In some patients with uncontrolled and controlled (shown for the first time) AH the concentration of AHD was below the limit of quantitative determination. (4) Conclusions. The obtained results indicate that the pharmacokinetics of AHD, apparently, does not play a significant role in the development of ineffectiveness of the ongoing therapy for AH. Therapeutic drug monitoring can be used to test adherence to the treatment.(1) Background: This study was planned to assess the concentration of antihypertensive drugs (AHD) in the blood serum in patients with controlled and uncontrolled arterial hypertension (AH). (2) Methods: We assessed 46 patients with AH. Based on the results of 24 h blood pressure monitoring (ABPM), the patients were randomized into two groups. The first group consisted of the patients with controlled AH; the second group consisted of the patients with uncontrolled AH. Venous blood was taken in both groups of patients in the morning before and 2 h after taking drugs to assess the concentration of lisinopril, amlodipine, valsartan, and indapamide. (3) Results. The first group included 27 patients, and the second group 19 patients. In patients with uncontrolled AH, the median concentrations of lisinopril, indapamide, amlodipine, and valsartan before and after taking the drugs did not differ from patients who reached the target BP values. ( Arterial hypertension (AH) is one of the most common diseases. According to the K.T. Mills’ systematic analysis, in 2010 the prevalence of AH in the world was 31.1% [In recent years despite the evidence-based approach to the treatment of AH, including lifestyle modification and 5 main groups of antihypertensive drugs (AHD), as well as starting combination therapy, it is not always possible to achieve target blood pressure (BP) in all cases.Resistant hypertension (RHTN) is defined as BP that is uncontrolled despite using ≥3 medications of different classes, commonly a long-acting calcium channel blocker, a blocker of the renin–angiotensin system  and a diuretic. All agents should be administered at maximum or maximally tolerated doses and at the appropriate dosing frequency [RHTN is generally attributable to persistent fluid retention , secondaThe refractory hypertension (RfHTN) phenotype refers to patients whose BP remains uncontrolled despite the use of maximal or near-maximal antihypertensive therapy. RfHTN is defined as BP that is uncontrolled despite using ≥5 different antihypertensive agents, including a long-acting thiazide diuretic  and mineralocorticoid receptor antagonists (spironolactone or eplerenone) .Most uncontrolled AH is not truly resistant to medical treatment but results from factors that lead to or maintain elevated BP readings independent of prescribed pharmacological treatment, termed pseudoresistance. The most common causes of pseudo-RHTN are inaccurate BP measurement, resulting in falsely elevated readings, the white coat effect, where in-office BP is persistently elevated but out-of-office BP is at goal, undertreatment, including clinical inertia, and medication nonadherence. Identification of factors that contribute to pseudoresistance is important in preventing costly and potentially risky diagnostic evaluations of patients who are not truly resistant to treatment and avoiding inappropriate intensification of treatment, which can be costly and potentially increases the risk of adverse events . The prevalence of RHTN ranges from 5 to 30% based on the definition used by relevant studies . Howeverp = 0.006). In patients with digoxin concentration between 0.5 and 0.8 ng/mL the mortality rate was 6.3% , 2.1–10.5%) lower compared to patients receiving placebo [In cardiology, TDM has been developed for single drugs. For example, it has been shown that high blood serum concentration of digoxin in patients with congestive heart failure was associated with an increased overall mortality rate from all causes , the study period covered February 2022–February 2023.Age over 18;Signed informed consent form;An established diagnosis of AH based on the Clinical Guidelines “Arterial hypertension in adults”, approved by the Scientific and Practical Council of the Ministry of Health of the Russian Federation, 2020 ;Mandatory patient compliance with recommendations for lifestyle modification in accordance with the Clinical Guidelines “Arterial hypertension in adults”, approved by the Scientific and Practical Council of the Ministry of Health of the Russian Federation, 2020 .Regular administration of any two AHD  in combination with indapamide for a month, possibly in fixed combinations, in stable dosages;Fertile female patients must use proper methods of contraception throughout the study period.Inclusion Criteria:Patient’s connection with the organization or conducting of the study;Pregnancy.Exclusion Criteria:We enrolled in our study 46 patients with AH. All patients underwent a routine examination, which included: anthropometry, assessment of BP, heart rate (HR), general and biochemical blood tests, urinalysis, and echocardiography.In addition, all patients underwent 24-h blood pressure monitoring (ABPM). According to the results of ABPM, we formed two groups of patients: 1. Controlled AH. Patients, who, according to ABPM, met the following criteria: mean daily systolic blood pressure (SBP) < 135 mm Hg, mean daily diastolic blood pressure (DBP) < 85 mm Hg, the average night SBP < 120 mm Hg, the average nighttime DBP < 80 mm Hg. 2. Uncontrolled AH. Patients, who, according to ABPM, had at least one of the following markers of poor BP control: mean daily SBP ≥ 135 mm Hg, mean daily DBP ≥ 85 mm Hg, the average night SBP ≥ 120 mm Hg, the average nighttime DBP ≥ 80 mm Hg. After randomization, in the morning before the next AHD administration and 2 h after AHD administration, venous blood samples were collected to assess the concentration of lisinopril, amlodipine, valsartan, and indapamide by highly efficient liquid chromatography with tandem mass spectrometric (HPLC MS/MS) detection, using chromato-graph Ultimate 3000 and mass-spectrometer TSQ Fortis (ThermoFisher).g  for 10 min at 40 °C. 600 µL of the supernatant was pipetted into labeled vials and placed in the autosampler for further analysis.For sample preparation, we have used methanol containing fexofenadine at a concentration of 1 ng/mL as an internal standard, which was added to serum samples in a ratio of 3:1 (600 µL of methanol with fexofenadine and 200 µL of serum). The resulting mixture had been shaken on a shaker for 1 min, then centrifuged at 19,000× The volume of the injected sample was 5 μL. Separation was performed on a UCT Selectra C18 4.6 mm × 100 mm, 3 μm, 100A column with a Selectra C18 Guard Cartridges SLC-18GDC46-3UM at a temperature of 35 °C, in a gradient elution mode in a ratio of 0.1% formic acid solution/acetonitrile: 0 min-80%/20%, 0.1 min-60%/40%, 6 min-15%/85%, 10 min-15%/95%, 10 min-80%/20% with a flow rate of 400 µL/min and positive detection electrospray ionization mode, electrospray voltage 3500 V, sheath gas 50 arb, auxiliary gas 10 arb, sweet gas 1 arb, vaporizer temperature 350 °C, ion transport tube 300 °C, using multiple reaction monitoring (MRM) modes with speed argon supply 2 mTorr. All the parameters for detection for each target compound were presented in The laboratory method was validated for the following parameters: selectivity, calibration curve, accuracy, precision, limit of quantitation, sample transfer, sample stability, and matrix effect .2 ≥ 0.99), ranging from 1 to 1000 ng/mL  levels  of each analyte, and five replicates were analyzed at each concentration on each of three different days. Data regarding the intra- and inter-day precision of the analysis of samples are shown in Extraction recovery and matrix effect were determined for (QC) samples from the serum at two concentrations (3 and 1000 ng/mL) . These rIn the present study, we assessed the analyte’s stability in terms of long-term storage (90 days), autosampler (24 h), and freeze/thaw conditions using two different QC samples (3 and 1000 ng/mL); the precision of the quantification method for the analyte was determined to be within 15%. The results obtained were processed with the use of StatSoft Statistica 13.0  and Microsoft Excel for MAC ver. 16.24 (ID 02984-001-000001). The distribution of the obtained data was evaluated by the Shapiro-Wilk test. If the value had normal distribution, we used the Student’s T test to assess the statistical significance of differences. In other cases, the Mann°Whitney test was used. Frequency values were compared with the Chi-square test.The results obtained are presented in tables and graphs as the mean and standard deviation (M (SD)) in data with a normal data distribution or median, minimum and maximum values, upper and lower quartiles  or Me ) for non-normal distribution.Among 46 patients