‐gas with a frequency of 5–8 hr, but were sampled regardless of the oscillations. These oscillations subsided upon approaching severe calorie restriction in the retentostat phase after 3 days.Chemostat cultures were assumed to be in steady state when, after at least five volume changes under the same process conditions, culture dry weight (see below) changed by less than 4% over two consecutive volume changes. Glucose‐limited cultures grown at pH 3 showed oscillations of CO2.32 production rate and residual glucose and ethanol concentrations in the effluent differed by less than 5% over three consecutive volume changes.Ammonium‐limited retentostats grown at pH 3 were preceded by a chemostat phase under the same nutrient limitation, essentially as described before . For glucose‐limited cultures, biomass concentrations were determined by filtering duplicate, exact volumes of culture broth, diluted to an approximate concentration of 2.5 g biomass/L, over predried Supor 47 membrane filters with a 0.45 μm pore size  as described by Postma, Verduyn, Scheffers, and Van Dijken (Concentrations of On Dijken . Biomass2.5Viability measurements in retentostats were based on colony‐forming unit (CFU) counts, which indicate reproductive capacity of single cells  was set to a fixed value of 0.5 gX/gS. This analysis generated quantitative estimates of specific growth rate and glucose consumption rates during the first, dynamic phase of retentostat cultivation  under accession number GSE133136.Detailed descriptions of sampling procedures  were investigated at near‐zero growth rates in retentostat cultures. In these retentostat cultures, a filter in the effluent line enabled full biomass retention  as the retentostats, but without cell retention  can provide strong indications for differences in maintenance‐energy requirements  at 0.025 hr−1, S. cerevisiae invests ca. 20% of the consumed glucose in cellular maintenance , the biomass yield of 0.419 ± 0.009 gx/gs measured in the present study was entirely consistent with the yield observed by . These results showed that the higher ms in retentostat cultures grown at high CO2 and low pH resulted from the low pH rather from the high CO2.Irrespective of culture pH, increasing CO2, low pH led to a strongly reduced viability of glucose‐limited chemostat cultures. Conversely, increasing the CO2 levels did not significantly affect culture viability. Assuming that cells measured as nonviable did not contribute to biomass formation or glucose consumption, specific rates were corrected for viability based on PI staining, resulting in higher specific growth rates (μ) and biomass‐specific substrate uptake rates . The differences between the ATP‐hydrolysis rate at pH 3 derived from MFA  than in the corresponding glucose‐limited cultures  > 2 and false‐discovery rate (FDR) < 0.005, see Section 2 alone , pH 3 alone  and 50% CO2 and pH 3 combined  , while NCE103, encoding carbonic anhydrase, was downregulated. Among 48 genes that were differentially expressed in response to low pH, both at high and low CO2 . Of the latter 12 genes, five  encode cell‐wall proteins, with PIR4, TIP1, SRL1, and TIR2 described as mannoproteins. An unexpectedly large transcriptional response, involving no fewer than 598 genes .A set of 13 genes that, irrespective of culture pH, were differentially expressed in response to high CO2 Figure b, β cons2 Figure b, δ, gen2 Figure c, δ. ThiPDR12, which encodes a plasma‐membrane transporter in weak organic acid tolerance . Elevated CO2 (50%) did not, by itself, affect the biomass yield or viability of S. cerevisiae as compared to those under reference conditions . Indeed, robustness of S. cerevisiae to various other stresses is inversely correlated with growth rate , high‐osmolarity glycerol (HOG) and calcineurin signaling pathways (de Lucena et al., 2 is relayed through sphingolipid‐mediated sensing, via the kinases Pkh1 and Pkh2, to the central nutrient sensor Sch9 (Pohlers et al., 2 (Figures 2 signaling, in S. cerevisiae, two interactions between the abovementioned signaling pathways could provide further leads of investigation. First, CWI is sensed by the GPI‐anchored nano‐spring Wsc1 (Dupres et al., 2 signal to Sch9 are also essential for Pkc1 activation of the CWI pathway (Inagaki et al., 2 conditions bicarbonate accumulates intracellularly, improves buffering of the cytosol, and attenuates the cytosolic pH (Buck & Levin, 2 and low pH signaling (Mascaraque et al., 2 and low pH conditions by the pH‐dependent GFP‐derivative pHluorin (Orij et al., While neither synergistic nor antagonistic physiological effects of low pH and high CO2 Figure a. In par Figures f and 6g.2 is unlikely to be a major concern for the development of robust yeast cell factories for production of dicarboxylic acids. Instead, minimizing maintenance‐energy requirement and death rate at low pH was identified as a major objective for strain improvement. Even in the absence of product formation, low pH was shown to augment the trade‐off, at low specific growth rates, between a reduced allocation of substrate to biomass formation and increased relative contribution of maintenance‐energy requirements. The strongly increased ms at low pH is clearly disadvantageous for industrial scale production of dicarboxylic acids and, moreover, is likely to be further enhanced in the presence of high product concentrations. For example, high concentrations of organic acids have been shown to cause increased maintenance‐energy requirements at low pH (Abbott et al., S. cerevisiae constitute a trade‐off between fermentation costs and costs for downstream processing. The complexity of the observed physiological and transcriptional responses indicates that improving robustness under industrial conditions is unlikely to be achieved by individual genetic modifications. Instead, exploration of yeast biodiversity (Palma, Guerreiro, & Sá‐Correia, The present study indicates that sensitivity to high COSupplementary information.fvClick here for additional data file.Supplementary informationClick here for additional data file.Supplementary informationClick here for additional data file.Supplementary informationClick here for additional data file.Supplementary informationClick here for additional data file.


Our purpose was to establish and validate a nomogram model in early hepatocellular carcinoma (HCC) patients for predicting the cancer‐specific survival (CSS).We extracted eligible data of relevant patients between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Further, we divided all patients into two groups  at random (7:3). Nomogram was established using effective risk factors based on univariate and multivariate analysis. The effective performance of nomogram was evaluated using concordance index (C‐index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic curve (ROC).We selected 3620 patients with early HCC including the training cohort  and the validation cohort . The nomogram‐related C‐indexes were 0.755 (95% CI: 0.739–0.771) and 0.737 (95% CI: 0.712–0.762), in the training and validation cohorts, respectively. The calibration plots showed good consistency of 3‐and 5‐year CSS between the actual observation and the nomogram prediction. The 3‐, 5‐year DCA curves also indicated that the nomogram has excellent clinical utility. The 3‐, 5‐year area under curve (AUC) of ROC in the training cohort were 0.783, 0.779, respectively, and 0.767, 0.766 in the validation cohort, respectively. With the establishment of nomogram, a risk stratification system was also established that could divide all patients into three risk groups, and the CSS in different groups  had a good regional division.We developed a practical nomogram in early HCC patients for predicting the CSS, and a risk stratification system follow arisen, which provided an applicable tool for clinical management. Our purpose was to establish and validate a nomogram model in early hepatocellular carcinoma (HCC) patients for predicting the cancer‐specific survival (CSS). We developed a practical nomogram in early HCC patients for predicting the CSS, and a risk stratification system follow arisen, which provided an applicable tool for clinical management. HCC rank as the sixth most common cancer and the fourth leading reason for cancer‐related mortality worldwide.As we all know, the American Joint Committee on Cancer (AJCC) TNM staging system is often used to evaluate the survival prognosis of HCC patients.Nomogram‐based clinical modeling is a reliable statistical predictive model, through comprehensive analysis of all tumor‐related risk factors, it can accurately calculate and predict the survival rate of different patients.Our study aimed to develop and validate a practical nomogram for predicting the CSS of patients with early HCC by integrating some significant variables in order that clinicians can make better decisions.22.1Data of clinical‐related for patients diagnosed with HCC between 2010 and 2015 were extracted from the SEER 18 registry database (1973–2015) by SEER*Stat 8.3.6 software. Data included baseline demographic, tumor characteristics, therapeutic method, stage at diagnosis, survival time, and so on. Inclusion criteria included: patients with early HCC . Exclusion criteria included: (a) unknown whether