Wickerhamomyces anomalus  is a kind of non-Saccharomyces yeast that has a variety of unique physiological characteristics and metabolic features and is widely used in many fields, such as food preservation, biomass energy, and aquaculture feed protein production. However, the mechanism of W. anomalus response to ethanol stress is still unclear, which greatly limits its application in the production of ethanol beverages and ethanol fuels. Therefore, we checked the effects of ethanol stress on the morphology, the growth, and differentially expressed genes (DEGs) and metabolites (DEMs) of W. anomalus.W. anomalus. Energy metabolism, amino acid metabolism, fatty acids metabolism, and nucleic acid metabolism were significantly influenced when exposing to 9% ethanol and 12% ethanolstress, which maybe universal for W. anomalus to response to different concentrations of ethanol stressl Furthermore, extracellular addition of aspartate, glutamate, and arginine significantly abated ethanol damage and improved the survival rate of W. anomalus.High concentrations of ethanol (9% ethanol and 12% ethanol) remarkably inhibited the growth of W. anomalus response to ethanol stress. Therefore, new strategies can be realized to improve the ethanol tolerance of W. anomalus through metabolic engineering.The results obtained in this study provide insights into the mechanisms involved in The online version contains supplementary material available at 10.1186/s12866-022-02691-y. Wickerhamomyces anomalus , formerly known as Pichia anomala, Hansenula anomala, and Candida pelliculosa, possesses a variety of unique physiological characteristics and metabolic features [W. anomalus secrete many kinds of glycosidases to improve the flavor of foods, generating numerous volatile ester compounds with floral and fruity aromas, and these compounds are safe for people and livestock [W. anomalus has been widely applied in various industrial fields including in food preservation and processing [features –3, such features . W. anomivestock , 6. W. aocessing , the eneocessing , and aquocessing . HoweverSaccharomyces cerevisiae improved the stability of cell membrane proteins and the cell membrane by increasing the synthesis of ergosterol and proline transporters under ethanol stress [Kluyveromyces marxianus stabilized its cell membrane structure by reducing the expression of some genes related to ergosterol and fatty acid synthesis [W. anomalus, the specific genes and pathways influenced by ethanol stress remain unclear.Studies have shown ethanol stress can trigger changes in a large number of genes and signal transduction pathways that are related to cell structure, substance metabolism and transport, and stress response , 11. Howl stress , 13. In ynthesis , 15. ForS. cerevisiae, the energy demand of yeast cells was maintained by adjusting the intracellular glucose metabolism pathways (glycolysis and TCA cycle), which helped cells to relieve the damage caused by an adverse environment (ethanol stress) [S. cerevisiae encountered ethanol stress [K. marxianus [K. marxianus stress response to ethanol, but not in the S. cerevisiae response. Therefore, it is necessary to explore the metabolites of W. anomalus in response to ethanol stress.At the metabolic level, yeast cells can cope with ethanol stress by adjusting various intracellular metabolic reactions and the contents of metabolites. For  stress) , 17. In l stress –20. Thusarxianus . The metW. anomalus C11, was obtained from the spontaneous fermentation of Rosa roxburghii Tratt by the sniffing method [W. anomalus. To the best of our knowledge, this study represents the first integrated transcriptomic and metabolomics analysis to reveal the ethanol stress response mechanism of W. anomalus.In our previous study, an intense aroma-producing yeast strain, g method . This st. anomalus cells were ellipsoidal, full, and smooth when no ethanol stress was induced (0% ethanol group). With the increase in ethanol concentration, the fullness decreased and the surface became uneven and deformed in many W. anomalus cells  and metabolic profile analysis (metabolomics).In addition, ethanol stress also reduced the biomass of Here, 7,141,520,437, 6,897,852,543, and 6,664,489,895 raw bases were obtained in 0%, 9%, and 12% ethanol treatment groups, respectively. The 0%, 9%, and 12% ethanol treatment groups obtained 6,969,933,135, 6,735,048,208 and 6,501,374,566 clean bases after data processing, respectively. The error rate in these three groups was 0.02%, and the values of Q20 (%) and Q30 (%) were greater than 90%  |> 2. There were 2227 DEGs found in the 9% ethanol treatment group compared to control group, of which 1,068 DEGs were upregulated and 1159 DEGs were downregulated  of ed Figs. .Fig. 3Di. These pathways are related to energy metabolism , amino acid metabolism , protein synthesis (ribosome), fatty acid metabolism , and oxidative stress regulation (peroxisome)  Fig. . In addime) Fig. A and B.FThe quality control analysis of the metabolome sequencing samples was carried out by PCA, and the results show sequencing samples of each group in positive and negative ion modes were densely clustered, and the repeatability of samples was stable Fig. S.P < 0.05. There were 3845 ion peaks and 267 annotated DEMs detected between the 9% ethanol treatment group and control group in positive and negative ion modes. Among them, 162 DEMs were upregulated and 105 were downregulated  were screened with the criteria of variable importance in the projection (VIP) > 1 and W. anomalus, including energy metabolism, protein biosynthesis, and nucleic acid metabolism.We further enriched the DEMs using the KEGG database and found that most DEMs were enriched in the pathways related to yeast growth, such as cell cycle-yeast, longevity regulating pathway-multiple species, and meiosis-yeast Figs. G and H. Integrated analysis was performed to further identify the DEGs obtained from transcriptomics and DEMs obtained from metabolomics. The results are shown in Fig. 6. Most of the significantly enriched DEGs and DEMs were associated with oxidative phosphorylation and galactose metabolism. Metabolic pathways, such as aspartate and glutamate metabolism, starch and sucrose metabolism, and arginine biosynthesis, were also enriched. Sphingolipid metabolism, lysine biosynthesis, cystcine and methionine metabolism an pyrimidine metabolism were only enriched in 12% ethanol treatment group. Validation of differentially expressed genes and metabolites.Wa60613, Wa78611, Wa13980, Wa63188, and Wa46076 were proved to be upregulated, while Wa31465, Wa92106, Wa34481, Wa60885, and Wa26420 were downregulated , mitochondrial energy metabolism-related genes , amino acid metabolism-related genes, as well as protein folding- and protein degradation-related genes. In contrast, ribosome biosynthesis-related genes were significantly downregulated. For K. marxianus, the expression of heat shock proteins significantly increased, while the expression of genes related to carbon metabolism, ribosome biosynthesis, unsaturated fatty acid, and sterol biosynthesis remarkably decreased under ethanol treatment. Therefore, energy metabolism, amino acid metabolism, nucleic acid metabolism, and fatty acid metabolism pathways may be the common target of ethanol attack for yeasts, including S. cerevisiae, K. marxianus, and W. anomalus [K. marxianus [S. cerevisiae or W. anomalus subjected to ethanol stress.Transcriptomics is a powerful technique that can elucidate gene transcriptional regulation by monitoring the expression profiles of all genes in a certain physiological state. It has been widely used to explore the mechanism of various stress responses in cells , 25. Li l stress , 26. Mosanomalus . It is warxianus , 15, butS. cerevisiae under ethanol stress. The authors found the profiles of fatty acids changed significantly [S. cerevisiae to ethanol stress [W. anomalus. The functions of fatty acids in the response to ethanol stress can be further confirmed in future research.In a recent study, a metabolomics approach, using gas chromatography coupled with mass spectrometry, was performed to investigate the metabolic changes of ficantly . In a stl stress . In the S. cerevisiae from damage caused by ethanol stress, which supports the ethanol tolerances observed in yeast cells [W. anomalus . But, further investigation of the underlying mechanisms of these amino acids roles in the ethanol response of W. anomalus is needed.Microorganisms have developed different strategies to minimize the adverse effects of ethanol stress, and several of them are connected to amino acid metabolism. For instance, the intracellular accumulation of L-proline protected st cells , 29. Enrlus Fig. . Therefol damage . In the arxianus  and W. aW. anomalus, and the response mechanism of ethanol stress, based on transcriptomics and metabolomics approaches. High concentrations of ethanol  remarkably inhibited the growth of W. anomalus. Energy metabolism, amino acid metabolism, fatty acids metabolism, and nucleic acid metabolism changed significantly in response to ethanol stress. Thus, the results obtained in this study could provide insights into the mechanisms involved in the response of W. anomalus to ethanol stress, enabling future metabolic engineering approaches to improve its ethanol tolerance.To the best of our knowledge, this was the first study to systematically analyze the effect of ethanol stress on the morphological characteristics and growth of W. anomalus C11 strain was separated from the fruit spontaneous fermentation broth of R. roxburghii and cultured with yeast extract peptone dextrose medium (YEPD) at 28 °C for 72 h. The culture was kept at 4 °C for later use.The W. anomalus cells were cultured with a YEPD medium at 28 °C for 8 h. Different concentrations of ethanol  were added, and the cells were cultured for 6 h to implement ethanol stress. Cells cultured without ethanol (0% Ethanol) were used as the control for this experiment and other experiments in this study. Yeast cells were centrifuged at 4, 000 × g for 10 min and washed three times with physiological saline. Then, the cells were re-suspended in 2.5% glutaraldehyde for 4 h at 4 °C and washed three times with 0.1 M PBS (pH 7.4). Subsequently, the cells were eluted with a gradient concentration of ethanol solutions  for 15 min each. Yeast cells were dried with a critical point dryer , coated with a gold/palladium alloy, and observed with an FEI Quanta FEG 450 scanning electron microscopy system .W. anomalus cells were inoculated into YEPD liquid medium and incubated at 28 °C with 180 rpm shaking for 8 h. The cells were treated with 3%, 6%, 9% or 12% (v/v) concentrations of ethanol, respectively. The optical density (OD) of the cultures was measured at 600 nm every 4 h. Each experiment was repeated three times.For the growth curve determination, W. anomalus cells were treated with 3%, 6%, 9%, or 12% (v/v) concentrations of ethanol for 6 h. Yeast cells were harvested, washed twice with distilled water, and re-suspended in distilled water at a same cell concentration (OD600 = 1). The cell suspension was diluted to 100, 10−2, and 10−4, and 2 μL of each diluent was spotted onto YEPD solid plates. The cells were incubated at 28 °C for 36 h. Colonies were observed and captured using a microscope .For the survival analysis, The yeast cells under ethanol stress were monitored for 6 h, using methylene blue staining, and the death rate was calculated by counting the number of dead cells in a random location on the plate. Ten locations were observed for each sample.W. anomalus