enrolled in our study, there were 19 men (41%). The first group of patients with controlled AH included 27 patients, and the second group-19 patients.Demographic data are presented in Among the 46 patients included in the study, there were 19 men (41%). The first group of patients with controlled AH included 27 patients, and the second group 19 patients.It is noteworthy that among the patients with uncontrolled AH, there were statistically significantly more men. At the same time, when we compared the groups by age, and BMI, no statistically significant differences were found.Information about comorbidities is presented in The analysis of the laboratory data also did not reveal any statistically significant differences in fasting glucose levels, kidney function, lipid metabolism, electrolytes, and liver damage .The data of 24 h BP monitoring are presented in Based on the study inclusion criteria, BP values were statistically significantly higher in the group of uncontrolled AH, while there were no statistically significant differences in AHD doses .While studying the concentrations of AHD in the blood serum, the following results were obtained .In patients with uncontrolled AH, the median steady-state concentrations (before taking the drug) of indapamide, amlodipine, and valsartan did not differ statistically significantly from those of patients who reached the target BP.p = 0.06). After recalculating these indicators for the dose and body weight of the patients, no statistically significant differences in the concentration of AHD were obtained .Two hours after taking all the studied AHD, there were no significant differences in the medians of their concentrations between the groups of controlled and uncontrolled AH , the following results were obtained . The increase in the concentrations of lisinopril, valsartan and amlodipine did not differ statistically significant between the compared groups.p = 0.058).At the same time, the delta of change of indapamide concentration in the group of patients with Uncontrolled AH exceeded the delta of change in the group of Controlled AH at the borderline of statistical significance . In the group of patients with uncontrolled AH, in some cases, AHD was also not found in the blood  .p = 0.318). These results indicate that, regardless of BP control, at least 30% of patients with AH, taking AHD , do not achieve therapeutic concentrations, which is a potential opportunity to increase the effectiveness of the therapy.The difference between the proportion of the patients in the group with controlled and uncontrolled AH, in whose blood at least one AHD was not found, was not statistically significant . These results are consistent with similar results for assessing adherence to treatment in patients with arterial hypertension.A recent study assessed medication adherence in patients with RfHTN by measuring AHD and their metabolites in 24 h urine specimens using high-performance liquid chromatography-tandem mass spectrometry. Of 40 patients with RfHTN, only 16 (40%) had complete adherence with all prescribed medications, 18 (45%) had partial adherence, and 6 (15%) were completely non-adherent with all prescribed medications. Overall, 21 (52.5%) were adherent with 5 or more medications, including chlorthalidone and a mineralocorticoid receptor antagonist . This stOn the other hand, a decrease in AHD concentrations may be a consequence of a change in their pharmacokinetics: a decrease in absorption in the gastrointestinal tract, an increase in biotransformation, and an acceleration of excretion, which, in turn, may be due to the peculiarities of the genetic status of the patients  or transporter proteins ), as well as drug-drug interactions .Another factor that could affect the rate of absorption of antihypertensive drugs is the quality of the dosage forms .In our study, all patients took the same drugs (only generics). Therefore, this factor did not affect the obtained results.In summary, this study confirmed, that blood serum concentrations of AHD  do not differ in patients with controlled and uncontrolled AH. The obtained results indicated that the concentration of AHD does not play a significant role in the ineffectiveness of the ongoing therapy of AH.In some patients with uncontrolled and controlled (shown for the first time) AH the concentration of AHD was below the limit of quantitative determination.Thus, the approach to the treatment and diagnosis of patients with uncontrolled arterial hypertension should be comprehensive, including an assessment of the physiological, genetic, and biochemical factors of patients, and therapeutic drug monitoring can be used to assess adherence to the treatment.


This study investigatesthe use of environmentally friendly citricacid as the main player in the process, rather than as an additive,to remove impurities from amoxicillin trihydrate (AMCT) crystals,aiming to optimize their purity and yield. By manipulating the concentrationof citric acid, mixing speed, crystallization time, and pH, the researchersconducted experiments using a full factorial design. The dissolutionstage was analyzed in both batch and continuous crystallization processes,emphasizing the significance of citric acid in enhancing crystallization.HPLC analyses were performed on the resulting crystals, and the datawere analyzed using the Multi-Vari Chart program. The findings demonstratedthat higher citric acid concentrations positively affected the yield,while factors such as crystallization time, mixing speed, and pH alsocontributed to the increased yield. The crystals obtained exhibiteddesirable dimensions sought after in the pharmaceutical industry,eliminating the need for additional purification steps. This studyshowcased the potential of citric acid in AMCT crystallization, offeringadvantages in product design, purification, and synthesis. The optimizedconditions included a citric acid concentration of 2.0 M, mixing speedof 1000 rpm, crystallization time of 120 min, and pH of 5.5. Notably,the developed process proved to be environmentally friendly by avoidingthe use of harmful chemicals, serving as a green alternative for crystallizationprocesses, and producing purer AMCT products. Overall, this researchcontributes to the existing literature by highlighting the efficacyof citric acid in impurity removal and the optimization of AMCT crystalpurity and yield. Downstream processessuch as filtration, drying, and grinding, integral to the crystallizationprocess, significantly influence the size, shape, and structure ofthe obtained crystals, along with the physical and chemical propertiesof the crystal structure, including the dissolution rate and solubility.3 The crystal structure emerges through nucleation and crystal growth.During the nucleation stage, molecules within the solution aggregateand form nuclei. Subsequently, in the crystal growth phase, thesenuclei develop into macroscopic crystals, attaining a specific sizewithin a defined time frame. The kinetics and mechanism of crystallizationare governed by solubility, supersaturation, diffusion, temperature,and the presence of impurities.4Crystallization is a separationand purification technique involvinga phase change during which a crystalline product is obtained froma solution. The crystallization technique is frequently employed toproduce various products in the food and pharmaceutical industries.Particularly within the pharmaceutical sector, nearly 90% of all activepharmaceutical ingredients (APIs) consist of crystalline organic molecules.9 In their investigation, Feng et al. demonstrated that an increasein the concentration of degradation products leads to a decrease inAMCT nucleation.6 In another study, Ghassempuret al. detailed the integration of impurities into the AMCT crystallattice and its consequent impact on the process. They establisheda clear correlation between rising pH levels and declining 4-hydroxyphenylglycine(4-HPG) concentration.10 Furthermore, asper separate research, the addition of l-phenylalanine asan impurity at 0.01% (by weight) to l-alanine resulted ina decrease of over half in l-alanine’s growth rate.11 The deleterious impact of such impurities oncrystal growth can be attributed to their adsorption onto crystalsurfaces. Due to the similarity in the molecular structure, impuritiescan adhere to growth regions, forming structures that hinder the interactionof other crystal molecules.12 These differingimpurities significantly decelerate the crystal’s growth rate.17The literature predominantly focuses on the adverse impact of introducingimpurities structurally similar to the primary molecule on crystalgrowth. The separation of AMCT from its own degradation products,in particular, has garnered emphasis.18 In their research,Kitamura et al. demonstrated the impact of l-phenylalanineon l-glutamic acid growth. They stated that the (110), (111),and (011) surfaces of l-glutamic acid readily adsorb l-phenylalanine through hydrogen bonding.19 Similarly, Prasad et al. discussed the presence of phenacetin,which is integrated into the paracetamol crystal lattice. Their studyrevealed that phenacetin introduced defects in the lattice duringcrystallization, consequently slowing down the growth rate of paracetamolcrystals.20 Dowling et al. observed thatthe additives malonic acid and aspartic acid significantly acceleratedthe crystal growth rate in their crystallization processes.21In their study on anthranilic acid crystallization, Simoneet al.noted that the addition of benzoic acid during crystallization affectsthe crystal structure.2SO4), and glacial acetic acid (CH3COOH) are the most commonly used acids in the purificationof AMCT.24 However, using these acids raises several environmental, health,and safety concerns due to their extreme corrosiveness and respiratoryhazards.25 In this study, the utilizationof citric acid, a natural organic acid, was explored as an alternativeto these acids for the purification of amoxicillin. The goal was tomitigate the harmful effects associated with the use of these acids.Similar to other β-lactam antibioticdrugs, the crystallizationof amoxicillin trihydrate (AMCT) also plays a crucial role in controllingthe crystalline form, shape, and size. Affordable, widely available,and highly efficient acids such as hydrochloric acid (HCl), sulfuricacid , signifying its approvalas a food additive for use “in quantum satis” withinthe European Union.26 Citric acid is extensivelyemployed in the food and beverage industries as an acidifier. It enhancesthe flavors and aromas of fruit juices, ice cream, and marmaladesor serves to preserve food due to its antioxidant properties. In thepharmaceutical industry, it finds applications as a blood preservative,an effervescent agent, a pH corrector, an antioxidant to preservevitamins, and a source of body iron in the form of iron citrate tablets.Additionally, it is used in ointments and cosmetic preparations. Inthe textile sector, citric acid acts as a foaming agent, aiding inthe softening and treatment of fabrics. Within metallurgy, its exceptionalchelating ability with various metal ions is harnessed. Metal chelatingagents, known for their wide-ranging use in removing heavy metalsfrom soil and plant wastewater, make the most of this property. Owingto its reduced impact on eutrophication, citric acid serves as a substitutefor phosphates within the detergent industry.29Citric acid is a weak organic acid, characterized by being odorless,colorless, and a white crystalline powder, with the systematic IUPACdesignation 2-hydroxypropane-1,2,3-tricarboxylic acid. It was firstdiscovered by Scheele in 1784 1. Due toSeparation in the chemical andpharmaceutical industries constitutes40–70% of operating costs. However, separation procedures areextensively employed for the recovery and purification of finishedor intermediate products in sectors like pharmaceuticals and food,where the direct impact on human health is significant and substandardproducts are intolerable. Amoxicillin trihydrate, a semisyntheticantibiotic manufactured in substantial quantities globally, servedas the subject of this study.This study aims to elucidate therole of environmentally friendlycitric acid as a solvent in the crystallization process of amoxicillintrihydrate. The effects of citric acid on the nucleation and crystalgrowth of AMCT were investigated, followed by an analysis of the relationshipbetween efficiency and purity and two different concentrations ofcitric acid, three distinct mixing speeds, three varying pH values,and two different crystallization times by using a full factorialdesign. Furthermore, the study delves into particle size analysisand the methodology of crystallizing amoxicillin trihydrate usingcitric acid.The data obtained from this study, considered agreen process,will contribute not only to the purification of AMCT but also to thedevelopment of effective strategies for purifying other β-lactamantibiotics.22.12PO4), anddibasic potassium phosphate (K2HPO4) were procuredfrom Sigma-Aldrich. Citric acid was purchased from Merck, and solutionsof 1.5 and 2.0 M citric acid were prepared. Sodium hydroxide was obtainedfrom Sigma-Aldrich, and a 5.0 M NaOH solution was utilized. Distilledwater used throughout the study was prepared by using the Milli-Qsystem.The chemicals used in thisstudy were employed without undergoing any purification process. AMCTwas sourced from North China Pharmaceutical Inc., Hebei, China. 4-HPG,ethanol, monopotassium phosphate , three distinct pH values , threevarying mixing speeds , and two differentcrystallization times (30 and 120 min).The AMCT/4-HPG crystallization process using citric acid is illustratedin For all experiments, 1.67 g of AMCT and 0.167 g of 4-HPG were accuratelyweighed and placed in the jacketed reactor. The citric acid solution(1.5 or 2.0 M) at room temperature was added to the AMCT/4-HPG mixturein the reactor, and the mixture was stirred at the designated mixingspeed  until complete dissolution was observed(observed dissolution pH: 1.8). After complete dissolution, stirringwas continued under the same conditions for an additional 5 min. Subsequently,the undissolved solids were removed using a 0.45 μm Whatmannylon filter paper. The dissolved AMCT/4-HPG mixture was then adjustedto the desired pH  by gradually adding a 5.0 M NaOHsolution at a rate of 1 mL/min while maintaining the same temperatureas the system. Two different crystallization times, 30 and 120 min,were employed for crystal formation. The addition of both citric acidand NaOH was continuously monitored by using a pH meter. The resultingfine and delicate crystals were transferred slowly to a Büchnerglass funnel and filtered through a 0.2 μm Whatman nylon filterpaper. Vacuum pressure was applied to ensure the complete separationof the crystals from the mother liquor. Subsequently, the crystalswere transferred to a desiccator for drying after being rinsed witha mixture of 10 mL of ethanol and distilled water .2.2.2The AMCT crystalsprepared using citric acid were subjected to analysis using a Shimadzu1100 HPLC instrument equipped with a Shimadzu detector and pump. Calibrationcurves were established for both AMCT crystals and the 4-HPG impurityincorporated into the crystal lattice. The quantities of pure crystalsand impurities in the crystal sample were then determined based onthe peaks obtained.2HPO4 and 90 mL of 0.2 M KH2PO4 were combined and subsequently diluted to atotal volume of 1000 mL with deionized water. The entire solutionwas stirred for 5 min, after which the buffer solution was filteredthrough a 0.45 μm filter paper. The mobile phase, comprisingmethanol/acetonitrile , was prepared and subsequently filtered.The mobile phase gas was eliminated by using an ultrasonic bath. TheHPLC measurements were conducted under the following conditions: flowrate: 1.0 mL/min, wavelength: 230 nm, injector volume: 10 μL,and total run time: 15 min.A 5 μm Alltech Econosil C-18 column(250 × 4.6 mm) was employed for this study. To prepare the phosphatebuffer solution  utilized in the HPLC analysis, 10mL of 0.2 M KThe HPLC measurements for the studywere performed using the dualgradient elution method, shown in 2.2.3AMCT (pure amoxicillin trihydrate),P30 ,and P120  were utilized. The samples were individually mixed with deionizedwater in a mass ratio of 1:50. The particle sizes of the samples weredetermined using a Brookhaven Zeta Potential and Laser Particle SizeAnalyzer 90Plus Zeta instrument. Each sample was analyzed three times,and the average of the results obtained in this study was used.In thisstudy, samples coded as P2.2.4Unintentionalerrors in an experimental investigation can lead to wasted time, significantfinancial losses, and compromised empirical accuracy. To thoroughlyanalyze the effects of employing citric acid as a natural acid onthe crystallization of amoxicillin trihydrate, this study successfullyconducted experiments within the framework of a well-designed experimentalstrategy.Pilot tests were conducted by using various citricacid concentrations, crystallization times, pH values of the crystallizationprocess, and mixing speeds. These tests aimed to establish the correlationbetween the raw materials (AMCT and 4-HPG) employed in this studyand the specific crystallization conditions. Subsequently, the relationshipbetween these pilot studies and the “used citric acid–crystallizationconditions” was examined to develop a design for amoxicillintrihydrate crystals.A “general full factorial”design was employed toevaluate the impact of citric acid and crystallization process conditionson amoxicillin trihydrate crystals. This experimental design facilitatedthe assessment of multiple factors at more than two levels. The primaryobjective of using this