surgery was performed; (b) no first tumor; (c) unknown marital status or domestic partner; (d) unknown race; (e) unknown histological grade; (f) unknown insurance; (g) other tumors death and unknown cause of death. Ultimately, 3620 patients with early HCC from the SEER database were included and further analyzed in our study. Below variables were included analysis: baseline demographics , tumor features , and treatment strategy . The patients in the study included three race groups: white, black, or other ; age of patients were grouped as two groups: ≤65 and >65 years old; in treatment, included with or without surgery, with or without radiotherapy, with or without and unknown chemotherapy; and marital status were classified into “married,” “single (never married),” “divorced/separated,” and “widowed.” In addition, we analyzed using the seventh edition AJCC TNM staging.2.2n = 2536, 70% of total cases) and validation cohort . The nomogram model was structured using the training cohort, and the validation cohort was used for validation. We performed univariate and multivariate analysis to obtain for significant factors that significantly affect CSS (p < 0.05) and further construction of nomogram. We used the concordance index (C‐index), calibration curves, decision curve analyses (DCA), and receiver operating characteristics curve (ROC) for the validation of the nomogram. The C‐indexes were used to reflect the performance and predictive accuracy of the nomogram. Calibration plots at 3 and 5 years (1000 bootstrap resamples) were drawn to compare the predicted CSS with the CSS observed in our study, and the 45‐degree line is used as the actual result of the first‐rank model. ROCs were generated for the sensitivity and specificity of nomogram. The values of the C‐index or AUC ranges from 0.5 to 1.0, with 0.5 representing the random probability and 1.0 representing the perfect ability to accurately judge the result. DCAs were drawn to evaluate the clinical practicability of the nomogram. Moreover, according to the cutoff value that the total score calculated from the nomogram among the training cohort patients, all eligible patients were classified into three groups . Kaplan–Meier curves and the log‐rank test were used to compare the CSS of patients in the different groups. Data were extracted using SEER*Stat software version 8.3.6. We used statistical software IBM SPSS Statistics, Version 25.0  to execute univariate and multivariate regression analyses. The C‐indexes, calibration plots, ROCs, and DCA curves were generated using R version 3.6.3 with relevant packages. The cutoff value that the total score was calculated by X‐Tile, version 3.6.1. The Forest plot of showing univariate and multivariate analysis results and Kaplan–Meier curves were drawn using GraphPad Prism 8. When p‐value is less than 0.05, it is statistically significant.We randomly divided the total cases into two groups in a 7:3 ratio, which consisted of the training cohort (33.1n = 2356), validation cohort (n = 1084)  identified in univariate analysis in the training cohort , race (p = 0.001), grade (p < 0.001), T stage (p < 0.001), surgery (p < 0.001), chemotherapy (p < 0.001), tumor size (p < 0.001), and marital status (p = 0.038) were independent prognostic factors for CSS  variables included in the nomogram, the influence of pathological grade for CSS is the largest. Margonis GA and colleagues indicated that poorly differentiated HCC was associated with worse prognosis.In addition, baseline demographics variables  were identified as prognosticators of HCC. Some studies showed young patients with early HCC had a better prognosis,Up to now, there are several staging systems have been constructed about HCC, such as Barcelona Clinic Liver Cancer (BCLC) staging, American Joint Committee on Cancer (AJCC) TNM staging, Chinese University Prognostic Index (CUPI), and so on. The above staging systems are generally more practical for the prognosis of all HCC stages. However, our nomogram shows a satisfactory ability of predicting the CSS of early stage HCC. Santambrogio et al. revealed that C‐indexes of BCLC staging system for early HCC in the training and validation cohort were 0.6479 and 0.6323, respectively, and AUCs in the training and validation cohort were 0.5949 and 0.5873, respectively.Based on the nomogram, the formation of a risk stratification system, which could distinctly divide all patients into three risk prognostic groups. In addition, we also drew DCA curves to assess the clinical practicability of nomogram. A study indicated that the DCA curve could evaluate whether medical decisions and strategies based on nomogram could facilitate survival prognosis of patients to reflect the clinical value of the nomogram.In summary, based on the significant risk factors identified in the analysis, we developed a practical and reliable nomogram for predicting the CSS of the early HCC patients. This nomogram was helpful to guide clinicians to individualize the treatment for patients with early HCC, and provided the basis for the clinical management of high‐risk patients, showing great potential for clinical application. Despite internal validation was performed, external validation on the relevant early HCC data set should be considered.The authors have no conflicts of interest to declare.The approval process of Institutional Review Board was waived because of the de‐identified information of the patients included in the Surveillance, Epidemiology, and End Results database.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Table S1Click here for additional data file.


Both enantiomers of lactate can be converted with acetate into the presumed beneficial butyrate by a phylogenetically related group of anaerobes, including Anaerobutyricum and Anaerostipes spp. This is a low energy yielding process with a partially unknown pathway in Anaerobutyricum and Anaerostipes spp. and hence, we sought to address this via a comparative genomics, proteomics and physiology approach. We compared growth of Anaerobutyricum soehngenii on lactate with that on sucrose and sorbitol. Comparative proteomics revealed complete pathway of butyrate formation from sucrose, sorbitol and lactate. Notably, a gene cluster, lctABCDEF was abundantly expressed when grown on lactate. This gene cluster encodes a lactate dehydrogenase (lctD), electron transport proteins A and B (lctCB), nickel‐dependent racemase (lctE), lactate permease (lctF) and short‐chain acyl‐CoA dehydrogenase (lctG). Investigation of available genomes of intestinal bacteria revealed this new gene cluster to be highly conserved in only Anaerobutyricum and Anaerostipes spp. Present study demonstrates that A. soehngenii and several related Anaerobutyricum and Anaerostipes spp. are highly adapted for a lifestyle involving lactate plus acetate utilization in the human intestinal tract.The  While all these SCFAs confer health benefits, butyrate is used to fuel colonic enterocytes and has been shown to inhibit the proliferation and to induce apoptosis of tumour cells  bacteria, these are important cross‐feeding metabolites in the diet driven trophic chain existing in the intestinal microbiome . Evidence for a molecular mechanism of conversion of lactate to acetate was first demonstrated in the acetogenic model organism Acetobacterium woodii, where for every molecules of lactate converted to acetate only 1.5 molecules of ATP were generated  is active. Extensive search of publicly available bacterial genomes revealed that this gene cluster is highly conserved in several Anaerobutyricum and Anaerostipes spp. among the butyrate producers from the human intestinal tract. This genomic organization suggests that both Anaerobutyricum species and Anaerostipes caccae and also a recent human infant isolate Anaerostipes rhamnosivorans  strain L2‐7 was grown routinely in a medium as described previously , KH2PO4 (0.41 g/l), MgCl2.6H2O (0.1 g/l), CaCl2.2H2O (0.11 g/l), cysteine‐HCL (0.5 g/l), 1 ml hemin , 0.2 ml vitamin K1 solution  and trace elements I, trace elements II and vitamin solutions. The trace elements I  solution contained the following (mM): 0.1 Na2SeO3, 0.1 Na2WO4, 0.1 Na2MoO 4 and 10 NaOH. The trace elements II (acid) solution was composed of the following (mM): 7.5 FeCl2, 1 H3BO4, 0.5 ZnCl2, 0.1 CuCl2, 0.5 MnCl2, 0.5 CoCl2, 0.1 NiCl2 and 50 HCl. The vitamin solution had the following composition (g/l): 0.02 biotin, 0.2 niacin, 0.5 pyridoxine, 0.1 riboflavin, 0.2 thiamine, 0.1 cyanocobalamin, 0.1 p‐aminobenzoic acid and 0.1 pantothenic acid. This basal medium was supplemented with 30 mM of sodium acetate . For routine use, the medium was distributed in 35 ml serum bottles sealed with butyl‐rubber stoppers and incubated at 37°C under a gas phase of 1.7 atm (172 kPa) N2/CO2 . The pH of the medium was adjusted to 7.0 before autoclaving. The vitamin solution was filter sterilized and added to the media bottles after autoclaving.A. soehngenii was pre‐cultured in medium with 60 mM glucose and inoculated (5%) in 450 ml of media in triplicates  containing 20 mM sucrose, 40 mM d‐sorbitol or 80 mM d,l‐lactate respectively. In all three conditions, 30 mM of acetate was added in the medium as it is shown to improve growth of A. soehngenii . The column used was Shodex SUGAR