cells under different ethanol treatments. Briefly, 30 mL of W. anomalus cells were collected by centrifugation at the speed of 4, 000 × g for 10 min. The cells were dried at 65 °C until a constant weight was achieved, and the weight was measured using an analytical balance .Biomass was detected by measuring the dry weight of W. anomalus cells were treated with 9% or 12% (v/v) concentrations of ethanol for 6 h, collected via centrifugation at the speed of 4, 000 × g for 10 min, frozen quickly using liquid nitrogen, and stored at − 80 °C for transcriptomics and metabolomics analyses.Total RNA was extracted from the yeast cells using Trizol reagent  according to the manufacturer’s instructions, and genomic DNA was removed using DNase I . An RNA-seq transcriptome library was prepared using a TruSeqTM RNA sample preparation Kit from Illumina  and 1 μg of total RNA. Libraries were size-selected for cDNA target fragments of 300 bp on 2% Low Range Ultra Agarose, followed by PCR amplification using Phusion DNA polymerase  for 15 PCR cycles. The paired-end RNA-seq sequencing library was sequenced with the Illumina HiSeq xten/NovaSeq 6000 sequencer .https://github.com/jstjohn/SeqPrep) and Sickle (https://github.com/najoshi/sickle) with default parameters. Then, clean reads were separately aligned to reference genomes with orientation mode using HISAT2 (http://ccb.jhu.edu/software/hisat2/index.shtml) software. The mapped reads of each sample were assembled by StringTie (https://ccb.jhu.edu/software/stringtie/index.shtml? t = example) in a reference-based approach. To identify differentially expressed genes (DEGs), the expression level of each transcript was calculated according to the transcripts per million reads method. RSEM (http://deweylab.biostat.wisc.edu/rsem/) was used to quantify gene abundances.The raw paired-end reads were trimmed and controlled by SeqPrep (https://github.com/tanghaibao/Goatools) and KOBAS (http://kobas.cbi.pku.edu.cn/home.do) [In addition, KEGG pathway analysis was carried out by Goatools (home.do) –33.https://cloud.majorbio.com).Liquid chromatography-mass spectrometry (LC–MS)-based metabolomics was conducted by Majorbio Biotech . Briefly, 50 mg of the ethanol-treated samples or control sample were accurately weighed, and the metabolites were extracted using 400 µL of methanol:water  solution, containing 0.02 mg/mL of L-2-chlorophenylalanine as the internal standard. The mixture was allowed to settle at − 20 °C, and then it was vortexed for 30 s and ultrasonicated at 40 kHz for 30 min at 5 °C. The supernatant was carefully prepared for LC–MS analysis after centrifugation at 13,000 × g for 15 min. The chromatographic separation and mass spectrometric conditions were from a previously published method by Chen . The rawactin and analyzed using the 2−ΔΔCT method.  Total RNA was extracted from the ethanol-treated groups and control group using Trizol reagent , according to the manufacturer's instructions. cDNA was transcribed using the PrimerScript RT Reagent Kit (Takara). Real-time PCR was performed using the Light Cycler 96 detection system . The primers used for q-PCR in this study are shown in Table SW. anomalus cells were cultured at 28 °C for 8 h and then divided into ten groups. For the control group and ethanol treatment groups, no ethanol and 9% or 12% (v/v) ethanol were added, respectively. For the amino acid supplementary groups, 9% or 12% (v/v) ethanol was added, along with 5 mM of exogenous aspartate, glutamate, or arginine. All the treatment groups were further cultured at 28 °C for 8 h. The cultures were diluted to 100, 10−2, or 10−4, spread on YEPD solid medium and incubated at 28 °C for 36 h.P < 0.05 was considered statistically significant.The results are expressed as the mean ± standard deviation. Univariate analysis of variance (ANOVA) of the data and the determination of the significance of the difference were performed using SPSS 21.0 software. Additional file 1:FigureS1. Cells death determination under different concentrations of ethanoltreatment by methylene blue staining. A,0% ethanol treatment group; B, 3% ethanol treatment group; C, 6% ethanoltreatment group; D, 9% ethanol treatment group; E, 12% ethanol treatment group.Bar=100 μm.Figure S2.  Results of principalcomponent analysis (PCA) of the samples for transcriptome sequencing. FigureS3.  PCA score plots of the samples for metabolomicsanalysis in positive and negative ion modes. A, Positive ion mode; B, Negativeion mode. Table S7. Primersused in this study for real-time quantitative PCR detection.Additional file 2: Table S1.Additional file 3:Table S2.Additional file 4:Table S3.Additional file 5:Table S4.Additional file 6:Table S5.Additional file 7:Table S6.


To evaluate the application of such an approach in preclinical investigations, the plasma pharmacokinetic profiles of seven commonly used probe substrates of microsomal enzymes, including phenacetin, tolbutamide, omeprazole, metoprolol, chlorzoxazone, nifedipine, and baicalein, were predicted in rats using bottom-up PBPK models built with in vitro data alone. The prediction’s reliability was assessed by comparison with in vivo pharmacokinetic data reported in the literature. The overall predicted accuracy of PBPK models was good with most fold errors within 2, and the coefficient of determination (R2) between the predicted concentration data and the observed ones was more than 0.8. Moreover, most of the observation dots were within the prediction span of the sensitivity analysis. We conclude that PBPK modeling with acceptable accuracy may be incorporated into preclinical studies to refine in vivo investigations, and PBPK modeling is a feasible strategy to practice the principles of 3Rs.Pharmacokinetic characterization plays a vital role in drug discovery and development. Although involving numerous laboratory animals with error-prone, labor-intensive, and time-consuming procedures, pharmacokinetic profiling is still irreplaceable in preclinical studies. With physiologically based pharmacokinetic (PBPK) modeling, the  Becausemization . Howevermization . Therefomization , attempt process . For exasorption  or metabsorption , such as process . DespitePBPK modeling was raised back in 1937 and initially applied in predicting the distribution of environmental compounds in mammalian tissues , and furin vitro PK parameters for bottom–up modeling or experimental in vivo parameters for “middle-out” approaches as preliminary verification and model optimization  to predict the systemic disposition of seven commonly used liver microsomal enzyme probe substrates in rats. The reliability of each prediction was examined with sensitivity analysis. As a practice of 3Rs, this proof-of-concept study will shed light on the refinement of current drug development procedures and may reduce animal usage in drug development.To evaluate the feasibility of using PBPK modeling as an alternative for animal experiments, we developed bottom–up PBPK models solely with in vitro parameters were divided into three categories. Physical chemistry and blood binding-related parameters include molecular weight (MW), neutral species octanol: water partition coefficient (logPo:w), compound type (base/acid/mono/diprotic/ampholyte), negative decadic logarithm of the ionization constant of an acid (pKa), blood to plasma partition ratio (B/P), and fraction unbound in plasma (fu). Distribution is affected by the free fraction and the lipid solubility of the drug, which is related to the physiological characteristics mentioned, such as logPo:w, pKa, and fu. Parameters about absorption include apparent permeability coefficients (Papp), polar surface area (PSA), and hydrogen bond donors (HBD). In vivo elimination can be extrapolated via a well-stirred liver model together with the parallel tube model , maximum velocity for metabolism (Vmax), and intrinsic clearance (CLint). Various in vitro data of model drugs collected from different literature and databases  were compiled in Typical probe substrates of cytochrome P450 (CYP) 1A2, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4/5, and UDP-glucuronosyltransferase (UGT), namely, phenacetin, tolbutamide, omeprazole, metoprolol, chlorzoxazone, nifedipine, and baicalein were used as model drugs. The be model  and the be model  using inin vitro data alone using Simcyp® . The in vitro parameters used in the PBPK model are described in Peff) was calculated based on its relationship with Peff in human  with in vitro parameters of logPo:w, compound type, and pKa using mechanistic model 2  based on a perfusion-limited model. The Kp scalar was selected as 1 by default in our model. The whole organ metabolic clearance pane was selected in the elimination screen. The liver or intestinal clearance was extrapolated via in vitro metabolic data.PBPK models were constructed with in human , which ceriments  or predieriments . Initial model 2 , which i(0–t)], peak plasma concentration (Cmax), and time to reach Cmax (Tmax)} after orally administered single-dose of model drugs were predicted. Meanwhile, the PK process of some model drugs administrated with various doses was simulated.The drug plasma concentration–time profile and PK parameters {areas under the concentration–time curve to last time point  was 17.1–59.9 μl/min/mg protein and the range of CLint [intestine microsomes (IM)] was 7.37–14.7 μl/min/mg protein. The median value of varied parameters was calculated as input parameters in The elimination of metoprolol fu ranged from 0.046 to 0.373 and the CLint was from 5.00 to 38.8 μl/min/mg protein. The value of other parameters collected from different sources was relatively consistent. Similarly, the median value was taken as the input value in Chlorzoxazone is the probe substrate of CYP2E1. As shown in fu was 0.01–0.08, and the median value was calculated for input. The clearance of nifedipine was found related to concentration , Cmax, and Tmax) of model drugs in rats orally administrated with a different single-dose of the drugs were assembled from the literature. The AUC(0–t), Cmax, and Tmax units were unified as μg·h/ml, μg/ml, and h, respectively. The observed drug plasma concentration–time data were extracted from concentration–time course curves using the GetData software . Multiple records of PK parameters of model drugs were summarized in max and AUC(0–t) at high doses were lower than low doses for some records : 761.7217–1,393 μg·h/ml at 20 mg/kg vs. 183.88 μg·h/ml at 20 mg/kg), and Tmax also varied greatly (0.89–7.1 h). The relatively consistent PK value at a 50 mg/kg dose was finally selected for the tolbutamide PBPK model accuracy evaluation. Meanwhile, it is difficult to detect baicalein’s plasma concentration because of the complicated in vivo disposition, poor bioavailability, and extensive metabolism, leading to a considerable variation among the PK parameters. The ones after oral administration of 121 mg/kg were selected due to relatively consistent values (shown in (0–t) and Cmax of metoprolol were 5–7 times higher and 2 times higher in DA (Dark Agouti) rats and Sprague–Dawley rats, respectively, than those in Wistar rats after oral administration of 5 mg/kg ((0–t) of baicalein was 11.7 times higher after oral administration of baicalein–nicotinamide nano-cocrystals than coarse powder, 7.1 times higher than that of baicalein nanocrystals, and 1.8 times higher than that of baicalein–nicotinamide cocrystals  were commonly consistent, while the fu and CLint (LM or hepatocytes) values obtained from the literature varied. To further explore the reliability of the PBPK model, the