approach was to yield clearer and more reliableresults with a reduced number of trials. Furthermore, this designmethod aimed to standardize the levels of the crystallization processparameters. The study sought to attain purer and finer-grained AMCTcrystals.In this study, citric acid was utilized in two differentconcentrations;the crystallization pH took on three distinct values, the mixing speedvaried at three rates, and the crystallization time was set at twodifferent durations. The process parameters and corresponding levelsemployed are detailed in The yield and purity of the final productwere evaluated followingthe crystallization process with citric acid. After each trial, crystalsamples were subjected to HPLC analysis to assess the process yieldsand purities. Using the gathered data, the overall yield and purityof the AMCT crystals were calculated.33.1Crystal size holdsgreat significance in various fields including chemistry, pharmaceuticals,and food. The pharmaceutical industry, in particular, emphasizes theproduction of specific polymorphs that manifest the desired drug properties.Factors such as the solvent choice, pH, process conditions, and presenceof impurities can exert a substantial influence on crystallizationoutcomes.In this study, two important factors that affect thegrowth of AMCT crystals are the impurity (4-HPG) added to the AMCTmolecule and the citric acid, in which the AMCT molecule dissolves.AMCT, P30, and P120. Throughoutthe entire crystallization process, both P30 and P120 were visually inspected and examined by using online instruments.As anticipated, an increase in particle size and a broader distributionwere observed as the crystallization time of amoxicillin trihydrateextended from 30 to 120 min. AMCT), as measured, ranged between 1250 and 1500 μm,aligning with literature values.31 For the othersamples, the mean particle diameters were found to be 315–500μm for P30 and 500–630 μm for P120. In this study, two significant factors influencing thegrowth of the AMCT crystal are the impurity (4-HPG) introduced tothe AMCT molecule and the citric acid in which the AMCT molecule dissolves.Particle size analysis was conducted on a total of three samplesincluding P3.2The calibration curve representsone of the most important methodsfor quantifying the quantities of active, auxiliary, or impurity substanceswithin the final product. In our study, a calibration curve was employedto determine the quantities of AMCT and impurity substances presentin the crystal samples obtained using natural acid. Five standardswere utilized for AMCT. The calibration curve was generated usingregression analysis to establish the linear relationship between theconcentration and peak area. All analyses were conducted in triplicate,and the average values were recorded. The HPLC data acquired wereprocessed to provide comprehensive insight into the efficiency andpurity of the crystals obtained through the utilization of naturalacid.3.2.1P values below 0.05 were consideredsignificant at a 95% confidence level. As a result, it was determined that the factors Cons, Speed, Time,pH, and the interactions Cons*Speed, Cons*pH, and Speed*pH were significant.Furthermore, by examining the Pareto chart in S) is 0.00077.The R2 value indicates that 97.71% ofthe variation in the response can be explained by the model, whichis considered good. The remaining 2.29% of the variation cannot beexplained by the model. The adjusted R2 value (R2(adj)) is 96.79%, indicatinga modified R2 value. The relatively smalldifference between R2 and R2 (adj) suggests the presence of insignificant factorsin the model. The predicted R2 value (R2 (pred)) is 99.77%, indicating the predictabilityof the model for new observations. In thecrystallization process conducted using natural acid, the purity valueof AMCT was modeled according to the full factorial design in Minitabsoftware. The analysis of variance (ANOVA) table for purity valuesis provided in The main effect graph for AMCT purity 5 examineAmong the investigated process variables, mixing speed has an effecton AMCT purity, and a range of 250–1000 rpm was studied. Whenthe mixing speed is increased from 250 to 500 rpm, the AMCT purityincreases with a small slope. However, when the mixing speed is furtherincreased from 500 to 1000 rpm, the purity increases with a steeperslope.Another process variable investigated is the crystallizationtime.Two different times, 30 and 120 min, were applied for the completionof the crystallization process after reaching the predetermined pHvalue of the solution prepared with natural acid. It was observedthat as the expected time for crystallization increases, the purityof the crystal structure also increases.Another significantvariable affecting the crystallization processis the pH. In this study, three different pH values  were preferred. It was found that the pH of the crystallizationprocess affects the amount of 4-HPG incorporated into the AMCT crystallattice. As the pH of the crystallization process increases, the solubilityof 4-HPG also increases. Therefore, it was determined that the amountof 4-HPG in AMCT decreases as the pH of the process increases, resultingin an increase in the AMCT purity.According to the multipleeffect graph shown in The factors influencing purityin the crystallization process arepresented in the pie chart in 3.2.2R2 value indicates how much of the variationin the response can be explained by the model and is 93.71%, whichis considered good. The remaining 6.29% of the variation cannot beexplained by the model. The adjusted R2 value (R2 (adj)) is 92.13%. The totalyield in the study conducted using natural acid can be expressed asthe ratio of the amount of product obtained after washing to the totalamount of material used. The obtained AMCT yield values were modeledby using a full factorial design model in Minitab software, and theanalysis of variance (ANOVA) values are provided in The main effect graphfor the AMCT yield 9 examineThe factors influencing AMCT yield are shown in the pie chart in 3.2.3To optimize the purityand total yield of AMCT crystallized withnatural acid, input variables such as the natural acid concentration,mixing speed, crystallization time, and pH combination were used.The response optimizer module of the Minitab software package wasemployed in the study for optimization, and the most suitable solutionwas determined. As shown in the multiple response prediction 7, a natu432 Moreover, the use of citric acid is expectedto have positive contributions to various aspects of the AMCT crystallizationprocess, including product design, purification, and synthesis.Thisarticle presents a significant study that focuses on the removalof 4-HPG, an impurity added to the crystal lattice of amoxicillintrihydrate (AMCT), whose molecular structure is similar to that ofAMCT. The impurity is removed from its crystalline structure usingenvironmentally friendly citric acid. The study sheds light on thedissolution stage of AMCT, which is the initial step in both batchand continuous crystallization processes for AMCT. This study is thefirst to highlight the importance of citric acid utilization in thecrystallization process from a comprehensive perspective. The effectsof citric acid on the nucleation and growth of AMCT crystals werestudied and analyzed. The resulting crystal structures exhibit dimensionsthat are highly desirable in the pharmaceutical industry. These dimensionswere achieved without the need for additional purification steps.The study was designed using a two-level citric acid concentration,a three-level mixing speed, a three-level pH, and a two-level crystallizationtime. Experiments were conducted by following a full factorial design.The purity and yield values of AMCT were analyzed using a multivariatetable, and process parameters such as the citric acid concentration,pH, stirring speed, and crystallization time were examined. Insteadof using the commonly preferred acids in the crystallization process,different concentrations of citric acid were employed. This considerationtook into account the balance among cost, purity, yield, and environmentalimpact. The results were then compared. The highest purity value wasachieved under the conditions of pH of 5.5, stirring speed of 1000rpm, and crystallization time of 120 min, yield of 99.64%. Additionally,the highest yield of 59% was obtained with a 2.0 M acid concentration,pH of 5.5, stirring speed of 1000 rpm, and crystallization time of120 min.Furthermore, this study highlights the distinct natureof the crystallizationprocess compared to other procedures documented in the literatureand industry. The notable difference lies in the absence of any environmentallyharmful chemicals employed during the process. As a result, this studyexemplifies an exemplary green process, setting it apart within theexisting body of the literature.