SH1011. For the mobile phase, 0.01 N H2SO4 was used to which 30 mM of crotonate was added as internal standard.Actively growing For whole cell protein extraction, cells were thawed on ice and washed with reduced phosphate buffer solution (PBS with 0.2 mM Titanium (III) citrate) twice before extraction of proteins. The cell pellet was resuspended in SDS‐DTT‐Tris‐Lysis buffer, and cells were disrupted using a French pressure cell (1500 psi). For every sample, the cells were passed through the French pressure cell three times (cell disruption checked under light microscope). After cell disruption, the samples were placed immediately on ice. The extracted proteins were denatured by heat treatment (95°C for 30 min). Protein quantification was done using the Qubit™ Protein Assay Kit . A total of 50 μg of proteins plus 5 μl of loading dye was mixed and electrophoresed on 10% SDS gels (10% Mini‐PROTEAN® TGX™ Precast Protein Gels) for 40 min at 20 mA. Staining was done using Coomassie Brilliant Blue R250 (2.5 g/l)  for 3 h and destained with destaining solution  overnight.2. Trypsin digestion was performed by adding 50 μl of sequencing grade trypsin (5 ng/μl in 50 mM ammonium bicarbonate) and incubated at room temperature overnight while shaking. The resulting tryptic peptide samples were desalted and concentrated using a μColumn (made with LichroprepC18) and subjected to nanoLC‐MS/MS using a Proxeon Easy nanoLC and an LTQ‐Orbitrap XL instrument  and 20 mM acrylamide (pH 8). Each lane was cut into three even slices, which were further cut into small pieces of approximately 1–2 mmet al., et al., et al., et al., et al., A. soehngenii. Visualization was done using R packages, ggplot2 and ggpubr   and additional results filtering  were performed as described previously . Reads were merged using PEAR v0.9.6  . Tab delimited files were imported into R for further processing and visualization  were downloaded for randomly chosen 100 participants from the Human Microbiome Project Data Portal     sucrose and sorbitol compared to d,l‐lactate during sampling of biomass for proteome extraction  when A. soehngenii was grown in presence of sorbitol compared to sucrose. Among these, seven were overexpressed and 16 were less expressed when cells were grown on sorbitol compared to sucrose  for sorbitol are located between the gutM gene coding for the glucitol/sorbitol operon activator and licR encoding the lichenan operon transcriptional antiterminator  abundance of these proteins during growth on sorbitol when compared to that on sucrose suggests that this gene cluster is coordinately expressed and highly induced in the presence of sorbitol. The promoter region is located before the gene EHLA_1883 that encodes sorbitol‐6‐phosphate dehydrogenase, while the terminator is located immediately after the transcriptional antiterminator protein EHLA_1878.In total, we identified 23 proteins that were differentially expressed  in sorbitol compared to sucrose. Also the protein encoded by EHLA_0193 was observed at lower abundance , albeit not significantly, but we observed that it was >5 log2 fold higher in abundance in cells grown on sucrose compared to cells grown either on d,l‐lactate or sorbitol  did not have a homologue in the genome of A. hallii. Based on our findings, we hypothesize that genes with the locus tags EHLA_0192, EHLA_0193, EHLA_0194 and EHLA_0195 are likely responsible for the ability of A. soehngenii to utilize sucrose. These genes are not (completely) present in A. hallii, which cannot grow on sucrose ) had a ten‐fold higher abundance in cells grown on sucrose Table . We idenol Table . UpstreaBifidobacterium and Lactobacillus are known lactate producers. Lactate is one of the major cross‐feeding metabolites that are converted to butyrate by butyrate producing bacteria such as A. soehngenii . A total of 15 proteins were shared between the two comparisons  that were induced by growth on d,l‐lactate. Proteins with significantly higher abundance during growth on d,l‐lactate included lactate permease, lactate dehydrogenase, electron transfer flavoprotein beta subunit, electron transfer flavoprotein alpha subunit, short‐chain acyl‐CoA dehydrogenase and lactate racemase (encoded by genes with locus tags EHLA_0973 ‐ EHLA_0978)  complex allowing growth on lactate, which is a low energy substrate , homologous to that coding for butyryl–CoA dehydrogenase, was identified to be located downstream of the gene encoding lactate dehydrogenase, and corresponding gene products were found produced together with other lactate‐specific proteins. The organization of the new d,l‐lactate utilization gene cluster, lctABCDEFG is depicted in Fig. Several intestinal bacteria such as those belonging to the genera 78) Fig. . RemarkaAnaerobutyricum , EHLA_1081], larC1  and larC2 , predicted to be involved in nickel incorporation were detected in the proteome but encoded in a scattered way in the genome. A BLASTp search for amino acid sequences similar to the product of the gene with locus tag EHLA_0978 revealed their presence in genomes of members of genera Intestinimonas and Flavonifractor, as well as in Eubacterium barkeri, E. aggregans, Pseudoaramibacter alactolyticus, Clostridium merdae, C. jeddahense and related species. A phylogenetic analysis suggests that lactate racemase (encoded by the gene with locus tag EHLA_0978) in A. soehngenii shares similarity to the lactate racemases present in related E. barkeri, E. aggregans, Intestinimonas spp. and Flavonifractor  and Intestinimonas spp. are known for their ability to convert lactate plus acetate to butyrate  in the genomes of bacteria closely related to cum Fig. . Anaerobate Fig.  ‐colonized mice  in a SIM‐colonized mouse model testifies for their functionality in vivo. Overall, these observations provide evidence for the adaptation of A. soehngenii, A. hallii and Anaerostipes caccae to utilize an important but low‐energy cross‐feeding metabolite, d,l‐lactate.A BLASTp search for amino acid sequences of lactate dehydrogenase (encoded by the gene with locus tag EHLA_0974) revealed a widespread distribution of homologues in the IMG genome database Fig. . As withins Fig. . Mining  samples . This coA. soehngenii , LDH/Etf complex, a homologue of butyryl‐CoA dehydrogenase] that were also detected in high amounts in the expressed proteome data. In conclusion, data presented here has revealed how Anaerobutyricum soehngenii and related butyrate producers can overcome the energetic barrier to utilize d,l‐lactate to produce butyrate.The bacterial species, Table S1. Supporting information Significantly different proteins identified for each pairwise comparison.Click here for additional data file.Table S2. Identification of lctABCDEFG cluster of A. soehngenii L2‐7 in cecum metatranscriptomes from mice colonized with a simplified intestinal microbiota (SIM).Click here for additional data file.Fig. S1. Overview of the global proteomic comparison.A. Number of proteins detected in each of the biological triplicates for different carbon sources.B. Coverage of proteins detected in all the samples. Totally nine samples were processed for proteomics, for each carbon source there were biological triplicates (i.e. 3×3), and the numbers given on either side of the bar plot represent the coverage of proteins in different number of samples.C. Principal components analysis of demonstrating the variation in protein expression profiles under different growth conditions.D. Correlation heatmap depicting the replicability between biological triplicates for each growth condition.Fig. S2. Maximum likelihood phylogenetic tree of 285 lactate dehydrogenase homologues. Phylogenetic tree of 285 LDH homologs identified by searching against 55 499 isolate genomes of the IMG/ER database (as of 2 October 2018). Labels represent the IMG gene ID, IMG annotation for the gene product, taxonomic identity, strain name, an assembly and/or contig/scaffold, which contains this gene. Where several genomes for a species were present, the branch of the tree was collapsed for clarity. The numbers on branches represent the bootstrap values from 1000 replicates. See methods for details on calculation.Fig. S3. Comparison of the neighbourhood of genes involved in lactate utilization in genomes of A. soehngenii and related lactate‐utilizing bacteria.The red line highlights the gene cluster involved in lactate utilization. Neighbourhoods of genes in other genomes with the same top cluster of orthologs (COG) hit and roughly same matching length are shown below using the IMG gene neighbourhood search. Genes of the same colour (except light yellow) are from the same orthologous group (top COG hit).A. soehngenii lactate utilization gene cluster.A. Shows selected bacterial genomes that share similar gene organization as Anaerostipes caccae lactate utilization gene cluster.B. Shows selected bacterial genomes that share similar gene organization as Fig. S4. Mining of publicly available metagenomes and metatranscriptomes.lctABCDFG protein homologues in human gut metagenomes from the human microbiome project.A. Abundance of lctABCDFG gene cluster of Anaerobutyricum soehngenii in Simplified Intestinal Microbiota (SIM)‐colonized mouse model.B. Detection and prevalence of transcripts for Click here for additional data file.