effect of fu and CLint (LM or hepatocytes) was investigated. The sensitivity analysis of fu and CLint (LM or hepatocytes) was determined via comparing the predicted results of PBPK models, which were constructed using the range values of fu and CLint (LM or hepatocytes) with all the other factors maintained constant. The uncertainty range of the PBPK model was defined as the predicted range of the PK parameters in the sensitivity analysis. The sensitivity results were plotted via Simcyp and showed in As shown in To verify the prediction accuracy of the PBPK model, we compared the predicted parameters with the experimental data, and fold error was introduced to measure the deviation. The fold error is the ratio between the predicted PK parameters and the corresponding observed values shown in , 2. The 2, mean absolute error (MAE), and root mean squared error (RMSE) were applied to evaluate the overall performance of the PBPK model. The equations are presented as follows .i and yi are the observed and the predicted concentrations, respectively, ‾x is the average of the observed values, and ‾N is the number of data points.R follows –5. The lin vitro parameters . The predicted results of each drug were presented later, and the PK curves are displayed in PBPK models for probe drugs  were constructed using max was slightly higher than the observed one with the fold error of 1.93 at 5 mg/kg, the fold error values were all within the threshold of 2, indicating good simulation performance. Meanwhile, the predicted data were slightly higher than the observed ones at 5 mg/kg, while other observed points were around the predicted values at 10 and 20 mg/kg  mainly were within 2, indicating acceptable prediction performance. Although the difference between predicted Tmax and the observed values was more than 2 times (2.16 times), with a significant individual variation between the measured values of Tmax (0.08–1.5 h), the fold errors of Cmax and AUC(0–t) were within 2, indicating good prediction accuracy. As shown in max at 5 mg/kg, while the observed dots were close to the predicted ones at 3 and 6 mg/kg.The predicted results of the nifedipine PBPK model are shown in max was slightly lower than the observed one, the fold errors were within 2. The observed PK data spread around the predicted profile, suggesting good prediction performance.The simulation results are shown in fu and CLint (LM or hepatocytes) was evaluated with other factors maintained constant. The variation spans of fu and CLint (LM or hepatocytes) from various literatures are listed in fu and CLint (LM or hepatocytes) obtained from various literatures.The sensitivity analysis of fu (0.145–0.5) and CLint (hepatocytes: 20.7–78 μl/min/106 cells) for the phenacetin PBPK model was performed at 10 mg/kg  ranged from 0.18 to 0.28 h, 1.93 to 15.74 μg/ml, and 1.10 to 14.25 μg·h/ml, respectively. Most concentration points are within the uncertainty range.The sensitivity analysis of shown in . The prefu (0.0201–0.268) and CLint (LM: 2.72–8.1 μl/min/mg protein) was investigated for the tolbutamide PBPK model at 50 mg/ml  ranged from 0.32 to 0.71 h, 152.72 to 260.15 μg/ml, and 231.67 to 4,587.60 μg·h/ml, respectively. The observed points were in the area between the median prediction curve and the lowest prediction curve .The sensitivity analysis of shown in . As a refu (0.105–0.232) and CLint (LM: 119–188 μl/min/mg protein) was applied for the sensitivity analysis of the omeprazole PBPK model at 10 mg/kg  ranged from 0.38 to 0.46 h, 0.21 to 0.77 μg/ml, and 0.26 to 0.98 μg·h/ml, respectively. As shown in The range of shown in . The resfu (0.80–0.925) and CLint (LM: 17.1–59.9 μl/min/mg protein) was determined for the metoprolol PBPK model at 2.5 mg/kg in max, Cmax, and AUC(0–t) were 0.54–0.79 h, 0.15–0.28 μg/ml, and 0.25–1.02 μg·h/ml, respectively. It is illustrated in The sensitivity analysis of fu ranging from 0.046 to 0.373 and CLint (LM) from 5 to 38.8 μl/min/mg protein  ranged from 0.29 to 0.70 h, 37.69 to 199.27 μg/ml, and 34.69 to 1970.18 μg·h/ml, respectively. The observation dots were around the lowest prediction curve (Prediction_Minimum limitation curve), as shown in Sensitivity analysis of the chlorzoxazone PBPK model was performed at 50 mg/kg with the shown in . The resfu (0.01–0.08) and CLint (LM: 35.18–402 μl/min/mg protein) was investigated for the nifedipine PBPK model at 3 mg/kg  ranged from 0.36 to 1.20 h, 0.51 to 7.40 μg/ml, and 0.52 to 47.27 μg·h/ml, respectively. The observation points were centered on the median prediction curve, as shown in The sensitivity analysis of shown in . The prefu and CLint (LM) was performed with the range from 0.029 to 0.0791 and 338.9 to 574.11 μl/min/mg protein for the baicalein PBPK model at 121 mg/kg (shown in ). The results showed the predicted Tmax, Cmax, and AUC(0–t) ranged from 0.19 to 0.36 h, 0.59 to 2.08 μg/ml, and 0.51 to 2.36 μg·h/ml, respectively. As illustrated in The sensitivity analysis of shown in . The ren = 390, shown in Papp from in vitro experiments (Caco-2 experimentation). The input of Papp was also applied to the median data of the various values. As shown in 2, was better than using Papp from Caco-2 experimentation. As displayed in The fold errors are summarized in in vitro parameters, we noticed the fu and CLint from different sources varied; therefore, the median values were applied to construct the model. A sensitivity analysis was performed to further explore the impact of these two parameters on the model prediction accuracy. The overall predicted accuracy of PBPK models using the median value was good with most fold errors within 2, and the R2 between the predicted concentration data and the observed data was more than 0.8 (int (38.8 μl/min/mg protein) and fu 0.373  were 0.29 h, 37.69 μg/ml, and 34.69 μg·h/ml, respectively. Moreover, most of the fold errors were within 2 (observation 1–3), indicating good prediction accuracy. Similarly, the prediction result of metoprolol using the median PBPK model was also slightly higher at 2.5 mg/kg, and the observed data was close to the lowest predicted curve. The prediction Tmax, Cmax, and AUC(0–t) of the metoprolol PBPK model using highest experimental CLint (59.9 μl/min/mg) and fu (0.93) at 2.5 mg/kg were 0.53 h, 0.15 μg/ml, and 0.25 μg·h/ml, respectively and the fold errors were within 2 , the variation of prediction Tmax in the sensitivity analysis of other drugs was slight, meaning that the experimental difference of fu and CLint had little impact on Tmax compared with Cmax and AUC(0–t). Compared with phenacetin, omeprazole, metoprolol, and baicalein, the experimental variation of fu or CLint of nifedipine, chlorzoxazone, and tolbutamide is relatively large (fold errors of fu or CLint > 8), which led to a large variation span in the sensitivity analysis with the fold errors of Cmax or AUC(0–t) more than 10. Meanwhile, the variation of both fu and CLint was higher than 3, resulting in significant fold errors of Cmax and AUC(0–t) (>8.0). Moreover, inflection points were noted in the change trend graph of fu and CLint of phenacetin, nifedipine, chlorzoxazone, and tolbutamide shown in the figure of sensitivity analysis, with the prediction value significantly fluctuating around. Therefore, sensitivity analysis is necessary for model construction. That is to say, when the in vitro parameters are close to the inflection point, extra attention should be paid to the PBPK model construction.In the sensitivity analysis, we summarized the variation span of the experimental Papp from in vitro experiments such as Caco-2 experimentation. Interestingly, the results showed that the overall prediction performance of PBPK models using the PSA and HBD methods was better. It could be related to defects in the in vitro absorption experiments, such as the inability to simulate the dynamic flow of fluids in the body, the lack of mucus layer, the more minor tight junctions, and the thicker unstirred water layer. These may cause the Papp to not to truly simulate the diffusion of drugs in vivo and thus cannot truly predict the drug absorption degree in the body.In addition to using the PSA and HBD methods to predict the absorption of model drugs in the PBPK model, we also explored the prediction accuracy of the PBPK model using in vitro parameters is beneficial to drug development, and the more accurate the value of in vitro parameters, the better fit the model. However, the model compounds we applied were BCS (biopharmaceutics classification system) class II drugs, exhibited linear absorption and primarily cleared via metabolism. Therefore, our models were appropriate to the PK prediction of chemicals not affected by transporters such as P-gp (p-glycoprotein) efflux transporter. For other compounds with complex drug metabolism characteristics, using in vitro parameters alone to construct a PBPK model and the prediction accuracy still require further research and validation. Moreover, in recent years, with the development of computer technology and in vitro experiments, the contribution of PBPK models to advancing 3Rs in new drug development is undoubtedly a hot topic of discussion. Since rodents were widely used in preclinical pharmacological experiments, this study used rats as the model animal when constructing PBPK models based on in vitro parameters. It is expected that animal experiments can be reduced in the early stage of drug development with the validation of the feasibility and reliability of the models. However, due to the differences in physiological parameters of other species, the construction of their PBPK models needs further exploration and optimization, and due to the complexity of the human body, whether this method can be directly applied to predict the pharmacokinetic characteristics of candidate compounds in the humans and the reliability of the predicted results still need further exploration.It is worth mentioning that the PBPK model using in vitro data were developed, and popular probe substrates were used as model drugs. Most of the fold errors between the observed PK data with the predicted ones were within the threshold of 2, indicating good prediction accuracy. The influence of in vitro data was comprehensively analyzed, and results supported that the model accuracy is related to the precision of the in vitro data. Moreover, most of the observed PK data is within the uncertainty range, and R2 is more than 0.8, indicating the applicability of the PBPK model in the absence of in vivo data in the early drug development. In conclusion, a strategy of the bottom–up PBPK model with the quantity of an uncertainty range is constructed, which helps reduce animal experiments and is a good practice of 3Rs in the early screening of candidate compounds.In our study, bottom–up PBPK models constructed with