Cecropia obtusifolia, belonging to the Cecropiaceae family, such as ursolic acid, α-amyrin, chrysin, and isoorientin. These compounds were evaluated with specific molecular docking and molecular dynamics (MD) studies using three different malarial targets with the PDB codes 1CET, 2BL9, and 4ZL4 as well as the prediction of their pharmacokinetic (Pk) properties. Docking analysis revealed the following best binding energies : isoorientin–1CET (−9.1), isoorientin–2BL9 (−8.8), and chrysin–4ZL4 (−9.6). MD simulation validated the stability of the complexes. Pharmacokinetics analysis suggested that the compounds would generally perform well if administered. Therefore, these results suggest that these compounds may be used as potential drugs for the treatment of malaria.Malaria is a disease that affects many people in the world. In Mexico, malaria remains an active disease in certain regions, particularly in the states of Chiapas and Chihuahua. While antimalarial effects have been attributed to some species of Cecropia in various countries, no such studies have been conducted in Mexico. Therefore, the objective of this study was to evaluate the in silico antimalarial activity of some active compounds identified according to the literature in the species of  Anopheles spp. which requires feeding for reproduction; the infection of this disease causes symptoms such as fevers, headache, and chills, symptoms that can be implicated in fatigue, confusion, seizures, shortness of breath, anemia, coma, and death [Malaria is a disease that affects many people worldwide but mainly newborns, children under five years of age, pregnant women, travelers, and people with a compromised immune system are at risk of developing a major infection . It is and death ,2.P. vivax is the second most studied species, only below P. falciparum due to the severity of the disease it causes and its wide distribution; in Central America alone, P. vivax is the etiologic agent of 95% of diagnosed malaria cases and in the region of the Americas the incidence is 4.6. Some standardized treatments to combat this disease have been generating resistance due to bad practice or continuous use. Many of the current drugs have been developed by the use of plants in a traditional way and by studies of some compounds present in the species with some activity, such as some flavonoids of plant species that have been described with antimalarial potential, although many more are unknown [Cecropia obtusifolia is a perennial tree distributed from Mexico to Brazil, that has been extensively studied for its pharmacological properties and use in the treatment of chronic diseases [Plasmodium spp. parasite and transmitted to humans by the Anopheles spp. vector.In 2021, half of the world’s population was at risk of malaria and during the same year, there were 247 million cases and 619,000 deaths mainly in the African region where the incidence is 232.8 (cases per 1000 people at risk) [diseases ,12, amondiseases . Recentldiseases , an infeCecropia obtusifolia. On the other hand, some of the most targeted molecular or therapeutic sites in the search for antimalarial drugs are the proteins 2BL9, 4ZL4, and 1CET. The protein 2BL9, or dihydrofolate reductase protein of Plasmodium vivax, is a crucial target in the development of new antimalarial agents [P. vivax and it is the site of action of synthetically formulated compounds to combat malaria such as WEHI-842 [Plasmodium falciparum lactate dehydrogenase cofactor binding site, which is the main binding and action site for chloroquine, one of the most widely used drugs to treat malaria [To date, no studies have evaluated the potential antimalarial activity of some compounds of l agents ,16 such l agents . SimilarWEHI-842 ,18,19,20 malaria ,22.C. obtusifolia through in silico analysis, as well as to assess their pharmacokinetic properties.Therefore, the main objective of this study was to evaluate the potential antimalarial activity of the metabolites identified formerly in Due to some background in the use of flavonoids from other species with potential antimalarial activity, it was decided to work with flavonoids such as chrysin, isoorientin, and triterpenes such as ursolic acid and α-amyrin present in this species C. obtusifolia, using Autodock Vina to calculate the binding energy. The three target proteins used were 2BL9, 4ZL4, and 1CET, which play a key role in malaria treatment and have known binding sites in their crystalline structures (found in the .pdb file) with a 25 Å grid.Molecular docking analysis was performed to assess the interaction capacity between the target proteins and the compounds found in The binding energy of all compounds was below −7.7 kcal/mol, as shown in P. vivax. On the other hand, isoorientin demonstrated the highest affinity among the compounds tested with 1CET.Overall, chrysin showed the strongest binding affinity for both 2BL9 and 4ZL4. Its interaction with 2BL9 involved residues Cys14, Ala15, Asp53, Phe57, Ile173, and Tyr179 which coincide with pyrimethamine. In contrast, its interaction with 4ZL4 only aligned with a Wehi-binding residue (Asp48). The high affinity of chrysin for these two receptors positions it as a promising compound for malaria treatment, particularly in the eradication of The validation of molecular docking was performed by re-docking the ligands into the active target sites. Ligand re-docking was conducted after randomizing the ligand’s conformation and orientation. RMSD values were manually calculated using PyMOL, resulting in the following values: 0.635 Å (1CET-Chloroquine), 0.333 Å (4ZL4-WEHI), and 0.639 Å (2BL9-pyrimethamine).After evaluating the protein–ligand interactions and obtaining satisfactory results, molecular dynamics (MD) analysis was conducted on the ligands with the highest affinity energies to their targets to assess the stability of the protein–ligand complexes. MD simulations were performed for 50 ns for the complexes with the lowest affinity energy: chrysin–2BL9, chrysin–4ZL4, and isoorientin–1CET.To analyze the structural stability and fluctuations of the complexes, the trajectories were analyzed using RMSD (root mean square deviation), RMSF (root mean square fluctuation), RG (radius of gyration), and SASA (solvent accessible surface area).The RMSD of the protein backbone was plotted against the 50 ns simulation duration to assess variations in structural conformation . The chrThe RMSF plot was used to analyze the flexibility of each amino acid residue in the target protein upon binding to a ligand . In the C. obtusifolia tend to have lower RG values.RG values of the protein backbone atoms were used to analyze the compactness and structural flexibility of the complex over simulation time . In the Furthermore, the changes in the surface area (SASA) for all complexes were plotted against time to estimate the variations . In all C. obtusifolia were assessed using the ADMETlab 2.0 web server. The main pharmacokinetics parameters are presented in The ADMET properties of four compounds found in The distribution parameter was evaluated for plasma protein binding (PPB), volume distribution (VD), and the blood–brain barrier (BBB). All the compounds exhibited very high affinity to plasma proteins , resulting in a low therapeutic index. However, the results obtained for VD and BBB were appropriate for all tested compounds. In terms of excretion, isoorientin and ursolic acid showed low clearance (CL), while α-amyrin and chrysin exhibited good clearance. The half-life (T1/2) was long for α-amyrin and ursolic acid but short for chrysin and isoorientin.Regarding toxicity, none of the compounds were found to be probable inhibitors of the human ether-a-go-go-related gene (hERG) channels or carcinogenic. However, only ursolic acid presented an intermediate probability of being hepatotoxic to humans (H-HT).Plasmodium spp. and transmitted by mosquitoes of the Anopheles spp. genus [C. obtusifolia compounds such as α-amyrin, ursolic acid, chrysin, and isoorientin; the degree of purity of a compound, identifying and comparing analytes, and monitoring a reaction. It is easy to understand and execute, allowing separations in a short time and them to be versatile and low cost. In addition, only small amounts of reagents, standards, and samples are used for the identification of different compounds even in the same run [Malaria is an infectious disease caused by the protozoa parasite p. genus . Despitep. genus . Therefop. genus . Plants p. genus ,27,28. Hsame run ,30. Thessame run , Rivera-same run , and othC. obtusifolia and three pharmacologically relevant proteins from Plasmodium spp.: dihydrofolate reductase protein (2BL9) [P. vivax [P. falciparum [To evaluate the antimalarial activity, we utilized computer-aided drug design (CADD) tools. CADD encompasses various techniques such as de novo drug design, receptor-based ab initio pharmacophore modeling, dynamic trajectory water pharmacophores, free energy perturbation calculations, polypharmacology, and big data analysis ,32,33. On (2BL9) , aspartiP. vivax , and laclciparum .Plasmodium spp. releases numerous proteins into the cytosol that are essential for its survival in this environment. One such vital enzyme in these pathways is the aspartic protease plasmepsin V (4ZL4) [P. falciparum, lactate dehydrogenase