Endoscopic retrograde cholangiopancreatography (ERCP)‐related tissue acquisition, including fluoroscopy‐guided forceps biopsy (F‐FB), is a common technique in diagnosing indeterminate biliary lesions. Recently, peroral cholangioscopy (POCS) and POCS‐guided forceps biopsy (POCS‐FB) has also been used for the diagnosis of indeterminate biliary lesions. However, it is uncertain which of those techniques were superior for the diagnosis of extrahepatic cholangiocarcinoma (ECC). We aimed to evaluate the diagnostic yield and safety of F‐FB for indeterminate biliary lesions compared with POCS‐FB.Patients who underwent F‐FB or POCS‐FB to evaluate indeterminate biliary lesions between October 2011 and August 2019 were enrolled retrospectively. We carried out propensity score matching to balance these clinical differences between the F‐FB group and POCS‐FB group. In the propensity score‐matched cohort, we compared the diagnostic performance of F‐FB with that of POCS‐FB based on the pathological evaluation. We also evaluate adverse events associated with F‐FB and POCS‐FB.vs 29.0%, P = 0.289).We enrolled 113 patients with biliary diseases, and 62 patients were analyzed in the propensity score‐matched cohort. Sensitivity, specificity, and accuracy of F‐FB were 82.4, 100, and 90.3%, and for POCS‐FB, those values were 83.3, 100, and 90.3%, respectively. There were no significant differences in the diagnostic performance between F‐FB and POCS‐FB. There were also no significant differences in the occurrence of adverse events between F‐FB and POCS‐FB  were 82.4, 100, and 90.3%, and for peroral cholangioscopy‐guided forceps biopsy (POCS‐FB), those values were 83.3, 100, and 90.3%, respectively. There were no significant differences in the diagnostic performance between F‐FB and POCS‐FB. Extrahepatic cholangiocarcinoma (ECC), which causes biliary stricture, is one of the poor prognostic malignant diseases with a 5‐year survival rate of 20.5% .Endoscopic retrograde cholangiopancreatography (ERCP)‐related tissue acquisition, including bile aspiration cytology, biliary brush cytology, and forceps biopsy, is a standard technique to obtain specimens from indeterminate biliary lesions for pathological examinations. The specificity of the pathological evaluation of tissues obtained from indeterminate biliary lesions by ERCP‐related technique is nearly 100%. However, the sensitivity of this technique is not sufficient, with a range of 41.6–48.1%.Recently, peroral cholangioscopy (POCS) has been widely used to diagnose indeterminate biliary lesions. POCS‐guided forceps biopsy (POCS‐FB) was also used for tissue acquisition in indeterminate biliary lesions. However, the sensitivity of POCS‐FB was also insufficient (60.1%).In this study, patients who underwent ERCP‐related tissue acquisition with POCS‐FB and/or F‐FB to diagnose extrahepatic biliary lesions between December 2011 and August 2019 at our hospital were enrolled retrospectively. Exclusion criteria were as follows: (i) patients who did not provide consent; (ii) patients aged 20 years or younger when endoscopic procedures were performed; (iii) patients who underwent ERCP tissue acquisition with bile cytology only and/or endoscopic scraper (Trefle); (iv) patients who had surgically altered anatomy except Billroth‐I; and (v) patients with intrahepatic biliary lesions, gallbladder lesions, ampullary lesions, and extra biliary lesions, such as pancreatic cancer. The F‐FB group was defined as including patients who underwent F‐FB as the first technique for tissue acquisition for indeterminate biliary lesion, and the POCS‐FB group was also defined in the same way. We evaluated the diagnostic performance of F‐FB and that of POCS‐FB for ECC based on the pathological evaluation. Furthermore, we compared adverse events in F‐FB with those in POCS‐FB. This study was performed according to the guidelines described in the Helsinki Declaration for biomedical research involving human participants. The study was approved by the institutional review board of Tottori University . Informed consent was obtained from all participants using an opt‐out approach in the retrospective study.A side‐viewing duodenoscope  was used for ERCP. We also used a 0.035‐inch hydrophilic guidewire  and/or a 0.025‐inch hydrophilic guidewire  during ERCP. If there was difficulty in inserting the cannula into the biliary tract, patients underwent a precut papillotomy with a needle knife .F‐FB was performed under X‐ray fluoroscopy. The forceps were inserted into the biliary tract, opened at the perihilar side of the stricture, and then drawn into the stricture. The forceps were pressed against the stricture if they became stuck and closed to carry out a biopsy of the tissue. FB‐45Q‐1  biopsy forceps, with a 2.6 mm‐diameter cup, were used to carry out a wire‐guided biopsy  direct visualization system. A cholangioscope was inserted into the bile duct over the guidewire, and POCS‐FB under direct vision was performed with M00546270 (Boston Scientific Corporation) with a 1.0 mm‐diameter cup . In the POCS‐FB group, EST was performed for almost all patients who had not previously undergone EST. Patients who received antithrombotic therapy underwent endoscopic papillary balloon dilation (EPBD) using a balloon dilatation catheter .We also evaluated the adverse events caused by endoscopic procedures and those severities that were referred to as the guidelines of the American Society for Gastrointestinal Endoscopy.The diagnosis of cholangiocarcinoma was based on pathological diagnosis of bile aspiration cytology, transpapillary forceps biopsy, endoscopic ultrasonography‐guided fine needle aspiration biopsy, or surgical specimen. Biopsy specimens were stained with hematoxylin and eosin, and if necessary, immunostaining, including Ki‐67 and p53, was also performed. In histological findings, malignancy or suspected malignancy, including biliary intraepithelial neoplasm‐3, was considered positive. Benign and mild atypia was considered negative . Patients with benign biliary disease had a final diagnosis based on clinical and radiological follow‐up data.vs female), location of biliary lesion , length of biliary stricture (continuous), acute cholangitis (present or absent), T‐Bil (continuous), CEA (continuous), CA19‐9 (continuous), previous history of EST (present vs absent), and prebiliary stenting before ERCP (done vs not done). Each patient in the F‐FB group was matched to a patient in the POCS‐FB group with the nearest‐neighbor method using a caliper range of 0.2 of the standard deviation of the pooled propensity scores.We carried out propensity score matching because between‐group differences in baseline characteristics in our study population could influence the diagnostic accuracy of forceps biopsy for ECC. The propensity score of undergoing F‐FB or POCS‐FB was calculated using a multivariable logistic regression model. Length of biliary stricture or tumor size and serum total bilirubin (T‐Bil) were reported as the factors affecting the accuracy or sensitivity of ERCP‐related tissue acquisition for cholangiocarcinoma or malignant biliary stricture.U‐test. All values are expressed as median with interquartile ranges. P < 0.05 was considered significant.Statistical analysis was performed using StatFlex ver. 7.0 for Windows . Categorical variables were compared using the chi‐square test or Fisher's exact test as appropriate. Continuous variables were compared by using the Mann–Whitney We enrolled 113 patients with 114 biliary lesions in this study . Participants included 75 men and 38 women aged 26–91 years . Sixty‐one patients had ECC, and 52 had benign biliary lesions. We performed F‐FB for 53 patients (F‐FB group) and POCS‐FB for 60 patients (POCS‐FB group). Table P < 0.001). The mapping biopsy, a method for defining the longitudinal extension of ECC, was also performed for many patients who underwent POCS‐FB. Meanwhile, fewer patients underwent mapping biopsy in the F‐FB group than that in the POCS‐FB . There was no significant difference between the median procedure time of F‐FB and that of POCS‐FB  and three range, 2–7) in the F‐FB group and the POCS‐FB group, respectively. In the POCS‐FB group, almost all patients with naïve papilla underwent EST. Meanwhile, only nine patients received EST in the F‐FB group. Therefore, patients who have a previous history of EST or underwent EST in the F‐FB group were significantly fewer than in the POCS‐FB group , with 9 patients developing acute pancreatitis (29.0%), 3 patients developing infection , and 1 developing pulmonary disorder (3.2%). Adverse events following POCS‐FB occurred in nine patients (29.0%), with six patients developing acute pancreatitis (19.4%), including one of severe pancreatitis; two developing infections ; and one patient who underwent a precut papillotomy developing bleeding (3.2%). There was no significant difference in the occurrence of adverse events between the F‐FB group and the POCS‐FB group. No perforations and no procedure‐related mortality were observed.To obtain the specimens from the biliary lesions, ERCP‐related tissue acquisition, including bile aspiration cytology, biliary brush cytology, and forceps biopsy under X‐ray fluoroscopy, is a commonly used method. Every tissue acquisition method is almost 100% specific.et al. reported that less tissue was obtained from POCS‐FB than F‐FB.Recently, POCS has been widely used in diagnosing indeterminate biliary lesions. We can not only observe the optical viewing of the biliary systems but also perform targeted biopsies on indeterminate biliary lesions under direct vision with POCS. In previous studies, the use of POCS improved the diagnostic accuracy of indeterminate biliary lesions.In our study, almost all patients (93.5%) had a previous history of EST or underwent EST in POCS‐FB, and this rate was significantly higher than that in F‐FB. For biliary access, EST are is when the cholangioscope is inserted into the bile duct, but as a result, the sphincter of Oddi function is lost entirely. Although whether EST is or not needed to insert a biopsy forceps into the bile duct for the F‐FB is controversial, avoiding EST may help prevent early adverse events, including bleeding, and late adverse events, including liver abscess.Longitudinal extension is a feature of cholangiocarcinoma, and this was used in 14.6% of patients with cholangiocarcinoma.This study has some limitations. First, this was a single‐center study with a small number of cases. Second, patients who were diagnosed by a clinical follow‐up were also included in this study. A prospective randomized study including a larger number of patients with long‐term follow‐up is required.In conclusion, the diagnostic performance of F‐FB for ECC is similar to that of POCS‐FB. At least for the initial diagnostic technique based on pathological evaluation for indeterminate biliary lesions, POCS‐FB is not necessary.