We studied the prognosis and clinicopathological background of lung adenocarcinoma predominance among patients who underwent lobectomy using data from the Japanese Joint Committee of Lung Cancer Registry.Two thousand eight hundred sixty-three cases were extracted. Recurrence free survival (RFS) rates, overall survival (OS) rates and clinicopathological factors and epidermal growth factor receptor (EGFR) mutation status were examined.Median follow-up period was 65.5 months. Adenocarcinoma predominance was sub-grouped according to OS and RFS rate. In pathological stage I, 5-year RFS and OS rates were respectively 92.2% and 95.8% in group A , 89.3% and 92.1% in group B (lepidic), 79.2% and 89.7% in group C (papillary + acinar + variants) and 69.0% and 79.0% in group D (solid + micropapillary). In pathological stage II + IIIA, they were, 43.6% and 72.4% in B, 39.5% and 66.9% in C and 31.0% and 53.7% in D. Group D showed significant worst outcome both in stage I and II + IIIA. Up stage rate from clinical stage I to pathological stage II + IIIA was 0.0%, 3.7%, 15.9% and 33.3%. The frequency of lymph-vessel, vascular, pleura invasion and positive EGFR mutation were 0.0%, 0.0%, 0.0% and 57.1% in group A, 15.6%, 10.0%, 12.1% and 55.1% in B, 36.6%, 31.8%, 29.7% and 44.9% in C, 50.2%, 57.8%, 38.9% and 21.3% in D. In group D, lymph-vessel, vascular and pleura invasion were most, EGFR mutation was least frequent not only in pathological stage I but also stage II + IIIA. In multivariate analysis, age, pathological stage, vascular invasion, and group D were independent factors affected RFS and OS.Limited to lobectomy cases, solid + micropapillary was independent prognostic factor both in early and locally advanced stage. Its malignant degree was related to the frequency of pathological invasive factors and EGFR mutation status. Adenocarcinoma is the most frequent histological type of lung cancer, and its frequency has been increasing , 2. In 2The JJCLCR conducted a nationwide registry study of patients who had undergone lung cancer surgery. The committee asked 629 teaching hospitals certified by the Japanese Board of General Thoracic Surgery to participate in the study. The registry followed the ethical guidelines for epidemiologic studies; the study protocol was approved by the institutional review board of Osaka University Hospital  and registered at University Hospital Medical Information Network- Clinical Trials Registry (UMIN000020215).From the 18,973 cases in the JJCLR dataset in 2010, cases of a lobectomy with mediastinal lymph-node dissection achieving R0 with comprehensive information of adenocarcinoma predominance by IASLC/ATS/ERS classification were selected. Cases of induction therapy, histology other than adenocarcinoma, did not meet the pathological definition of IASLC / ATS / ERS classification and a final pathological stage higher than IIIA were excluded. The final number of cases analyzed for this study was 2,863.th Edition classification, lymphatic vessel invasion, vascular invasion, pleural invasion, epidermal growth factor receptor (EGFR) mutation status, adjuvant therapy, clinical stage, pathological stage and lung adenocarcinoma predominant histological subtype. Predominant subtypes of adenocarcinoma were pathologically diagnosed at each facility, not undergone by a central review. Variants consisted of invasive mucinous, colloid, fetal (low and high grade) and enteric adenocarcinoma. Postoperative recurrence was determined by each physician and histopathologic confirmation at the time of recurrence was not required.Overall survival (OS) and recurrence-free survival (RFS) rates were calculated, and their relationship to the following clinicopathological factors was examined: age, gender, smoking history, operation time, transfusion, pathological stage according to TNM 7OS was defined as the time interval from surgery to death from any cause. RFS was defined as the time interval from surgery to the time of first recurrence or death. For those patients who were living, data on RFS were censored at the last visit.P values of less than 0.05 were considered to indicate statistical significance.The data were analyzed using the Kaplan–Meier method and compared using the log-rank test. Patients were excluded from the analyses for RFS if the time of their recurrence was not available. Clinicopathological data were evaluated from univariate analysis. Descriptive statistics used included means and standard deviations for continuous variables and percentages for categorical variables, which were compared using Mann–Whitney U and chi-squared tests. Multivariate analysis was performed with a Cox proportional-hazards regression model. Results are summarized as a hazard ratio (HR) with 95% confidence intervals (CI). HR represents the relative increase (or decrease if < 1) in the risk of recurrence or death. The median follow-up period was 65.5 months. Table p < 0.001).EGFR mutation was examined in 1424 case: 49.7% of all cases. The frequency of positive EGFR mutation was 45.0% for all cases examined. 912 cases were examined in no recurrence (40.8%), but 551 cases were in recurred cases (79.7%). The number of no examination of EGFR mutation was 1441 cases, 90.3% in no recurrence and 9.7% in recurred cases. Examination of EGFR mutation was frequently performed in recurred cases . For additional analysis, the pathological stages were combined and termed as I (IA + IB) and II + IIIA (IIA + IIB + IIIA). The 5-year RFS rate for stage I was 82.7% and 38.0% for stage II + IIIA.The RFS rate was 72.1% at 5 years in all cases. The RFS curves according to pathological stage are shown in Fig. p = 0.446). Papillary, acinar and variants showed similar RFS curves; no statistical differences were observed . RFS curves for solid and micropapillary almost overlapped (p = 0.823). Therefore, adenocarcinoma predominant subtypes can be combined to AIS + MIA (group A), lepidic (group B), papillary + acinar + variants (group C) and solid + micropapillary (group D). The 5-year RFS rate of group C was 67.5% and 50.8% of group D.RFS curves according to predominant histological subtype are shown in Fig. p = 0.171). Group C showed worse RFS than Group A  and group B . Group D showed worse RFS than group C .In pathological stage I, the RFS curves for each predominant subtype groups are shown in Fig. p = 0.181). Group D showed significant worse outcome than group B  and group C .In pathological stage II + IIIA, RFS curves are shown in Fig. p < 0.001, all), and stage IB also showed better outcome then higher stages . But the differences between IIA and IIB, IIB and IIIA were not significant . From these results, pathological stages can be combined and indicated as stage IA + IB and IIA + IIB + IIIA. The 5-year OS rates were 90.3% for stage IA + IB and 64.6% for stage IIA + IIB + IIIA.The OS rate at 5 years was 84.1% in all cases. OS curves according to pathological stage are shown in Fig. p = 0.634, 0.107, 0.231). The difference between acinar and variants, papillary and variants were not observed . Solid showed worse outcome than that of AIS, MIA, lepidic, acinar, and papillary , but not with micropapillary (p = 0.119). Thus, these predominant histological subtypes can be combined in the same manner as for RFS.The OS curves according to predominant histological subtype are shown in Fig. p = 0.561). Group C showed worse outcome than that of group A , and group B . Group D showed worse outcome than group C .In pathological stage I, OS curves of histological subtype groups are shown in Fig. p = 0.018, HR = 1.949 [1.116–3.430]) and group C .The OS curves in pathological stage II + III are shown in Fig. Table p < 0.01). This was similar in pathological stage; 52.3% (150/287), 71.1% (1242/1745), 89.2% (469/526) and 100% (304/304) (p < 0.01). Concerning pathological stage II + IIIA, group D was most frequent of 47.7% followed by 28.8% of group C, 10.8% of group B (p < 0.01). Up stage rate from clinical stage I to pathological stage II + IIIA in each group was 0.0%, 3.7%, 15.9% and 33.3%. EGFR mutation in group D was least frequent o21.3% than 51.3% of other groups (p < 0.01).In clinical stage, stage I was less frequent of 78.4% (225/287) in group D, followed by 84.6% (1477/1745) of group C, 92.6% (487/526) of group B and 99.7% of group A (303/304) . When group D was compared with group C, the rates of male, smoker, Ly + , V + and EGFR mutation were significantly different . The rate of positive EGFR mutation rate in group D was the least (13.6%) and was significantly different from group A, B and C . Cases of adjuvant therapy were rare in group A.Table p = 0.023, p < 0.001, p = 0.003). In the group D, the rates of Ly + and V + were higher compared with group C (p = 0.023 v. 0.003). The rate of positive EGFR mutation in group D was also least (27.0%) and was significantly lower than group B and C . Two-thirds of the patients in the stage II + IIIA group received adjuvant chemotherapy.Table Table To our knowledge, this is the most expansive report to date on the relationship of prognostic outcome and lung adenocarcinoma predominant subtype. By selecting cases who had undergone lobectomy with mediastinal lymph-node dissection, it enabled us to minimize stage migration, and figure out precise malignant behavior of each predominant subtype. As expected, patients with group A showed excellent outcome. Majority of them were in stage I, with only 3.0% in a higher stage. However, even in the group A, there was a small percentage with lymph node metastasis. Especially in MIA, defined as invasion of size ≤ 5 mm, if an aggressive component exists in this small invasive lesion, it might metastasize to the hilar and/or mediastinal lymph node and up staged.From the report limited to stage I, invasive adenocarcinoma has been further grouped as intermediate or high grade . In the The 5-year RFS and OS rate of solid and micropapillary predominant were similar, thus, these subtypes can be dealt as one group. Group D showed the worst outcome, not only in early stage but also in higher stage. Originally, in pathological stage I, these subtypes were categorized as high-grade. But even in higher pathological stages of II + IIIA, it also showed worst outcome than other subtype groups. Up stage rate of group D from clinical stage I to pathological stage II + IIIA was 33.8%, much higher than other groups. It also indicates potential malignant behavior of group D. Multivariate analysis including pathological stage showed independent negative impact of group D for both RFS and OS rates.We examined pathological invasive factors to analyze negative impact to RFS and OS rates in each predominance groups. Three factors; Ly + , V + and PL + were reported as showing significantly worse prognosis in lung cancer , 17. In The IASLC/ATS/ERS classifications was introduced in 2011, they separated invasive adenocarcinoma into 5 types according to dominant histologic pattern by microscopic findings. Including AIS, MIA and variants, there are totally 8 types of predominance. This classification was corelated with prognosis, but the number as a prognostic indicator thought to be too many, it would be inconvenient in clinical situation. And small percent of solid or micropapillary component regardless of its main predominance was reported to have negative impact for prognosis . In thisThere are several limitations in this study. First, although this is an expansive report on the analysis of the impact of predominant histological subtype of adenocarcinoma, it is a retrospective study and potential bias may exist. Second, central review of pathological factors including histology of adenocarcinoma, lymphatic vessel invasion, vascular invasion and pleural invasion was not performed. Third, EGFR status was only known for half of cases included. Most of them was examined in recurred cases, it might not reflect true characteristics of each pathological predominant group. Fourth, postoperative surveillance was not uniform and might have affected the time of detecting recurrence.Predominant subtype of adenocarcinoma is one of the prognostic factors. Especially, solid + micropapillary showed the worst outcome not only in stage I but also in stage II + IIIA. The frequency of pathological invasive factors and EGFR mutation might be related to the malignant behavior of each adenocarcinoma predominance.