produces lactate as a byproduct associated with growth regulation [Regarding the targets of interest, dihydrofolate reductase (2BL9) is a target of interest for the treatment of various infectious diseases, including leishmaniasis, tuberculosis, bacterial infections, and malaria, among others . It playV (4ZL4) . Additiogulation . This enC. obtusifolia compounds to bind to and inhibit these enzymes and determined their potential as antimalarial agents. Our compounds exhibited greater binding affinity compared to the control compounds obtained from the provided .pdb file, except for the 4ZL4-ursolic acid complex. Among the compounds tested, chrysin demonstrated the highest affinity for both 2BL9  and 4ZL4 ; similar results to those obtained by Sivaramakrishnan et al. [P. falciparum lactate dehydrogenase. Chrysin is a natural polyphenol with documented biological activities, including anti-cancer and hepatoprotective properties [P. falciparum sensitive and resistant to chloroquine [P. vivax and P. falciparum receptors, exhibiting lower binding energies than the control compounds.Having compounds that can inhibit this type of enzyme is a key aspect of the development of new antimalarial drugs ,42,43. Tn et al.  against n et al.  against operties . Likewisoperties  to an exoroquine . On the oroquine ,50, antioroquine , and actoroquine ,53. Its oroquine , althougTo assess the stability of the evaluated complexes from molecular docking, a molecular dynamics (MD) analysis was conducted on the best docking results. MD simulations investigate the dynamic behavior of proteins and their interactions with small molecules, such as drugs . To confRMSD, or root mean square displacement, measures the average deviation of backbone atoms in the protein–ligand complex from a reference structure (Rref) and is plotted over the simulation time. RMSF, or root mean square fluctuations, quantify the positional deviations of individual particles (atoms) in relation to their reference positions. The radius of gyration (RG) provides information about the overall size and structural changes of the protein during MD simulations . The solBy analyzing these parameters, we confirmed that chrysin and isoorientin do not significantly impact the stability of the studied targets. They remain attached to the proteins without causing structural disturbances, suggesting their potential to function effectively when administered and bound to specific protozoan proteins.C. obtusifolia. ADMETlab 2.0 is a web server designed for predicting the pharmacokinetics and toxicity properties of chemicals, including various endpoints such as physicochemical properties, medicinal chemistry properties, ADME properties, toxicity endpoints, and toxicophore rules [In order for a substance to be used as a medicine, it must undergo evaluations to determine its ability to efficiently navigate through the body, thereby producing the expected effects. Additionally, the substance’s potential toxicity needs to be evaluated. These assessments fall under the domain of ADMET, which encompasses pharmacokinetics . Conductre rules .Among the four compounds, isoorientin is the only one that may not be suitable for oral administration as it exhibits low intestinal absorption and poor permeability through Caco-2 cells. However, the possibility of administering it via non-oral routes is not ruled out. Distribution analysis indicates favorable volume distribution (VD) and high affinity to plasma protein binding (PPB) for all compounds, as well as the ability to penetrate the blood–brain barrier (BBB). This last aspect is particularly relevant for cases of cerebral malaria, as certain drugs may struggle to reach the brain due to this biological barrier ,61.Regarding excretion, α-amyrin demonstrates the most favorable values for both clearance and half-life (T1/2). However, the excretion values of the other compounds do not impose limitations on their potential administration; rather, dosage adjustments may be necessary based on their respective rates of excretion ,63.Toxicity is a critical consideration when assessing compounds for potential drug use in vivo. Therefore, it is crucial to determine any potential toxicity before initiating further analyses . One of P. vivax  and P. falciparum (1CET) were obtained from the Protein Data Bank  with resolutions of 1.90 Å, 2.37 Å, and 2.05 Å, respectively. The structures were processed using the Dock Prep Module of UCSF Chimera 1.14 [Crystal structures of key receptors for era 1.14 . This inera 1.14 .https://pubchem.ncbi.nlm.nih.gov/, accessed on 25 November 2022). Avogadro 1.2.0 [The ligands  and control compounds  were obtained in a Mole2 file format from PubChem , 4ZL4 , and 1CET , based on the previous position of the natural ligand and providing additional space. The Lamarckian genetic algorithm (LGA) was performed with the rigid receptor molecule to search for the best conformers. Ten independent docking experiments were carried out for each structure using the Autodock Vina standalone tool ,70. AmonMolecular dynamics simulations were conducted to assess the binding stability, conformation, and interaction modes between the compounds  and receptors . The selected ligand–receptor complex files were subjected to molecular dynamics studies using GROMACS 2019.2 software . The top+ and Cl− ions. Each complex underwent a 50 ns simulation in the NPT ensemble .For the MD simulation, the complex structure was first minimized in a vacuum using the steepest descent algorithm for 5000 steps. The solvated complex was then placed in a cubic periodic box with dimensions of 0.5 nm, using a simple point charge (SPC) water model and maintained at a temperature of 310 K. The system was further equilibrated with an appropriate salt concentration of 0.15 M by adding Nahttps://simlab.uams.edu/, accessed on 5 January 2023). Graphs were generated using the Xmgrace tool of Grace Software [Trajectory analysis, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), and solvent accessible surface area (SASA), was performed using the GROMACS simulation package through the online server “WebGRO for Macromolecular Simulations” . Specifically, α-amyrin and isoorientin exhibited the most promising activities and their pharmacokinetic behavior was predicted to be satisfactory, indicating their potential for administration without issues. Therefore, these two compounds possess the necessary characteristics, along with in vitro and in vivo studies, to be considered as potential antimalarial agents. Moreover, these results provide evidence supporting the traditional use of C. obtusifolia as a treatment for malaria.This study demonstrates the potential inhibitory activity of the four compounds identified in


Single-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. Various imputation methods have recently been developed to address this problem. Therefore, it is important to perform a systematic evaluation of different imputation algorithms.In this study, we evaluated 11 of the most recent imputation methods on 12 real biological datasets from immunological studies and 4 simulated datasets. The performance of these methods was compared, based on numerical recovery, cell clustering and marker gene analysis. Most of the methods brought some benefits on numerical recovery. To some extent, the performance of imputation methods varied among protocols. In the cell clustering analysis, no method performed consistently well across all datasets. Some methods performed poorly on real datasets but excellent on simulated datasets. Surprisingly and importantly, some methods had a negative effect on cell clustering. In marker gene analysis, some methods identified potentially novel cell subsets. However, not all of the marker genes were successfully imputed in gene expression, suggesting that imputation challenges remain.In summary, different imputation methods showed different effects on different datasets, suggesting that imputation may have dataset specificity. Our study reveals the benefits and limitations of various imputation methods and provides a data-driven guidance for scRNA-seq data analysis.The online version contains supplementary material available at 10.1186/s12859-023-05417-7. Advances in single-cell RNA sequencing (scRNA-seq) technologies have enabled the exploration of the transcriptome at the resolution of individual cells . This caRecently, many studies have reported promising advances in the field of single-cell omics, highlighting the importance of single-cell data analysis , 5. In pAs the ultimate goal of imputation is to recover true data and gain more reliable biological insights, it is essential to determine whether these methods can aid in subsequent analyses, such as discovering the cell clusters, and determining whether these clusters can be discriminated by marker genes and represent meaningful cell types –19. MoreIn this study, we conducted a systematic evaluation for 11 known or adapted imputation methods on 12 real datasets and 4 simulated datasets, based on numerical recovery, cell clustering and marker gene analysis.We first evaluated these methods from the perspective of numerical recovery, and calculated imputation errors to directly demonstrate their ability to recover true expression levels. We then evaluated the methods on the cell