Patients with dementia have an increased 30‐day mortality after hip fracture. We investigated clinical management including time to surgery, out‐of‐hours admission and surgery, surgery on weekends, surgery volume per ward, and anesthesia technique for this excess mortality risk.This register‐ and population‐based study comprised 12,309 older adults (age 70+) admitted to hospital for a first‐time hip fracture in 2013–2014, of whom 11,318 underwent hip fracture surgery. Cox proportional hazards regression models were applied for the analysis.The overall postoperative 30‐day mortality was 11.4%. Patients with dementia had a 1.5 times increased mortality risk than those without (HR = 1.50 [95% CI 1.31–1.72]). We observed no time‐to‐surgery difference by patient dementia status; additionally, the excess mortality risk in patients with dementia could not be explained by the clinical management factors we examined.Increased mortality in patients with dementia could not be explained by the measured preoperative clinical management. Suboptimal handling of postoperative complication and rehabilitation are to be investigated for their role in the witnessed increased mortality for patients with dementia. In this register‐ and population‐based cohort study of 12,309 older adults (age 70+) with hospital admission for a first‐time hip fracture in 2013–2014, we found an increased 30‐day postoperative mortality in patients with dementia. However, this excess mortality was not explained by the clinical factors examined . Additionally, we found no difference in time to surgery by the patient's dementia status. Patients with dementia were at a higher risk of dying within 30 days after hip fracture surgery.This excess postoperative 30‐day mortality in patients with dementia could not be explained by the measured preoperative clinical management.1Hip fractures are common among the elderly, with substantial consequences of decreased mobility and increased morbidity and mortality  is a risk factor for postoperative mortality  number assigned to each individual at birth and to persons who hold a Danish residence permit upon immigration  who had a first‐time hip fracture admission to a hospital in Denmark between January 1, 2013, and December 31, 2014 (2013–2014).We excluded individuals from the study if they met any of the following criteria: (a) history of hip fracture in 2008–2012, (b) died before the index date of hip fracture admission, and (c) emigration on the index date. In addition, we also excluded individuals who immigrated to Denmark later than December 31, 2007, due to potentially incomplete information on hip fracture, chronic diseases, and medication in the registers for the period of 2008 to 2012  and the date of the hip fracture surgery  for mortality assessment.Records of hip fracture and hip fracture surgery were extracted from the Danish National Patient Register (DNPR)  codes were used to define hip fractures as fracture of femoral head or neck (S720), pertrochanteric fracture (S721), or subtrochanteric fracture (S722) in 2013–2014 from the DNPR. Additionally, ICD‐10 code M809B for osteoporotic hip fracture was used together with hip fracture codes (S72*) for inclusion. Using the same hip fracture diagnosis codes, an individual's history of hip fracture records in 2008–2012 was also traced in the register for exclusion purpose. Both primary and secondary diagnoses were included with any type of inpatient, outpatient, or emergency department registration from Danish hospitals. The Nordic Medico‐Statistical Committee codes for arthroplasty (KNFB*) and osteosynthesis (KNFJ*) were used to extract potential hip fracture surgery performed on eligible study participants.2.22.2.1We defined 30‐day postoperative mortality as death within the first 30 days after hip fracture surgery . We obtained information on death  including the date of death from the CPR Pedersen, , which r2.2.2A person was identified with dementia (any type) if they fulfilled one of the following criteria: (1) a primary or secondary dementia diagnosis  in the DNPR or in the Danish Psychiatric Central Research Register (PCRR)  codes were used to identify dementia‐related medications prescribed in Denmark   comorbid diseases, in any combination, based on the number of diseases present on the index date of hospital admission for hip fracture, as well as on the date of the hip fracture surgery.2.2.4A person was defined as taking sedative medication if they had taken any medication of four types registered in the LMDB: sleeping medications (ATC code: N05C), antipsychotics (ATC code: N05A), antianxiety medications (ATC code: N05B), and antidepressants (ATC code: N06A), within 100 days prior to the index date for hip fracture admission . We grouped usage of these medications into three categories: 0, 1, 2, or 3 and more (3+), in any combination.2.2.5We defined TTS as the time from the hospital admission for hip fracture to the time hip fracture surgery was performed and measured in hours. There were a few cases with a zero‐time registration between admission and surgery. If the two events occurred on the same day, then we set the TTS equal to 0.5 days, that is, 12 hrs.We defined out of hours as any admission and surgery in the time period of 16:01–07:59.Any surgery performed on Saturdays, Sundays, and on Danish public holidays in 2013–2014 was defined as surgery on weekends.Anesthesia technique used for hip fracture surgery was divided into two groups: general anesthesia (NAAC*), and regional anesthesia (NAAD*) includes both spinal (NAAD1*) and epidural anesthesia (NAAD0*). Since very few patients were operated on with local anesthetic and/or conscious sedation (NAAB*), we categorized them with the regional anesthesia group.Surgery volume per ward  was measured by the total number of registered hip fracture surgeries performed (only arthroplasty (KNFB*) and osteosynthesis (KNFJ*) surgeries were included) in 2013–2014. We only counted one procedure in cases where several procedures were recorded for the same patient in the study period.Based on the distribution [median 233 (25th‐75th percentile 203–699)] per unit or ward, we grouped the volume of surgeries performed into three groups: (1) low volume (fewer than 200 surgeries), (2) medium volume (200 to 700 surgeries), and high volume (more than 700 surgeries).2.2.6The study participants' age, sex, marital status, immigration, and emigration status were all extracted from the CPR Pedersen, . The parThe patients were divided into three age groups: 70–79, 80–90, or 90 years and older (90+). Marital status was grouped into four categories: married (including partnership or cohabitation), divorced (including dissolved partnership), widowed (including partner died), or single (no cohabitation). Education level was grouped according to the individual's highest education level in years: low education (<10 years), medium education (10–12 years), or higher education (>12 years).We also included American Society of Anesthesiologists Physical Status Classification System codes (ASA class) scale as a covariate Daabiss, . Given t2.3We described the age group, sex, education, marital status, presence of comorbid diseases, and use of sedative medication by the index date of hospital admission for hip fracture in patients with and without dementia. We also described the characteristics of hip fracture surgeries in 2013–2014, including clinical management for hip fracture surgery by dementia status.The 30‐day postsurgery mortality was time to event. Patients were censored at the end of the study or at their date of emigration. However, for the TTS, patients were also censored if they died before the hip fracture surgery. We applied Cox proportional hazards regression (Cox regression) to estimate association between dementia and the study outcomes.For the association between dementia and TTS, the multivariable Cox regression was adjusted for age group, sex, education, marital status, comorbid diseases, sedative medications, and out‐of‐hours hospital admissions for hip fracture.For the association between dementia and 30‐day postsurgery mortality, we applied three Cox regression models. Model A was univariable analysis of dementia for 30‐day mortality after hip fracture surgery. Model B was Model A adjusted for confounders . Model C was Model B adjusted for clinical management factors including out‐of‐hours admissions, out‐of‐hours surgery, surgery on weekends, anesthesia technique, and surgery volume per ward. Additionally, ASA class scores, type of surgery, and type of hip fracture were all adjusted for in Model C.The risk estimations are presented as hazard ratios (HRs) with corresponding 95% confidence intervals (95% CIs). All statistical analysis was performed using STATA version 14.0 (StataCorp LP).2.4Complying with European data protection rules, the Danish Data Protection Agency registered the project . According to Danish law, review by an ethics board or patient consent is not required for purely register‐based studies.33.1A total of 12,309 first‐time hip fracture hospital admissions were identified in the period 2013–2014 among older people (age 70+) in Denmark, and among them, 1,853 (15.1%) patients had a diagnosis of dementia. There