Escherichia coli, a major route of furfural toxicity is the depletion of NADPH pools due to its use as a substrate by the YqhD enzyme that reduces furfural to its less toxic alcohol form. Here, we examine the potential of exploiting benzyl alcohol dehydrogenases as an alternative means to provide this same catalytic function but using the more abundant reductant NADH, as a strategy to increase the capacity for furfural removal. We determine the biochemical properties of three of these enzymes, from Pseudomonas putida, Acinetobacter calcoaceticus, and Burkholderia ambifaria, which all demonstrate furfural reductase activity. Furthermore, we show that the P. putida and B. ambifaria enzymes are able to provide substantial increases in furfural tolerance in vivo, by allowing more rapid conversion to furfuryl alcohol and resumption of growth. The study demonstrates that methods to seek alternative cofactor dependent enzymes can improve the intrinsic robustness of microbial chassis to feedstock inhibitors.While lignocellulose is a promising source of renewable sugars for microbial fermentations, the presence of inhibitory compounds in typical lignocellulosic feedstocks, such as furfural, has hindered their utilisation. In  Correspse NADPH , the expse NADPH , the rerse NADPH ,25, and se NADPH ,27.E. coli is the NADH-dependent lactaldehyde reductase FucO ) were then plotted against the latter. A curve was fitted on each plot using non-linear regression for the estimation of Vmax. This analysis has been provided as ‘Supplementary Data S4.xlsx’. The following equations were used to determine the KM and kcat:The The structures for AcBAD and BaBAD were obtained from the Protein Data Bank  . Additionally, citric acid was added to 0.05% (w/v) to prevent precipitation of iron, and casamino acids to 0.01% (w/v) in order to alleviate stress caused by recombinant protein expression [Growth assays were carried out in M9 medium  with 2% solution  . The column was an Acquity UPLC BEH C18, 130 Å, 1.7 µm, 2.1 mm × 100 mm (Waters). The mobile phase A) was 5% (v/v) methanol  with 0.1% (v/v) acetic acid (Fisher Optima), B) methanol with 0.1% (v/v) acetic acid. The elution gradient was 16% (v/v) B) isocratic for 2.5 min, which was ramped up to 100% B) over 0.3 min, left there for 0.1 min, and returned to 16% (v/v) B) over 0.4 min; total run time was 4 min. Flow rate was 0.5 ml/min, injection volume 2 µl, column temperature 45°C, and sample temperature 10°C. The UV detection wavelength was 230 nm. Standard curves of furfural, furoic acid and furfuryl alcohol  were constructed in water. Data analysis was undertaken with Xcalibur 4.0 . OriginPro 2021b software was used to plot the concentration and OD


We study the asymptotic behaviour of solutions of a boundary value problem for the Laplace equation in a perforated domain in This article is part of the theme issue ‘Non-smooth variational problems and applications’. A family of Poincaré problems approximating a mixed boundary value problem for the Laplace equation in the plane has been studied by Wendland et al. . A studyf Kirsch . Costabef Kirsch  studied f Kirsch . Also, sf Kirsch  by meansf Kirsch  have invf Kirsch , where tatum. In  we proveatum. In , we consatum. In –15). Allatum. In –18). HowIn , we conset al. [et al. [The purpose of the present article is to give answers to these questions. Here we consider only the case of dimension of Il’in  and the  [et al. ,21, whic [et al.  provided [et al. –27). Mor [et al.  for seve [et al.  for applet al. [et al. [Instead of the methods of asymptotic analysis, here we exploit the so-called functional-analytic approach proposed by Lanza de Cristoforis in . The goaet al. . Here, wet al.  and Lanzet al.  and to aet al. –36 and F [et al. .et al. [et al. [et al. [As a first step, we introduce the geometric setting in which we are going to consider our boundary value problem. As the dimensional parameter, we take a natural numberrudinger  for the nd Murat ,40 and ond Murat , as wellnd Murat , Arrieta [et al.  and Ferr [et al. . We also [et al.  concerni [et al. ,47 on diWe observe that in the present article, the boundary of the hole depends on ϵ simply through a dilation. However, in the literature one can find examples where the geometry changes in a more drastic way, as in the case of oscillating boundaries (see e.g. –44). On This article is organized as follows. In §2, we analyse a toy problem in an annular domain. In §3, we transform problem  into an . 2As we have done in , we consOur aim is to recover and understand such behaviour of the solution and of its energy integral in a more general situation, for both the geometry and the boundary conditions. Indeed, we will show that the main features discussed above can be identified in the general solution . So lettions of . The pro , t, t perturbations of the linear case.. 4We observe that under the additional assumption , equatioMotivated by  and 4.24.2, we rThen, if initions  and (4.5e system –4.2). T. Tϵ∈ ar arϵ∈. T. Tϵ∈ inWe note that if e system –4.9), t, t by by, real aet al. ), assumpet al. , p. 10, et al.  and Valeet al. , theorem dσy si siF is lNow that we have converted  into a sLet the assumptions of proposition 4.1 hold. Then we setBy propositions 3.2 and 4.1 and definition 4.3, we deduce that for each  problem . In the Let the assumptions of proposition 4.1 hold. Letbe a bounded open subset ofsuch thatThen there existand a real analytic mapfromtosuch thatfor allandfor allMoreover, if we setwe have thatandsolves the Neumann problemTaking nel (cf. ), we verequality  holds. Bial ,  problem . The proBy proposition 4.1, remark 4.2 and theorem 4.4, if We now consider in theorem 4.6 the behaviour of the rescaled solution Let the assumptions of proposition 4.1 hold. Letbe a bounded open subset ofThen there existand a real analytic mapfromtosuch thatfor allandfor allMoreover, if we setwe have thatandsolves the (nonlinear) Robin problemTaking nel (cf. ), we verequality  holds. Bial ,  problem . The proFinally, we consider the energy integral Let the assumptions of proposition 4.1 hold. Letbe as in theorem 4.6. Then there existand a real analytic mapfromtosuch thatfor allMoreover,Let equality  holds. Mins (cf. , §§3.4 aequality  holds.. 5We have studied the asymptotic behaviour of solutions of a boundary value problem for the Laplace equation in a perforated domain of sumption  we need


The pheS gene was cloned into the pUC19 vector and used to construct a standard curve for absolute quantification. Standard curves for quantification were constructed for each LAB species for serial dilutions between 1011 and 106 CFU/mL, with R2 > 0.99. The number of viable bacteria in the fermented milk detected by PMA-qPCR was significantly lower than that of qPCR (P < 0.05), indicating that PMA inhibited the amplification of DNA from dead cells. This was corroborated by the results from bacterial staining and plate count experiments. The proposed PMA-qPCR method provided rapid qualitative and quantitative determination of the number of viable bacteria for each LAB species in fermented milk within 3 h.The number of viable lactic acid bacteria (LAB) is a key indicator of the quality of fermented milk. Currently, the combination of propidium monoazide (PMA) and qPCR has been applied in the quantification of viable bacteria in various matrices. In this research, the PMA-qPCR method was used to detect the number of viable bacteria of each LAB species in fermented milk. By analyzing  PMA is aracasei . HoweverL. plantarum A3, L. helveticus CGMCC 1.9090, and Lactococcus lactis subsp. lactis CGMCC 1.1936 (L. lactis) were used with yogurt starter (Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus thermophilus) to produce fermented milk. Whether the phenylalanine-tRNA ligase subunit alpha (pheS) gene could be used as a new marker for the identification of LAB species was verified. The specific primers and probes for the pheS gene were also designed to determine the number of multiple viable LAB in fermented milk by PMA-qPCR. The method was validated by comparing results with those of the plate count method, qPCR, and confocal laser scanning microscopy.In this study, we developed a method that could rapidly detect and quantify different LAB specie in fermented milk. Lactobacillus delbrueckii subsp. bulgaricus KLDS1.0207, L. helveticus CGMCC 1.9090, S. thermophilus ABT-T, L. plantarum A3, L. lactis CGMCC 1.1936 were obtained from the China General Microbiological Culture Collection Center . S. thermophilus was cultivated in M17 liquid medium at 42°C and L. lactis was cultivated in M17 liquid medium at 30°C. L. delbrueckii subsp. bulgaricus, L. plantarum, and L. helveticus were cultivated in MRS liquid medium at 37°C.pheS gene of the type strain in each LAB were downloaded from the NCBI database,1 which included L. delbrueckii subsp. bulgaricus ATCC 11842, S. thermophilus ATCC 19258, L. helveticus DSM 20075, L. lactis JCM 5805, and L. plantarum ATCC 14917. The sequence alignment function of the Geneious prime software2 was used to analyze sequence similarity between the pheS gene and the 16S rRNA gene.The sequences of the 16S rRNA gene and the 2O. The PCR reaction procedure was as follows: 95°C for 5 min, followed by 30 cycles of 95°C for 30 s, 58°C for 30 s, 72°C for 1 min, and finally 72°C for 10 min, 4°C for 30 min. The PCR product was purified with an E.Z.N.A.® Gel Extraction Kit . The pUC19 plasmid was linearized by SmaI and HindIII restriction endonucleases . The plasmid linearization reaction components included 1°μg of pUC19 plasmid, 1°μL each of SmaI and HindIII endonucleases, 2°μL of 10 × quickcut green buffer, and supplemented with ddH2O to a total volume of 20°μL. The plasmid linearization procedure was as follows: 37°C for 20 min, 85°C for 20 min, and 4°C for 30 min. The PCR purification product and linearized pUC19 plasmids were ligated using the ClonExpress Ultra One Step Cloning Kit . The recombinant plasmid was sequenced using the M13 universal primer to confirm the correct ligation and then extracted with a HiPure Plasmid Plus Micro Kit  according to the manufacturer’s instructions. Plasmid concentrations were determined using a NanoDrop ND-2000 spectrophotometer .The DNA samples were extracted from 3 mL of bacterial cultures of LAB with a TIANamp Bacteria DNA kit  according to the manufacturer’s instructions. Cloning primers with homology arms were designed by CE design software  . The PCRpheS gene in five LAB were designed using the Geneious prime software . According to the reaction conditions of the 2 × Spark Universal Depolluting Probe qPCR Mix protocol , the total reaction volume was 25°μL: 12.5°μL of 2 × Spark Universal Depolluting Probe qPCR Mix, 0.5°μL of each forward and reverse primer (10°μmol/L), 0.25°uL of probe (10°μmol/L), 1°μL of DNA template, 10.25°μL of RNase free ddH2O. The cycling conditions were: 2 min at 37°C, 5 min at 95°C, followed by 45 cycles of denaturation at 95°C for 10°s and annealing at 60°C for 30°s. The results were analyzed using Light 96 software . The following equation was used to calculate the amplification efficiency during quantitative PCR:The recombinant plasmids were diluted 10-fold in RNase free ddHwhere: E is the amplification efficiency, and the slope is the slope of the standard curve.