clustering task, to determine their ability to recover and enhance the underlying clusters within the original data. We paid more attention to evaluating the methods based on marker gene expression, because the investigation of marker genes is an excellent way to determine actual biological significance. This study reveals the benefits and limitations of various imputation methods, and provides data-driven guidance for scRNA-seq data analysis.The initial aim of imputation is to impute the dropouts in scRNA-seq data to approximate the true expression values. Therefore, it is a direct way to measure the numerical difference between the true values and imputed values of different imputation methods, to evaluate the bias distribution and imputation accuracy.On real datasets, including ILC, HCC, CRC, NSCLC, PBMC, BCC, ITC, human and mouse DCs and Melanoma.1  and PhenoGraph on both original and imputed data to capture the underlying clustering structure. As the analysis results based on these two methods were relatively consistent, we have only shown the results of SC3.A crucial factor that reflects the effect of imputation on clustering analysis is the consistency between the clusters uncovered from the imputed data and the ground truth. Therefore, the adjusted rand index (ARI) was evaluated for all of the methods.On real datasets, surprisingly, data imputed by most imputation methods had lower ARI scores than those before imputation Fig. a. Most mOn simulated datasets, most methods performed well on Sim1, which has a dropout rate of approximately 30%  . Thus, tWe further selected the two slightly separated monocyte subsets derived from DrImpute to remap monocytes in data imputed by other methods Fig. c. Based Overall, four methods — NE, SAVER, scImpute and DrImpute — improved the original data in the marker gene analysis. scImpute and DrImpute may be beneficial to detect subtle cell types.Unlike previous benchmarking studies that mainly used cell lines with homogeneous cell populations, this paper evaluates the methods mainly on single cell datasets from real world. Such datasets tend to exhibit greater cellular heterogeneity, introducing higher variability and complexity, making the evaluation more challenging. Additionally, the availability of reference datasets is often more limited compared to well-characterized cell lines, further complicating the evaluation process.Besides 11 imputation methods mentioned above, we also evaluated some other methods, such as AutoImpute . HoweverThe evaluation results are summarized in Fig. On the numerical recovery task, almost all of the methods showed a biased estimation of dropout events. Furthermore, statistical model-based methods performed unstably on simulated datasets, which may result from inconsistencies with the assumed models. Additionally, it may be too difficult for imputation methods to achieve accurate numerical recovery. For example, the lowest mean absolute imputation error on ILC exceeded 175 Fig. a, while We found that the effects of imputation methods, in terms of median error, varied in different protocols. For most imputation methods, it was difficult to reduce the median error on Smart-Seq2 (and Smart-Seq) datasets, but easy on 10x Chromium datasets Fig.  and 4, wIn clustering analysis, based on the ARI metric, only SAVER slightly improved most real datasets, while the other methods generally showed unstable performance. However, on simulated datasets, most methods performed relatively well. Moreover, under the silhouette score based on the ground truth cluster labels, SAVER and NE showed better performance. However, under the silhouette score based on SC3 clustering results, most methods were unstable on real datasets, but performed better on simulated datasets. Overall, no method performed consistently well across all datasets, and some methods even had negative effects on most datasets. Furthermore, it is not easy for imputation methods to improve real datasets, particularly those with biologically homogeneous cell subsets, such as HCC, CRC, and NSCLC. For example, on HCC, in which all of the cells are T cells, most methods performed poorly, as indicated by the ARI and silhouette scores. Thus, how to improve the cluster analysis in highly homogeneous subsets remains a substantial challenge for imputation methods.Evaluations based on clustering analysis and visualization also suffers from some difficulties, due to the overdependence on the ground truth or the lack of prior knowledge. On one hand, for ARI and silhouette scores based on the known cluster structures, the ground truth was annotated in the original studies. If there is an unknown but significant difference between the ground truth and the true intrinsic clustering structure, evaluation based on the ground truth is of little benefit. Although the ground truth of simulated datasets is accurate, analyses based on simulated datasets are always limited, due to the differences between real and simulated datasets. On the other hand, the silhouette coefficient based on SC3 clustering results, can be used to evaluate imputation methods without ground truth, thus eliminating the errors caused by inexact ground truth. However, evaluation that relies on no prior knowledge would be unreliable. As a result, how to evaluate the effect of imputation on clustering analysis remains to be improved.During the evaluation, much attention was paid to marker gene analysis, because of its biological significance. Imputation methods are expected to at least recover marker gene expression values. However, our results showed that, different imputation methods had varied performance in marker gene expression and may introduce false-positive signals.For example, DCA, DrImpute, MAGIC, scVI, and ZINBWaVE introduced a large amount of noise on ILC Fig. b. In addFive imputation methods were selected according to their performance Fig. , and werBesides, based on marker gene analysis, it appeared that imputation may assist the discovery of potentially novel cell subsets. DrImpute and scImpute have been found to have advantages to identify more sub-clusters Fig. , which mWe also evaluated the impact of imputation on gene-gene correlation. Based on some significantly correlated gene pairs from bulk RNA-seq datasets , we compThere are some potential improvements for imputation methods. For example, to promote biological discoveries, imputation methods should focus more on the improvement of data in downstream analyses, which is closely related to the method design. Therefore, imputation methods could incorporate the characteristics of scRNA-seq data, such as interactions among genes and connections between cells, to improve their effectiveness. Moreover, with the development of high-throughput sequencing technology, the size of scRNA-seq data will grow rapidly. However, some imputation methods cost a lot of time on some datasets , then choose the best-performing one for subsequent tasks. Furthermore, it is essential to understand the purpose of the analysis. For instance, when analyzing the gene-gene correlation, high correlation after imputed by some methods, like MAGIC, should be treated with caution, unless it can be supported by other techniques such as bulk RNA-seq. Additionally, since imputation is dataset-specific, it is crucial to analyze the expression patterns of known marker genes after imputation to determine their validity within the dataset.In this study, we conducted a systematic evaluation of 11 imputation methods for scRNA-seq data. The results revealed that the performance of different methods varied across different datasets, suggesting that imputation may have dataset specificity. In particular, based on the experiments evaluating downstream analysis, real datasets were barely improved by most imputation methods. In contrast, simulated datasets were always improved. Furthermore, methods without statistical models had more advantages on simulated datasets.In general, based on their performance in cell clustering and marker gene analysis, two imputation methods, SAVER and NE, are recommended for downstream analyses. In additional, we recommend DrImpute and scImpute for discovering novel subtle cell types, due to their potential in identifying sub-clusters of single cells.All of the tested datasets –35 are sTo further evaluate the effectiveness and robustness of the different methods, four simulated datasets with varying proportions of dropouts were synthesized using Splatter . BrieflyQuality control of the real datasets was performed before imputation. First, bulk RNA samples within the datasets were removed. Low-quality single cells were then filtered out if the number of expressed genes or the library size exceeded the upper threshold or fell below the lower threshold. The upper threshold was defined as the 75th percentile of all cells plus three times the interquartile range (IQR), while the lower threshold was defined as the 25th percentile minus three times the IQR. Genes that were expressed in no more than two cells were removed.In dataset BCC, which contains more than 50,000 cells, only the top 1000 genes with the highest expressional variance were retained for imputation, to speed up the calculation. DrImpute and scImpute were not applied to this dataset, as the number of cells exceeds the