were a higher proportion of older, female, and divorced or widowed patients, and more patients with comorbid chronic diseases and taking sedative medications in the dementia group than in the nondementia group  TTS of 22 hr (15–31). Patients with dementia experienced a median TTS of 21 hr (15–31), whereas patients without dementia have a median TTS of 23 hr (15–35).However, multivariable analysis adjusted for age group, sex, marital status, education, comorbid diseases, sedative medications, and out‐of‐hours hospital admissions for hip fracture showed no significant TTS difference (HR = 1.04 [95% CI = 0.99–1.10]) between patients by dementia status. Additionally, age, sex, marital status, education, comorbid diseases, and sedative medication use showed no effect on TTS Table .3.3Among the 11,318 patients who underwent a hip fracture surgery, half (51.7%) had a fracture of the femoral head or neck; nearly two‐thirds of the total study population received osteosynthesis surgery; and more than one‐thirds (37.8%) of the total surgeries were performed out of normal business hours, and 29.4% on weekends (Table p < .05).There was no significant difference in clinical management of hip fracture surgery, including out‐of‐hours admission, surgery on weekends, and surgery volume per ward, between patients with and without dementia. However, patients with dementia were more likely to undergo an operation out of hours and receive general anesthesia than those without dementia  died within 30 days after the surgery, including 284 patients with dementia. The computed Kaplan–Meier estimate of postoperative 30‐day mortality showed a significant higher mortality in patients with dementia  Table .Further adjusting for clinical management factors for hip fracture surgery, including out‐of‐hours admission, out‐of‐hours surgery, surgery on weekends, anesthesia technique, and surgery volume per ward, did not change the estimate for excess mortality for dementia patients (HR = 1.50 [95% CI 1.31–1.72]). However, patients who received regional anesthesia showed an 18% reduction of 30‐day postsurgery mortality risk compared with the patients who received general anesthesia.Increased age, male sex, comorbid diseases, sedative medications, and higher ASA scores were significantly associated with 30‐day postsurgery mortality.A sensitivity analysis the using Charlson Comorbidity Index  admitted to hospital for a first‐time hip fracture in 2013–2014 who underwent hip fracture surgery, patients with dementia had a 1.5 times (HR = 1.50 [95% CI 1.31–1.72]) higher postoperative 30‐day mortality compared with patients without dementia. However, we observed no TTS difference by patient dementia status; additionally, the excess mortality risk in patients with dementia could not be explained by the clinical management factors we examined.4.1To the best of our knowledge, our study is the first population‐based study to investigate dementia for postoperative 30‐day mortality in Denmark by taking into account the clinical management factors investigated in this study. Other studies, although in line with our postoperative mortality risk estimations in patients with dementia, lack clinical management factors for comparison  wrote the manuscript. Frans Boch Waldorff initiated the project concept. Sonja Wehberg (SW), Volkert Dirk Siersma (VDS), Frans Boch Waldorff (FBW), and JDP discussed and finalized the statistical analysis methods. Sonja Wehberg conducted the statistical analysis. Connie Thurøe Nielsen (CTN) provided her academic knowledge regarding psychiatric and geriatrics care. Bjarke Viberg (BV) provided his expert knowledge regarding hip fracture surgery. All the authors interpreted the study results, commented, and contributed to the manuscript. All authors approved the manuscript for publishing to this journal.https://publons.com/publon/10.1002/brb3.1823.The peer review history for this article is available at


Breath testing (BT) is used to identify carbohydrate malabsorption and small intestine bacterial overgrowth. Measuring methane alongside hydrogen is advocated to reduce false‐negative studies, but the variability of methane production is unknown. The aim of this study is to examine the effect of high methane production on hydrogen excretion after ingesting lactulose, fructose, or lactose.A retrospective audit was performed of patients with gastrointestinal symptoms who underwent BT. Following a low fermentable carbohydrate diet for 24‐h, a fasting BT before consuming 35 ml lactulose, 35 g fructose, or lactose in 200 ml water, followed by BT every 10–15 min for up to 3‐h, was performed. A positive test was defined as a ≥20 ppm rise of hydrogen or methane from baseline. A high methane producer had an initial reading of ≥5 ppm. Breath hydrogen and methane production were measured as area under the curve. Chi‐squared tests were used to compare proportions of those meeting the cut‐off criteria.vs 2375 [1810–3195] ppm min [P = 0.11]). Symptoms and breath test results were not positively related.Of patients, 26% (28/106) were high methane producers at their initial lactulose test. The test–retest repeatability of methane production was high, with the same methane production status before ingesting lactose in all (70/70) and before ingesting fructose in most (71/73). Methane production was highly variable during testing, with 38% (10/26) having ≥1 reading lower than baseline. Hydrogen produced by high or low methane producers did not differ (1528 [960–3645] ppm min The validity of including an increase of ≥20 ppm methane to identify carbohydrate malabsorption or small intestine bacterial overgrowth should be questioned due to the variability of readings during testing. Breath testing is used to identify carbohydrate malabsorption and small intestine bacterial overgrowth. Measuring methane alongside hydrogen is advocated to reduce false‐negative studies, but the variability of methane production is unknown. The validity of including an increase of ≥20 ppm methane to identify carbohydrate malabsorption or small intestine bacterial overgrowth should be questioned due to the variability of readings during testing. Irritable bowel syndrome (IBS) is a common gastrointestinal condition that affects ~11% of the population.Hydrogen breath testing after the administration of a carbohydrate substrate such as fructose, lactose, or lactulose is used to identify carbohydrate malabsorption.A lack of standardized protocols has hampered the use of breath testing for the identification of SIBO and carbohydrate malabsorption and, subsequently, for directing treatment.It has been suggested that breath tests can be used to direct therapy, including dietary therapy, and the implementation of a low fermentable oligosaccharide, disaccharide, monosaccharide, and polyol diet.In this paper, results of breath testing using lactulose, fructose, and lactose as the substrates in a clinical, real‐world population will be examined, with particular attention given to the role of methane and whether including a rise of ≥20 ppm methane from the initial reading is valuable in identifying SIBO and carbohydrate malabsorption.This study was a retrospective clinical audit of patients who were referred for breath testing from July 2014 to June 2017 due to the presence of gastrointestinal symptoms compatible with IBS. Patients were referred for the identification of either SIBO by lactulose or malabsorption of fructose or lactose. The study was approved by the University of Otago Human Ethics Committee  (HE15/008).Clinical data were extracted from paper records located at Gastroenterology Otago Ltd. and the electronic record at the Southern District Health Board.Breath testing was performed on three separate days, with at least 1 day in between for each of lactulose, lactose, and fructose. Restrictions before the test were as follows: avoid antibiotics and probiotics for 2 weeks, follow a diet low in fermentable carbohydrates for 24 h before the test, fast overnight, and avoid smoking and using perfume for 10 h before testing. Test technicians confirmed compliance with the pretest diet prior to test commencement.4ECK™ . After an overnight fast, patients consumed 35 ml of lactulose, or 35 g fructose, or 35 g lactose dissolved in 200 ml water. Before ingesting the test substrate, all patients provided an initial breath sample and a sample every 10 min thereafter for 180 min for lactulose or every 15 min for 180 min for fructose and lactose or until they were positively identified with SIBO or a carbohydrate malabsorption. The cut‐off value for identifying SIBO was an increase of ≥20 ppm from the initial reading within 90 min of the administration of lactulose for either hydrogen or methane or both. The cut‐off value for identifying fructose and lactose malabsorption was ≥20 ppm from the initial reading. A high methane producer was defined as someone with an initial reading of ≥5 ppm of methane, and a low methane producer had an initial reading of <4 ppm of methane.All breath samples were analyzed for hydrogen and methane levels by gas chromatography on a Bedfont GastroCHPatients recorded their own breath test measurements and noted any symptoms they experienced during the test using a standardized