®   and sucrose  were dissolved in distilled water  at 60°C. The milk was homogenized for 20 min at 6,000 rpm using a high-speed disperser . The homogenized milk was heat treated at 95°C for 10 min and cooled to 42°C. Samples were collected from the 16-h LAB culture and centrifuged at 8,000 g for 10 min. The supernatant was discarded, and bacteria were washed three times with sterile saline. The strain combinations for the subsequent fermenting of milk were as follows: L. delbrueckii subsp. bulgaricus + S. thermophilus + L. helveticus + L. plantarum (group A), L. delbrueckii subsp. bulgaricus + S. thermophilus + L. plantarum + L. lactis (group B), L. delbrueckii subsp. bulgaricus + S. thermophilus+ L. lactis + L. helveticus (group C), L. delbrueckii subsp. bulgaricus +S. thermophilus + L. helveticus + L. plantarum + L. lactis (group D). The initial viable cell density of each LAB inoculum was 5 × 106 CFU/mL. The fermentation was done at 42°C for 7 h, and after completion, fermented milk was stored at 4°C for 16 h.Skimmed milk powderFollowing the manufacturer’s instructions, 1 mg PMA  was dissolved in 20% dimethyl sulfoxide (DMSO) to obtain a 20°mmol/L stock solution and stored at −20°C in the dark. PMA solution (2.5°μL) was added to 1 mL fermented milk to a final concentration of 50°μmol/L. The samples were mixed once every minute for 10 min in the dark, then it was diluted 100 times with sterile saline and exposed for 15 min under a 100°W LED light source, while on ice to prevent heating during the exposure. After PMA treatment, DNA was extracted from 2 mL of the diluted sample using a TIANamp Bacteria DNA kit . The DNA samples were subjected to qPCR reactions under the aforementioned conditions. The formula shown below was used to calculate the number of viable bacteria in an initial 1 mL of fermented milk:where: the slope and intercept used in the formula are the same as those used in the standard curve, the first 50 is the total volume of DNA extracted from the 2 mL diluted sample, and the second 50 is the dilution multiple.One mL of non-PMA treated fermented milk was diluted 100 times, and 2 mL dilution was aspirated for DNA extraction. DNA was used for qPCR experiments under the aforementioned conditions. Milk not inoculated with LAB was used as a negative control.L. delbrueckii subsp. bulgaricus , Co., Ltd., Shanghai, China]. SYTO9 and PI were mixed in a 1:1 ratio in a brown 1.5 mL tube with a pipette, then 3°μL of the dye mixture was added to 1 mL of the diluted sample, mixed well, and incubated in the dark for 15 min at room temperature. Subsequently, 5°μL stained sample was dropped on a glass slide, covered with a coverslip. The stained LAB was observed and photographed under an LSM880 confocal laser scanning microscope .P < 0.05 was considered statistically significant. Graphs were constructed using Origin 2018 software .Three independent replicates of each sample were used in the experiments. The experimental data were analyzed by SPSS software , and one-way analysis of variance (ANOVA) was used to compare differences between the results obtained by PMA-qPCR, qPCR, and plate count. pheS and 16S rRNA gene of the five LAB species are shown in pheS gene was much lower compared with that of the 16S rRNA gene. The 16S rRNA of the five LAB species was 1,586 bp in length, in which 1,208 bases were identical, accounting for 76.3% of the full length of the sequence. The pheS gene was 1,094 bp in length, with 439 bases of identical sequence, which accounted for 40.1% of the full length. The lower sequence similarity allowed us to design specific primers and probes for each species, so the pheS gene was selected as the target gene for the identification of LAB species.The multiple sequence alignment of the Key to the success of the PMA-qPCR experiment was the specificity of the primers. The PCR assay was performed to validate primer specificity. Each pair of primers only amplified the target fragment from the targeted strain, amplicons produced single and bright bands of the expected length (100–200 bp) and lacked spurious products .pheS recombinant plasmid as the reaction template. There was a good linear fit between Cq values and lg plasmid copy number , whereas those of S. thermophilus and L. helveticus as detected by qPCR and PMA-qPCR did not significantly differ (p > 0.05) but were both significantly higher than counts obtained by the plate count (p < 0.05). p < 0.05). The counts determined by the PMA-qPCR method were fewer than those from the qPCR method but higher than from the plate count. In group C, L. delbrueckii subsp. bulgaricus, S. thermophilus, and L. lactis counts obtained by PMA-qPCR were significantly lower than from qPCR but higher than those obtained from the plate count (p < 0.05). However, for L. helveticus, counts from PMA-qPCR and plate count were similar and both were significantly lower than those from qPCR. In L. delbrueckii subsp. bulgaricus, S. thermophilus, L. plantarum, and L. lactis all differed significantly due to different methods. The results for L. helveticus viable counts by qPCR and PMA-qPCR were not significantly different but both were higher than the plate count (p < 0.05).The results of the three methods used to detect viable bacteria in group A are shown . The viaThe LIVE/DEAD™ BacLight™ Bacterial Viability Kit utilized both SYTO 9 green-fluorescent nucleic acid and PI red-fluorescent nucleic acid dyes. The viable bacteria in the sample are stained green, while the dead bacteria are stained red. The percentages of viable bacteria in samples of groups A-D were 93.90, 97.41, 95.10, and 96.78%, respectively .pheS and RNA polymerase alpha subunit (rpoA) housekeeping genes have sufficient robustness and variability for the identification of all recognized species of the genus Enterococcus  gene in L. paracasei was selected as the target gene to design specific primers and the results showed that the viable counts of L. paracasei obtained by PMA-qPCR were significantly lower than those obtained by qPCR. Comparing the results of the PMA-qPCR and the plate count, the results of the plate count were lower than the PMA-qPCR method, probably because some of the cells in the samples were in a viable but non-culturable (VBNC) state. Bacteria in this self-protective state cannot be detected by the plate count method, because they cannot grow into visible colonies on the plate. However, PMA-qPCR can successfully detect VBNC bacteria in samples, for example, Vibrio parahaemolyticus in the VBNC state using PMA-qPCR. The bacterial staining experiments were also conducted to assess the inhibitory effect of PMA on the amplification of DNA from dead bacteria in the samples. The LIVE/DEAD™ BacLight™ Bacterial Viability Kit was used to detect viable and dead LAB in fermented foods  can be found in the article/supplementary material.ZS: conceptualization, methodology, data curation, formal analysis, writing-original draft, and writing-review and editing. XL: conceptualization and methodology. XF: methodology, software, and data curation. JX: software and validation. DP: conceptualization, project administration, funding acquisition, validation, supervision, and writing-review and editing. ZW: supervision, validation, visualization, and resources. QL: visualization and investigation. All authors contributed to the article and approved the submitted version.


Objective: In this study, we investigated the efficacy and safety of remdesivir and tocilizumab combination therapy against dexamethasone for the management of severe COVID-19 patients.Methods: This was a multicenter study. Cases were randomly chosen and divided into two groups using an odd–even ratio of 1:1 applied to the hospital registration number. Group A received remdesivir [5 mg/kg (<40 kg) or 200 mg (>40 kg) on day 1 and then 2.5 mg/kg (<40 kg) or 100 mg (>40 kg) daily] + tocilizumab [8 mg/kg up to 800 mg highest 12 h apart], and group B was the control and received dexamethasone 6 mg/day. In addition, a broad-spectrum antibiotic and other essential treatments were received by all patients. To evaluate the mortality risk, the sequential organ failure assessment (SOFA) score was calculated on day-1. Treatment outcomes were measured as time to clinical improvement; mortality rate; duration of ICU stay; total period of hospitalization; the rate of  than the control (30.77%). The time to clinical improvement , NEWS-2 on discharge , duration of ICU stay , and duration of hospitalization  were less in the treatment group. Group A had a better percentage of lung recovery on chest CT than the control . All these differences were statistically significant (p= <0.05) in a t-test. However, no significant survival benefit was found among the study groups in Kaplan–Meier survival analysis, p = 0.739.Conclusion: The remdesivir–tocilizumab combination had preferable outcomes compared to the dexamethasone therapy for the treatment of severe COVID-19 concerning mortality rate and clinical and pulmonary improvement, although it did not demonstrate a significant survival benefit.Clinical Trial Registration:https://clinicaltrials.gov, NCT04678739. The coronavirus disease 2019 (COVID-19) was first reported in China and then quickly spread all over the world.  This was2pq/d2, where z = at 95% confidence limit the value of z is 1.96; n = required sample size, p = estimated prevalence = 0.5, q = 1-p, and d = margin of error at 5% .This was a multicenter study carried out in the COVID-19 Special Care and dependency unit of the M. Abdur Rahim Medical College Hospital, Cox’s Bazar 250-bed Sadar Hospital, and Chattogram General Hospital, Bangladesh. The estimated sample size was 384. This was calculated by using the following formula: n = zBerlin definition.COVID-19 cases with positive RT-PCR for SARS-CoV-2 infection who required admission in the ICU with a National Early Warning Score-2 (NEWS-2) ≥5 were randomly chosen and included in the study. Then, they were divided into two study groups (A and B) by using an odd–even ratio of 1:1 applied to the hospital registration number. After enrollment, every case was further accessed and confirmed by the investigators or attending physician. Acute respiratory distress syndrome (ARDS) was identified according to the Patients who had a pre-existing uncontrolled chronic condition with major compromised organ function before COVID-19 , those who were immunocompromised, those with an advanced stage of carcinoma, in chemo or adjuvant therapy, pregnant patients, those with AIDS, pulmonary tuberculosis, those with contraindications/possible drug interactions to remdisivir/tocilizumab/dexamethasone, and those who were already hospitalized at the time of enrollment due to other causes were excluded from the study.Initially, 291 patients were enrolled, among which 32 were unwilling to participate and 51 patients had pre-existing comorbid conditions or were hospitalized at that time due to other reasons and so were excluded. That left 208 patients enrolled, though three patients in group A withdrew themselves from the study. Finally, data of 205 patients were evaluated .Group A (n = 101) received remdesivir [5 mg/kg (<40 kg) or 200 mg (>40 kg) on day 1 and then 2.5 mg/kg (<40 kg) or 100 mg (>40 kg) daily]+ tocilizumab [8 mg/kg up to 800 mg highest 12 h apart]; and Group B (n = 104), the control group, received dexamethasone (6 mg/day in divided dose).In addition, a broad-spectrum antibiotic (meropenem), PPI, ascorbic acid, cholecalciferol, zinc, and symptomatic treatments were received by all patients.Treatment outcomes were measured as follows: time to clinical improvement, defined as the time from randomization to NEWS-2 score  Morgan, of ≤2 asNCT04678739.The purpose of the study was properly explained to the patients, and written informed consent was obtained in every case. This study was approved by the ethical approval committee of M. Abdur Rahim Medical College, Approval No: M.A.R.M.C.D/2020/1985. Clinical trial registration: Statistical analysis was carried out by SPSS version 27 and GraphPad prism 7. Each group was analyzed to calculate the mean ± SD and mean ± SEM. Frequency was calculated. T-tests, chi-square tests, and Kaplan–Meier survival analysis were performed among the groups to evaluate the statistical significance. Required groups were calculated in percentage and compared.The total number of patients was 205 ; men 156 (76.09%) and women 49 (23.90%); group A 101  and group B 104 subjects  . The higt-test p=<0.05. Among the study groups, higher CT chest involvement , P:F ratio , SOFA day-1 score , and low PaO2  were observed during admission in group A, which differs significantly with those of the control group B, p = <0.05. –Group A showed a better recovery rate of 74.25% and a lower death rate of 25.74% than that of group B, 69.23%, and 30.76%, respectively. The mean value of the time to clinical improvement , National Early Warning Score-2 on discharge , duration of ICU stay , and the total hospitalization duration  were less in the remdesivir–tocilizumab treatment group. Among the survivors of our study population, the mean duration of ICU  stay and the duration of hospitalization  were found lower in group A. A superior improvement of pulmonary pathology in the CT chest findings was observed (difference of involvement between the admission and discharge) among the survivors  who received remdesivir–tocilizumab as a treatment. All these differences were statistically significant in a In all 60% of women in group A had a relatively shorter ICU stay (≤10 days) than that of the men. This was reversed in group B, where 66.25% of men recovered from the ICU within 10 days in comparison to 50% of women. In group A, both genders had almost similar duration of hospital stay, but men had a higher recovery period than women in group B, 4.8% ; patientp = 0.739. hazard ratio (log-rank): group A (remdesivir–tocilizumab) 1.089; 95% CI ratio 0.64–1.83; group B (control/dexamethasone) 0.918; 95% CI ratio 0.54–1.54. median survival: Group A 27; 95% CI ratio 0.46–1.29; group B 35; 95% CI ratio 0.77–2.17  in group A and the >81 years age group (4/4) in group B. The 21–30 years age group had a high mortality rate in both study groups, 60% in group A and 100% in group B. The 41–70 years age group had the highest number of hospital recoveries in group A, and the 31–70 years age group had the highest number of hospital recoveries in group B. Mortality was observed among the 51–60 years age group in group A and the 41–50 and 61–70 years age group in group B.