form whereby symptoms at each breath sample could be recorded if they occurred. Breath hydrogen and methane production were quantitatively assessed by measuring the area under the curve (AUC) for the duration of the test. To test for variation within methane breath tests, values were normalized to zero, and the number of fluctuations below the initial reading was compared to the rise above the initial reading.At their first breath test, patients completed a gastrointestinal symptom questionnaire rating the severity of 20 GI symptoms that they had experienced within the week before testing (1–7 points with increasing severity). These symptoms included pain, bloating, diarrhea, constipation, early satiety, and nausea.U‐tests were used.Data were analyzed using STATA IC13 , with graphs drawn in GraphPad Prism version 7.03 . Descriptive data are reported as mean and standard deviation or median and 95% confidence interval as indicated. Chi‐squared tests were used to compare proportions of those identified with SIBO or a carbohydrate malabsorption. Student's t‐tests were used to compare characteristics if the data were normally distributed; otherwise, Mann–Whitney There were 106 patients who underwent lactulose breath testing, of whom 70 underwent a lactose breath test, and 73 underwent a fructose breath test. Patients were 42.5 ± 16.2 years old, and 75% (79/106) of patients were female. All patients had IBS or IBS‐like symptoms. Gastrointestinal comorbidities experienced by patients were gastroesophageal reflux (four), coeliac disease (four), Crohn's disease (five), and hemorrhoids (four). Two patients had had a colectomy. Other medical comorbidities included asthma (six), arthritis (four), eczema (three), depression (two) anxiety or stress (five), allergic rhinitis (three), and previous eating disorder (three).versus 79% (48/61) (P < 0.05).A total of 71% (75/106) of patients were identified to have SIBO by either hydrogen or methane on lactulose breath test. Symptoms were reported in 42% (45/106) of patients during the lactulose breath test (Table P = 0.53) or methane during lactose testing (P = 0.85).There were 16% (11/70) of patients with lactose malabsorption identified by a rise in breath hydrogen, with an additional three people identified through elevated methane levels (Table versus 56% (14/25) (P < 0.05).There were 30% (22/73) of patients who were positive for fructose malabsorption by raised hydrogen levels and five by elevated methane levels Table . During versus low (41.7 ± 16.5) methane producers (P = 0.39) or the proportion who were female  were high methane producers on their initial lactulose test, and 21% (15/70) and 21% (15/73) produced high methane readings on their subsequent baseline lactose and fructose tests, respectively. The test–retest repeatability of methane production status was high, with the same methane production status at the baseline test before ingesting lactose in all patients (70/70) and before ingesting fructose in most (71/73). There was no difference in mean age of high (45.1 ± 16.5) P < 0.01)    , including patients who had a concurrent rise in hydrogen, had at least one methane reading lower than the initial reading  (P < 0.05) (Table P = 0.10). There was no difference in the total amount of hydrogen produced, as measured by the AUC, by level of methane production (Table There were 61% 17/28) high methane producers who had a concurrent rise in hydrogen of ≥20 ppm from their initial reading. Of high methane producers, 18% (5/28) had a significant rise in hydrogen but did not have a significant rise in methane. Compared to patients who were low methane producers, there was a lower proportion of high methane producers with a ≥ 20 ppm rise in breath hydrogen levels: 61% (17/28) 8 high mesus 82% 6/78 (P < P = 0.33).A total of 74 patients had fructose breath testing, with 21% 15/74) being high methane producers  of methane producers had a rise of ≥20 ppm of methane, and 7% (1/15) had a significant rise in hydrogen following fructose ingestion. Methane levels during breath testing fluctuated, with 47% (7/15) patients having at least one reading below their initial reading.P = 0.10). Nearly three‐quarters of the high methane producers  had at least one methane level below their initial reading. There were only 2 of 15 high methane producers who had a rise of ≥20 ppm above their initial hydrogen level.Seventy patients had lactose breath testing, with 21% (15/70) being high methane producers on their initial test. Methane production appeared to suppress hydrogen production, with all high methane producers having <2000 ppm min of hydrogen in 90 min. However, there was no statistically significant difference in the total amount of hydrogen excreted between high  and low methane producers  . Moreover, breath methane excretion was shown not to be an accurate marker of methane production compared to methane measured in rectal samples.Previous research has called into question other aspects of breath testing as the coadministration of lactulose with radio‐opaque markers showed that a rise in breath hydrogen usually occurred when the markers arrived at the ileocecal junction,The variability in methane levels during breath testing needs to be investigated in a larger cohort to substantiate results in this study, especially as it is known that breath methane levels fluctuate independent of dietary intake.In conclusion, this audit suggests that identifying either carbohydrate malabsorption or SIBO by an increase of ≥20 ppm in methane producers needs to be questioned due to the variability in readings throughout testing. However, using a cut‐off value of ≥5 ppm of methane on a single time point breath test seems to identify methane producers.


Fusobacterium nucleatum with tumorigenesis. Our previous study demonstrated that F. nucleatum infection can induce epithelial‐mesenchymal transition (EMT) in oral epithelial cells and elaborated a probable signal pathway involved in the induction of EMT. However, the comprehensive profiling and pathways of other candidate genes involved in F. nucleatum promoting malignant transformation remain largely elusive. Here, we analysed the transcriptome profile of HIOECs exposed to F. nucleatum infection. Totally, 3307 mRNAs (ǀLog2FCǀ >1.5) and 522 lncRNAs (ǀLog2FCǀ >1) were identified to be differentially expressed in F. nucleatum‐infected HIOECs compared with non‐infected HIOECs. GO and KEGG pathway analyses were performed to investigate the potential functions of the dysregulated genes. Tumour‐associated genes were integrated, and top 10 hub genes  were identified by protein‐protein interaction (PPI) network, and Oncomine was used to validate hub genes' expression. LncRNA‐hub genes co‐expression network comprising 67 dysregulated lncRNAs were generated. Together, our study revealed the alteration of lncRNA and potential hub genes in oral epithelial cells in response to F. nucleatum infection, which may provide new insights into the shift of normal to malignant transformation initiated by oral bacterial infection.Accumulating evidence links  In this study, we aimed to comprehensively analyse the differentially expressed genes (DEGs) including lncRNAs and mRNAs from previous high‐throughput sequencing data for further investigation. To the best of our knowledge, this work is the first to evaluate the signatures of lncRNAs and tumour‐associated genes (TAGs) in the context of F. nucleatum contributing to EMT in oral epithelial cells, which may shed new light on the mechanism of F. nucleatum contributing to malignant transformation.Long non‐coding RNAs (lncRNAs) are classically defined as RNA transcripts larger than 200 nucleotides but lack of protein coding capacity.22.1F. nucleatum ATCC25586 was routinely cultured on tryptic soy broth agar plates containing 5% defibrinated sheep blood, 5 µg/mL hemin and 1 µg/mL menadione under anaerobic condition. The cellular model of HIOECs infected with F. nucleatum at a MOI of 100:1 was established as described previously.HIOECs were provided by Prof. Wantao Chen from Key Laboratory of Shanghai Oral Medicine, Shanghai Jiao Tong University, and grown in defined keratinocyte‐SFM (Gibco) with growth supplement at 37°C and 5% CO2. 2.2F. nucleatum at MOI of 100:1, and collected the total RNA from F. nucleatum‐infected HIOECs and the non‐infected HIOECs, and each had 3 repetitions, and thus, 6 RNA samples were submitted to analyse the global lncRNA and mRNA profiling at Genery Biotech Ltd. by high‐throughput sequencing on an Illumina Hiseq3000 platform (Illumina). Briefly, the raw data were filtered to remove low‐quality reads, and the resulting high‐quality data were aligned to the human reference genome . Four online analytic tools, including CNCI (https://github.com/www‐bioinfo‐org/CNCI), CPAT (http://lilab.research.bcm.edu/cpat/index.php), CPC (http://cpc.cbi.pku.edu.cn/) and PLEK (https://sourceforge.net/projects/plek/), were used to predict the coding potential. Transcripts without coding potential were the candidate set of lncRNAs. DESeq2 software was used to obtain gene level fragments per kilo‐base of exon per million (FPKM) reads as lncRNA and mRNA expression profiles. Fold change and P value were calculated based on the FPKM data. LncRNAs and mRNAs with a significant fold change of ≧2 and P value < .05 were deemed as differentially expressed genes (DEGs). Furthermore, the P value was adjusted by multiple testing using the Benjamini‐Hochberg false discovery rate (FDR) procedure, and a standard threshold of 5% was selected for declaring significance.We established the cellular model of HIOECs infected with F. nucleatum‐infected and the control HIOECs. Target genes in cis and trans‐acting of DElncRNAs were predicted for further functional analyses. The cis role of lncRNAs indicated their actions on neighbouring target genes and the coding genes close to 100k upstream and downstream regions of lncRNAs were considered as the cis role target genes. On the contrary, the trans target genes of lncRNAs are located anywhere outside that range and identified by expression levels, according to Pearson's correlation coefficient (PCC ≧0.95).Volcano plot filtering was performed to generate an overview of the differentially expressed (DE) lncRNAs and mRNAs with statistical significance between 2.3https://david.ncifcrf.gov/). Bubble plot package of R software was used to visualize the results of the GO and KEGG analyses. P value < .05 was considered to indicate significant GO terms and pathways. Furthermore, several DEGs relevant to cell migration were summarized according to the functional analyses.To gain insight into the functions and potential roles of the acquired DEmRNAs and DElncRNAs, Gene Oncology (GO) analyses including biological process (BP), cellular component (CC), molecular function (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the Database for Annotation, Visualization and Integrated Discovery  and TSGene database (https://bioinfo.uth.edu/TSGene/index.html), respectively. Then, the online Venn tool (https://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to identify the overlapping genes among the DEmRNAs (ǀlog2FCǀ ≧1.5) and all the TAGs candidates. The overlapping genes were then subjected to hierarchical clustering by Multi Experiment Viewer  (JCVI) and functional enrichment analyses.Tumour‐associated genes (TAGs) including oncogenes and tumour suppressor genes were obtained from the ONGene database (2.5http://string‐db.org) was applied to evaluate known and predicted protein‐protein interactions. The TAGs identified from the previous step were mapped to STRING database to construct the PPI network with a high confidence of 0.70. Cytoscape software was used to visualize the network, and CytoHubba plugin was utilized to screen out the top 10 hub genes with the highest degree of connectivity in the network.P < .05 was considered to have statistical significance.The Search Tool for the Retrieval of Interacting Genes and Proteins . Pearson's correlation coefficient (PCC) between DElncRNAs and the identified hub genes was calculated, and those pairs with PCC >0.990 were selected. The co‐expression network of DElncRNAs‐hub genes interactions was visualized using Cytoscape software.Interaction networks of DElncRNAs‐hub genes were constructed to explore the potential regulatory function of lncRNAs with aberrant expression (ǀlog2FCǀ ≧1 and 2.7https://www.oncomine.org/), which provided genome‐wide expression analyses in various types of tumours and normal tissues. The hub genes were submitted to the Oncomine database, and seven data sets of OSCC were selected to explore their expressions in clinical samples. A P value < .05 and a fold change ≧2 were considered as the cut‐off with gene ranking in the top 10%. For each hub gene, we compared the results of cancerous with those of normal tissues. Additionally, the expressions of some lncRNAs identified in the co‐expression network were obtained from the GEPIA database (http://gepia.cancer‐pku.cn/), which integrated the data from The Cancer Genome Atlas (TCGA) and the Genotype‐Tissue Expression project, containing sequencing data of 9736 tumours and 8587 normal samples. The data set of HNSC was selected for analysis, and the cut‐off was set as ǀLog2FCǀ >1, P value < .05 and Jitter size 0.4.The expression analysis of the identified hub genes such as FOS, CREB1, JAK2, was conducted using Oncomine database , of which 1261 genes were up‐regulated and 2046 genes were down‐regulated. Totally, 522 known lncRNAs with significant differential expression were identified , including 282 up‐regulated lncRNAs and 240 down‐regulated lncRNAs. The top 20 DEmRNAs and DElncRNAs were listed in Table Volcano plots were used to illustrate the general scattering of the differentially expressed lncRNAs and mRNAs between s Figure . A total3.2F. nucleatum, GO and KEGG analysis were performed. For DEmRNAs, the DEGs were significantly enriched in biological process (BP) of DNA‐templated transcription  Other lncRNAs including C17orf82, LINC00511, LINC00605, LINC00702, and MNX1‐AS1 were also detected in HNSC tissues, but the box plots showed no statistic difference between the HNSC and normal tissues. However, they were aberrantly expressed in other types of cancer, such as colon adenocarcinoma (COAD), oesophageal carcinoma (ESCA), pancreatic adenocarcinoma (PAAD) and rectum adenoma (READ), which have been reported to be associated with F. nucleatum infection.To determine whether the hub genes identified in HIOECs infected with 4Fusobacterium nucleatum is a well‐recognized commensal bacterium commonly localized in the human oral cavity, but also can be found in individuals with extraoral infections, including gastrointestinal and cardiovascular diseases, rheumatoid arthritis, respiratory tract infections, and pancreatic cancer.F. nucleatum was highly elevated in a subset of colorectal adenoma and carcinoma tissues.F. nucleatum gradually increased from premalignant adenomatous lesions to carcinomas in the colorectal carcinogenesis pathway, suggesting F. nucleatum could be used as a novel risk factor for disease progression from adenoma to cancer.Fusobacterium was in higher abundance in oral/head and neck cancer.Fusobacterium in tumour lesions increased by 6% , and a 2.93 higher chance  of Fusobacterium being present in tumour lesions,Fusobacterium infection might contribute to oral/head and neck cancer. In recent years, the researchers have focused on the associations between F. nucleatum status and clinicopathological and molecular features, as summarized in Table F. nucleatum has been detected to be enriched in OSCC, the clinical characteristics associated with F. nucleatum abundance were still in deficiency. Among the publications available online, only one study conducted by Yang et al analysed the correlation between the clinical features and Fusobacteria, concluding that increased colonization by Fusobacteria was positively associated with OSCC stage.F. nucleatum status in colorectal cancers and oesophageal squamous cell carcinoma (ESCC), suggesting that higher F. nucleatum burden was not significantly associated with the tumour location and differentiation, but was associated with advanced tumour stage and poor survival. Also, F. nucleatum burden was observed to be associated with molecular features such as KRAS, MSI, MLH1 and CIMP status.F. nucleatum. In addition, the sample size and demographic characteristics of the participants may also give rise to the heterogeneity. These studies provide more evidence that F. nucleatum is associated with tumorigenesis and could be a potential risk factor for OSCC and extraoral cancers, especially CRC. 95% CI, ‐9, and aF. nucleatum are chronic infection, interaction of cell surface molecules of these bacteria with immune system and stromal cells, immune evasion and immune suppression.F. nucleatum infection in cancers confirmed in previous studies were summarized in Table F. nucleatum infection in the malignant transformation. In this study, we mined the high‐throughput sequencing data obtained from a cellular model of oral epithelial cells infected by F. nucleatum at a MOI of 100.The most important carcinogenesis mechanisms of F. nucleatum‐infected host cells has seldomly been reported ever. ATM (ataxia telangiectasia mutated) has been reported to trigger a cascade of signalling reactions mediating phosphorylation of Akt, GSK3ß and Snail to promote EMT in breast cancer cells.Special attention was paid to the tumour‐associated genes including oncogenes and tumour suppressor genes. Among the genes shown in Figure F. nucleatum in our present study. Given that a number of the differentially expressed lncRNAs were unknown or rarely studied, we therefore mainly focused on the known lncRNAs. For the lncRNAs in the co‐expression network that might regulate the expression of the hub genes ; Formal analysis (lead); Software (lead); Writing‐original draft (lead). Chen Li: Conceptualization (supporting); Writing‐review & editing (supporting). Zhiying Zhang: Formal analysis (supporting). Yuchao Li: Formal analysis (supporting). Qian Li: Formal analysis (supporting). Fengxue Geng: Data curation (supporting); Formal analysis (supporting). Junchao Liu: Data curation (supporting). Yaping Pan: Conceptualization (supporting); Formal analysis (supporting); Funding acquisition (lead); Writing‐review & editing (supporting).Fig S1Click here for additional data file.Table S1‐S2Click here for additional data file.