–The early and the late age group of 11–40 years and 71->81 years, respectively, had a 100% hospital recovery within a 20 days period in group A. The same was observed among the 21–50 years and >81 years age groups in group B. Hospital recovery time was prolonged to >21 days among the 41–70 years age group in group A and 51–80 years age group in group B. Up to 80% hospital recovery was observed among all the age groups of group A within 10 days. On the other hand, most of the patients in group B were discharged from the hospital within 11–20 days. Most of the patients in all ages of group A recovered from the ICU within 10 days. On the contrary, the highest recoveries were seen within 11–20 days in group B. Group B had higher mortality (87.5%) than group A (61.5%) within 10 days –It was found that 58.7%61) patients in group A and 44.2%44) patients in group B had comorbid conditions. They were hypertension (HTN), diabetes mellitus (DM), ischemic heart disease (IHD), chronic obstructive pulmonary airway disease (COPD), rheumatoid arthritis (RA), benign prostatic hyperplasia (BPH), osteoarthritis, hypothyroid, ischemic stroke, heart failure, chronic kidney disease, bronchial asthma, inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), hepatitis B, migraine, and early carcinoma. Nearly similar numbers of comorbidities were noted in both study groups; 15.8%  and 21.1p = 0.739. However, the remdesivir–tocilizumab intervention group had a relatively lower mortality rate of 25.74% than that of the control group, which was 30.76%, in our study. There was significant reduction in the time to clinical improvement, NEWS-2 score on discharge, CT improvement (%), and duration of ICU and hospital stays in the remdesivir–tocilizumab group, p=<0.05 (In this research, a multicenter study of 205 selected patients after exclusion was carried out in the COVID-19 special care and dependency unit of multiple health centers/hospitals in Bangladesh. Combination therapy of remdesivir with tocilizumab was carried out in the intervention group and was compared with that in the control group which was treated with dexamethasone. To evaluate equal treatment outcomes among the study groups, COVID-19 cases in ventilator support and with severe comorbidities were excluded. The post-intervention outcome was measured as described in the methodology. Kaplan-Meier survival analysis was performed, and no significant advantage of the remdesivir–tocilizumab treatment was observed against dexamethasone therapy,  p=<0.05 . This re p=<0.05 . TherefoCurrently, many trials are ongoing for the management of COVID-19, and several trials have been completed recently.  Among thOur study has limitations: the small sample size; exclusion of COVID-19 cases that required ventilator support, and COVID-19 patients with severe comorbidities are a concern. In addition, analysis depending on the severity of COVID-19–ARDS was not performed. But to the best of our effort, we selected severe COVID-19–ARDS cases devoid of serious or uncontrolled comorbidity to ensure proper comparison and outcome among the study groups without influence.According to this study, the remdesivir–tocilizumab combination had preferable outcomes compared to those of the dexamethasone therapy for the treatment of severe COVID-19 concerning the lower mortality rate, better clinical and pulmonary improvement, reduced duration of the ICU stay, and decreased hospitalization period, although the survival benefit of this combination therapy was not significant against dexamethasone alone.


High-resolution mass spectrometry has been increasingly used in this context for the last years, because of its sensitivity and reliability. Here, we present the development and validation of a screening method that uses liquid chromatography coupled with a high-resolution mass spectrometer. (2) Methods: This method required only 100 µL of whole blood or plasma sample. Pretreatment consisted of a rapid and simple deproteinisation with methanol/acetonitrile and zinc sulphate. This new assay was validated according to international guidelines. (3) Results: To perform the method validation, 53 compounds were selected. The selection criteria were as follows: various chemical structures and therapeutic families (>15), large m/z distribution, positive or negative ionisation mode, and various elution times. The assays showed high selectivity and specificity, with optimal process efficiency. The identification limits, determined using predefined criteria, were established at sub-therapeutic or therapeutic concentrations. Applicability was evaluated using spiked plasma controls and external quality controls. (4) Conclusions: The new method was then successfully applied to routine clinical and forensic samples. Immunoassays are often used as a primary test. Although these methods are rapid, they are limited in the number of targeted xenobiotics and most of them propose only a drug class diagnosis. Due to lack of sensitivity and specificity, these assays must be confirmed with more appropriate technologies such as mass spectrometry ,2. For mon times ,36,37,38® HSS C18 1.8 μm 2.1 × 150 mm , and Accucore Phenyl Hexyl UPLC 100 × 2.1 mm, 2.6 µm  columns were tested using the previous solution. The chosen column was selected on the basis of the optimal shape of chromatographic peaks. The selected column was the Accucore Phenyl Hexyl UPLC column, as was the case for Helfer et al. [v/v) (mobile phase A) with or without ammonium acetate and acetonitrile + formic acid 0.1% (v/v) (mobile phase B). Mobile phases with ammonium acetate provided a better signal strength than water + formic acid 0.1% (V/V). After these choices, the gradient was optimised.Different chromatographic parameters were assayed and optimised to ensure good elution of the compounds with correct sensitivity. The first step of optimisation was the mass spectrometer parameters. A mixture of a pure solution of the 53 selected compounds in methanol was infused and then injected to optimise ionisation and mass spectrometer parameters. The second optimisation step was the chromatographic conditions with the choice of column, mobile phases, and elution gradient. Acquity BEH HILIC , Acquity HSS T3 , Acquity UPLCr et al. ,31. The v/v) was performed. After vortex mixing and centrifugation, the supernatant was evaporated under nitrogen flow at 40 °C. The dry extract was then reconstituted with 200 μL of water/acetonitrile  and formic acid 0.1% (v/v). Analysis of this sample failed to detect some polar compounds, particularly metformin and baclofen. Different protein precipitation techniques were tested using acetonitrile or methanol [Sample pretreatment was optimised by testing different liquid–liquid extraction procedures. First, an extraction using 100 µL of the sample, 1 mL of diethyl ether, and 20 µL of carbonate buffer ; rodenticides ; and antidiabetic such as sitagliptine, vildagliptine aztreonam, cefepime, cefotaxime, piperacillin, hydroxyalprazolam, isavuconazole, nordosulepine, norquetiapine, norsertraline, sulpiride, vortioxetine, and oxomemazine. For these compounds, compound name, formula, polarity, high-resolution mass, retention time, isotopic distribution, and HRMS spectrum were recorded to the library. Finally, the library consisted of 1489 compounds.−1 as well as a blank plasma sample were prepared and analysed (n = 6). Different data processing parameters were studied in order to detect all the compounds in the spiked plasma samples and none in the blank sample. Concerning the identification criteria, the first two major criteria tested were the presence of the high-resolution precursor ion mass and the isotopic pattern. The main limitation of this setting was the inability to discriminate isomeric compounds, such as 6-monoacetylmorphine and naloxone, morphine and norcodeine, O-demethylvenlafaxine, and tramadol. To overcome this problem, a DDA acquisition was set up. In DDA mode, in the first step, the mass spectrometer selected the most intense ions; then, in a second step, they were fragmented and analysed. Finally, the major criteria selected were the presence of high-resolution mass precursor ions, the presence of fragment ions, and the match with the full spectrum of the library spectrum. The minor criteria were retention time and isotopic pattern.Different plasma samples spiked with the 53 selected compounds at the concentration of 200 µg LFinally, we proposed a method with an analysis time of 15.25 min. Regarding the other reported method, the analysis time was similar to the procedure of Helfer et al. with precipitation only (17 min). The analysis time was longer with the Turboflow system .As in other reported methods, this present study was validated in accordance with the reference guidelines ,37,38,401.SelectivityAnalysis of 10 blank plasma samples from individual donors did not identify any interfering compound on the chromatograms (data not shown).2.SpecificityOur method was able to discriminate all pairs of compounds with the same molecular weight. For each analyte, retention times, accurate mass precursor, and mass tolerance range at 5 ppm are listed below: amitriptyline ), venlafaxine ), alprazolam ), and warfarin ), prazepam ), and citalopram ). For isomeric compounds, 3.Evaluating Interferences from Stable-Isotope Internal Standards and carry-overDuring the interference evaluation, no analyte of interest was detected (data not shown). Thus, for this analytical method, no interference from stable-isotope IS nor contamination was highlighted.4.Matrix effectResults for the matrix effect (ME) are presented in The recovery (RE) and process efficiency (PE) are described in −1 for a therapeutic concentration range of 3 to 15 µg L−1), buprenorphine (LOI 1 µg L−1 for a therapeutic concentration range of 0.5 to 10 µg L−1), metformin (LOI 100 µg L−1 for a therapeutic concentration range of 100 to 1300 µg L−1), and morphine (LOI 10 µg L−1 for a therapeutic concentration range of 10 to 100 µg L−1). For these compounds, the present method will be able to identify these compounds in therapeutic or toxic use. These results demonstrated the relevance of our method in the investigation of intoxication causes.As described in literature, the identification limit (LOI) was determined as the lowest concentration for which the three major criteria  are present ,30. LOIsThe identification limits reported in the different published studies have been displayed in n = 6) was performed. For each compound, the mean and relative standard deviation of the library score were calculated  and a blood pressure of 60/30. Routine toxicology screening detected acetaminophen, alimemazine, amlodipine, atenolol, caffeine, fluoxetine, furosemide, ketamine, midazolam and alpha-hydroxymidazolam, zopiclone, and demethylzopiclone. −1, toxic concentrations from 80 µg L−1 [−1, toxic concentrations from 2000–3000 µg L−1 [−1, toxic concentrations from 150 µg L−1 [In this context, quantifications of drugs inducing hemodynamic instability were performed. Quantification analyses revealed a major polyintoxication to amlodipine (701 µg L0 µg L−1 ), atenol0 µg L−1 ), and zo0 µg L−1 ). The ot−1, toxic from 300 µg L−1 and inducing coma from 1000 µg L−1 [−1, therapeutic concentrations: 50–110 µg L−1 [−1, toxic from 500 µg L−1 [−1, toxic from 1000 µg L−1 [−1 . She had a previous history of depression, a self-inflicted phlebotomy, and an attempt of self-hanging. The biochemical analysis revealed hypokalemia, hypoglycemia, and elevated transaminases. The toxicological screening revealed the presence of numerous drugs: acetaminophen, alprazolam, bromazepam, caffeine, ceftriaxone, codeine, flecainide, fluoxetine and norfluoxetine, laudanosine, milnacipran, morphine and morphine-3-glucuronide, noscapine, omeprazole, tramadol, tianeptine, verapamil and norverapamil, and zolpidem. Toxic concentrations were quantified for bromazepam (3460 µg L0 µg L−1 ), milnac0 µg L−1 ), zolpid0 µg L−1 ), and tr0 µg L−1 ). Tianep0 µg L−1 ), explai−1 .−1, lethal from 3000–5000 µg L−1 [−1, toxic from 100 µg L−1 [−1, therapeutic concentrations: 10–100 µg L−1 [−1, 29 µg L−1, and 85 µg L−1, respectively, therapeutic concentrations: 10–40 mg L−1, 2–10 µg L−1, and 10–50 µg L−1, respectively [−1, therapeutic concentrations: 20–800 µg L−1 [Case 4: Another case of alleged voluntary drug poisoning was reported concerning a woman aged 71 years old. She was admitted to the intensive care unit presenting a coma associated with vasoplegia and cardiogenic shock. The present routine toxicological screening allowed for the detection of acetaminophen, atropine, bisoprolol, cetirizine, dobutamine, hydroxyzine, levetiracetam, lidocaine, lormetazepam, nordiazepam, oxazepam and zopiclone, and desmethylzopiclone. In this context, quantifications of numerous drugs were performed. A lethal concentration was quantified for oxazepam (4750 µg L0 µg L−1 ). A toxi0 µg L−1 ). Bisopr0 µg L−1 ). Levetiectively ). Nordia0 µg L−1 ).−1 by a liquid chromatography method coupled with ultraviolet spectrophotometry. At this concentration, thiopental can induce coma and could be fatal [Case 5: A 57-year-old man was found unconscious at home with a Glasgow score of 3/15. The environment suggested an autolysis attempt by self-medication intoxication. Blood pressure was 182/144 mmHg, heart rate was 65 bpm, and respiratory rate was 18 breaths per minute. Oxygen saturation was 100%, and body temperature was 35 °C. The patient was transferred to the medical intensive care unit for further management. Routine toxicological screening was performed, revealing the use of alprazolam; cetirizine, citalopram and its metabolites, domperidone, hydroxyzine, ondansetron, morphine and codeine, salbutamol, thiopental, and zolpidem. Thiopental was suspected as being responsible for the condition of the patient. Thiopental was then quantified at the concentration of 21.8 mg Lbe fatal . Other d7-Amino-clonazepam, amiodarone, amlodipine, atenolol, atropine, bisoprolol, bromazepam, bupivacaine, citalopram, chlordiazepoxide, clobazam, clonazepam, clozapine, diazepam, estazolam, flupentixol, haloperidol, hydroxyzine, lamotrigine, lidocaine, loxapine, mianserin, metformin, midazolam, nordiazepam, mitrazepam, o-demethylvenlafaxine, oxazepam, prazepam, propranolol, risperidone, sertraline, tramadol, verapamil, warfarin, zolpidem, and zopiclone were purchased from Sigma-Aldrich . Alprazolam, amitryptiline, aripiprazole, baclofen, clotiazepam, desmethylzopiclone, fentanyl, paroxetine, and venlafaxine were purchased from Lipomed . Glibenclamide, levetiracetam, loprazolam, lorazepam, lormetazepam, norclobazam, oxazepam, and temazepam were purchased from LGC standard . Acetazolamide, cyamemazine, nefopam, and tiapride were obtained from Carbosynth . Buprenorphine, cocaine, ketamine, MDA, methadone, morphine, morphine-D3, and amphetamine-D5 were purchased from Cerilliant . Metformin-D6 and trazodone-D6 were obtained from Cluzeau info labo . Acetonitrile, methanol, formic acid, and water, all LC–MS hypergrade, were purchased from Biosolve . Zinc sulphate and ammonium formate were purchased from Sigma-Aldrich . Whole blood and plasma from healthy donors were purchased from the French Blood Bank .−1 stock solutions in acetonitrile. A working internal standard solution was then prepared monthly, containing 1 mg L−1 lopinavir-D8, metformin-D6, tramadol-D6, trazodone-D6, and 0.1 mg L−1 amphetamine-D5 and morphine-D3.The internal standards were prepared as 1 g L−1 stock solutions in methanol and stored at +4 °C. Working solutions were prepared with appropriate serial cascade dilutions in methanol.Other compounds were prepared as 1 g L−1 for amlodipine and haloperidol; 100 µg L−1 for atenolol, bupivacaine, cocaine, cyamemazine, glibenclamide, morphine, prazepam, and venlafaxine; 500 µg L−1 for metformine; and 1000 µg L−1 for furosemide, ketamine, levetiracetam, and warfarin.Quality controls were prepared in plasma after appropriate dilution of the stock solutions to obtain the following final concentrations: 10 µg Lv/v) (mobile phase A) and 2 mM ammonium formate, acetonitrile, and formic acid 0.1% (v/v) (mobile phase B). A programmed mobile-phase gradient was used at a flow rate of 0.5 mL.min−1. The gradient was programmed as follows: 0–0.5 min 99% A, 0.5–10 min 99% to 1% A, 10–11.5 min hold 1% A, and 11.5–15.3 min hold 99% A. The time of analysis and acquisition was 15.3 min including equilibration.An ultra-performance liquid chromatographic system with an Ultimate 3000 high-pressure pump  coupled with Orbitrap QExactive mass spectrometer  was used for the development and the validation of the method. Chromatographic separation was performed using an Accucore Phenyl Hexyl UPLC column , maintained at 40°C. Mobile phases consisted in 2 mM ammonium formate, water, and formic acid 0.1%  resolution power of 35,000 FWHM  for m/z = 200; (3) automatic gain control (AGC) target of 1.106 a.u., and (4) maximum injection time (IT) of 120 ms. For data-dependent acquisition mode (DDA), an inclusion list containing 1513 compounds was added. High-collisional dissociation (HCD) was performed on the three most intense ions selected from the full scan. Moreover, a dynamic exclusion for 3 s was planned for the most intense ions. DDA settings were as follows: (1) resolution power 17,500 FWHM for m/z = 200, (2), AGC target of 1E5 a.u., (3) maximum IT of 50 ms, (4) isolation window of m/z 2.0, and (5) collision energy stepped at 17.5, 35 and 52.5 eV.Heated electro-spray ionisation in positive/negative switching ionisation mode was performed with the following settings: (1) sheath gas 45 arbitrary units (a.u.), (2) auxiliary gas 15 a.u., (3) sweep gas flow rate 1 a.u., (4) spray voltage 3.50 kV, (5) ion transfer capillary temperature 300 °C, (6) S-lens RF level 70 V, and (7) heater temperature 350 °C. Mass spectrometry was performed using full-scan data and a subsequent DDA. Runtime acquisition was 0 to 15.25 min. The settings for full scan data acquisition were as follows: (1) scan range of ®, ThermoScientific, San Jose, CA, USA) according to the manufacturer’s recommendations.Mass calibration was performed once a week in positive and negative mode using an external calibration solution (Piercem/z) were detected with a minimal ratio signal-to-noise threshold of 10 a.u. and a mass tolerance of 5 parts per million (ppm); retention time, fragment ions, isotopic pattern, and library search were selected for confirmation as follows:-for retention time, the option “ignore if not defined” was selected, and window override was 60 s;-for isotopic pattern, the fit threshold was 70 a.u., with a mass tolerance of 5 ppm and an intensity deviation tolerance of 30%;-for fragment ions, the option “ignore if not defined” was selected, the minimum number of fragments was one, the intensity threshold was 5000 a.u., the product mass tolerance was 10 mmu, and the MS order was MS²;-for general library NIST settings, MS order was MS² and isolation width was used; for the NIST setting, search type selected was MS/MS, and options “ignore precursor”, “use all peak matching”, “reverse search”, and presearch “off” were selected; a probability threshold of 10 a.u, score threshold of 80 a.u., search index (SI) threshold of 500 a.u., and reverse search index (RSI) threshold of 600 a.u. were selected; precursor and product masses tolerances were 5 ppm and 10 ppm, respectively; library score was selected as a passing value type with a passing value of 20 a.u.TraceFinder Forensic 5.1 was used for LC–MS control, library management, acquisition, and processing. Precursor peaks . An update of the inclusion list with the new compounds was performed in our laboratory. For each compound, the library mentioned the compound name, formula, polarity, high-resolution mass, retention time, isotopic distribution, and HRMS spectrum.v/v) and 20 µL of the working internal standard solution were added to 100 µL of whole blood or plasma samples. Deproteinisation with 100 µL methanol followed by 200 µL acetonitrile was conducted. After vortexing for 30 s and centrifugation at 10,000× g for 5 min, the organic phase was evaporated under nitrogen flow at 40 °C. The dry extract was then recovered by 200 μL of water/acetonitrile (50% v/v) containing 0.1% formic acid (v/v). For chromatographic separation, 20 µL was injected. A total of 100 µL of zinc sulphate 5% .The isotopically labelled compounds used as IS may contain the unlabelled analytes as impurities. Interference from stable-isotope IS was evaluated by analysing a blank matrix sample spiked with the internal standards. The corresponding analytes of interest should not be detected. For carry-over, six blank plasma samples and six whole blood samples were spiked with the 53 selected analytes at a final concentration of 10,000 µg L4.Matrix effectn = 6), and the peak areas of the neat standard (53 compounds) were compared to the matrix samples spiked with neat standards after extraction. This assay was carried out at two concentration levels (50 and 500 µg L−1). The matrix effect factor was calculated by comparing the area under the peak of the spiked matrix after extraction and the area under the peak of the neat solution at the same concentration.The matrix effect defined as ionisation suppression or enhancement was evaluated according to the FDA (U.S. Food and Drug Administration)  and EMA Recovery (RE) and process efficiency (PE) were evaluated according to the FDA  and EMA Whole blood and plasma samples were spiked with the 179 selected compounds at different concentrations according subtherapeutic, therapeutic, and toxic concentrations, providing a large scale of concentrations ,42 for eWithin-run and a between-run analyses were assessed by analysing 6 samples per level. The limit of identification was determined as the lowest concentration at which the substances were identified in all replicates on the basis of the three following criteria : (1) then = 6) were performed. For mass spectra reproducibility evaluation, library score of the TraceFinder search index values was used as quality match factors. For each compound, mean and relative standard deviation of library score were calculated.The reproducibility of the MS/MS spectra was investigated by analysing the spiked plasma at three levels of concentration . BetweenGraphPad Prism 5.0  was used for statistical analysis. Data are described as mean and standard deviation.In this work, we described the development and validation of a large-scale HRMS toxicology screening on both whole blood and plasma. The method acquisition used was a full-scan DDA with an inclusion list of more than 1000 compounds. Sample work-up consisted of a straightforward deproteinisation with only 100 µL of plasma or whole blood. Validation was carried out according to international guidelines and the current literature. This new test is now successfully applied to routine clinical and forensic toxicology analyses. In the coming future, further development and validation of this assay will be performed in urine and other matrices. In addition, the data acquisition can be adapted to allow for the detection of new unknown compounds such as synthetic cannabinoids by using Compound Discoverer .