limit of DrImpute, and the run time of scImpute exceeds our time limit (5 days).To quantify the numerical recovery of the scRNA-seq data, we measured the imputation error for each imputation method on datasets with raw count data.l and l and l and Specifically, for simulated datasets, the baseline dataset, which has true expression values that are missing in the four simulated datasets, was treated as the ground truth. Following a similar strategy in scScope , two lisFor real datasets, we followed the downsampling strategy used in scVI . We simuTwo clustering algorithms, PhenoGraph  and SC3 i in ground truth and cluster j in clustering results, n denotes the number of all the cells, To compare the consistency between the clustering results and the ground truth or the original label in the corresponding study, we used adjusted rand index (ARI), which is defined asi is defined asa(i) denotes the mean intra-cluster distance of sample i and b(i) is the mean nearest-cluster distance of sample i. The silhouette score of a clustering is the mean silhouette score of all of the samples.We used silhouette coefficient to measure coherency, based on the ground truth or clusters generated using SC3. The silhouette score of a sample For real datasets, clusters annotated in the corresponding study were used as the ground truth. For simulated datasets, the original clusters in the baseline dataset were used as the ground truth.To determine whether imputation can improve marker gene analysis, we used marker genes to discriminate cell clusters for data before and after imputation, and evaluated whether the clusters are well separated. Marker genes of each cell type and subset were extracted from the published literature.First, the mean expression value of marker genes was used to measure how much a cell belongs to a cell type. For a particular cell type, if the mean value of the corresponding marker gene expression in a cell exceeded a predefined threshold, the cell would be labeled with that cell type. A cell could have no label, one label or multiple labels. The predefined threshold was optimized to maximize the proportion of one-label cells, while ensuring that more than 90% of cells had labels. The threshold varied among different datasets and imputation methods, due to their heterogeneity.Based on the cell labels, a new dataset was then constructed from the original dataset. The constructed dataset consisted of the two-dimensional projection of cells with one or more labels. Multi-labeled cells were duplicated multiple times. The silhouette score was calculated for the constructed datasets, to evaluate whether they could be well separated by the labels.The type of input data and the parameter settings of all of the imputation methods are shown in Table i in cell j. Next, we constructed a cell-to-cell similarity matrix by calculating the Pearson correlation between cells. Network enhancement was then applied to denoise the similarity matrix and enhance the cell-to-cell correlations. We normalized the denoised similarity matrix to a weighting matrix by dividing all of the similarity values by the maximum value of each cell, and set the self-weight of each cell to 1.5 times the maximum weight to its neighbors. To share information from similar cells, recovered data were obtained by multiplying the weighting matrix by the original data matrix, as in MAGIC [Specially, the NE algorithm, a network enhancement method , was adain MAGIC .For SIMLR , we firsWhen using SAUCIE , the inpAdditional file 1: Fig. S1: Visualization of different methods on ILC. On dataset ILC, data before imputation ('raw') and after imputed by different methods were visualized by t-SNE, with different colors representing different cell types. For each subgraph, values in the lower left and lower right corners represent the silhouette scores based on the ground truth and the ARI scores, respectively.Additional file 2: Fig. S2 Visualization of different methods on PBMC. On dataset PBMC, data before imputation ('raw') and after imputed by different methods were visualized by t-SNE, with different colors representing different cell types. For each subgraph, values in the lower left and lower right corners represent the silhouette scores based on the ground truth and the ARI scores, respectively.Additional file 3: Fig. S3Visualization of different methods on Melanoma.2. On dataset Melanoma.2, data before imputation ('raw') and after imputed by different methods were visualized by t-SNE, with different colors representing different cell types. For each subgraph, values in the lower left and lower right corners represent the silhouette scores based on the ground truth and the ARI scores, respectively.Additional file 4: Fig. S4 Visualization of different methods on HCC. On dataset HCC, data before imputation ('raw') and after imputed by different methods were visualized by t-SNE, with different colors representing different cell types. For each subgraph, values in the lower left and lower right corners represent the silhouette scores based on the ground truth and the ARI scores, respectively.Additional file 5: Fig. S5 Visualization of different methods on NSCLC. On dataset NSCLC, data before imputation ('raw') and after imputed by different methods were visualized by t-SNE, with different colors representing different cell types. For each subgraph, values in the lower left and lower right corners represent the silhouette scores based on the ground truth and the ARI scores, respectively.Additional file 6: Fig. S6 Visualization of different methods on DC_mouse. On dataset DC_mouse, data before imputation ('raw') and after imputed by different methods were visualized by t-SNE, with different colors representing different cell types. For each subgraph, values in the lower left and lower right corners represent the silhouette scores based on the ground truth and the ARI scores, respectively.Additional file 7: Fig. S7 Visualization of raw count, SAVER, NE and AutoImpute. Data before imputation ('raw') and after imputed by SAVER, NE, and AutoImpute were visualized by t-SNE, with different colors representing different cell types. For these data, log transformation was not performed before visualization, as AutoImpute imputed data with many negative values.Additional file 8: Fig. S8 The performance of five imputation methods on benchmarking datasets.Five selected imputation methods  were tested on datasets from three different protocols . (a) For the numerical recovery task, two indices, the median error and mean error, are shown. (b) For clustering analysis, three indices, ARI, silhouette based on ground truth 'sil(g)', and silhouette based on SC3 clusters 'sil(s)' are shown. The five selected imputation methods did not show different tendencies with respect to these five indices across these three protocols. Human samples including PBMCs and HEK293T cells, were used for the analyses.Additional file 9: Fig. S9 Cluster visualization of five imputation methods on benchmarking datasets.Clusters on three different protocols  were visualized by t-SNE. Colored cell labels were directly derived from the original study.Additional file 10: Fig. S10 Marker gene expression on HCC, CRC, NSCLC and DC_human. Expression values of marker genes of different datasets before and after imputation are shown: CD3D and CD3E for HCC, CRC and NSCLC; ITGAX and CD1C for DC_human. Expression values of marker genes in different datasets before and after imputation are shown. We selected the following marker genes for analysis: CD3D and CD3E for T cells; CD79A and CD79B for B cells; NCAM1 for NK cells; and ITGAX and CD1C for DCs.The datasets HCC, CRC, and NSCLC represent T cells, which should highly express CD3D and CD3E, but not CD79A, CD79B, or NCAM1. The dataset DC_human represents DCs, which should highly express ITGAX and CD1C, but not CD3E, CD79A, or CD79B.Additional file 11: Fig. S11 Marker gene expression of different cell types from the Quartz-Seq2 protocol. Expression values of marker genes of different cell types are shown: CD3D and CD4 for CD4$^+$ T cells; CD3D, CD8A and CD8B for CD8$^+$ T cells; CD19 and CD79A for B cells; NCAM1 for NK cells;CD14 for CD14$^+$ monocytes; FCGR3A for FCGR3A$^+$ monocytes; SLIT2 for HEK293T cells.Additional file 12: Fig. S12 Marker gene expression of different cell types from the Chromium protocol.Expression values of marker genes of different cell types are shown:CD3D and CD4 for CD4$^+$ T cells; CD3D, CD8A and CD8B for CD8$^+$ T cells; CD19 and CD79A for B cells; NCAM1 for NK cells; CD14 for CD14$^+$ monocytes; FCGR3A for FCGR3A$^+$ monocytes; SLIT2 for HEK293T cells.Additional file 13: Fig. S13 Marker gene expression of different cell types from the Smart-Seq2 protocol. Expression values of marker genes of different cell types are shown: CD3D and CD4 for CD4$^+$ T cells; CD3D, CD8A and CD8B for CD8$^+$ T cells; CD19 and CD79A for B cells; NCAM1 for NK cells;CD14 for CD14$^+$ monocytes; FCGR3A for FCGR3A$^+$ monocytes; SLIT2 for HEK293T cells.Additional file 14: Fig. S14 Run time of different imputation methods. The run times (in seconds) of different methods are shown for all datasets. Green and yellow grids correspond to faster and slower speeds, respectively. The methods were ranked by the 'overall' score, which is the average score of all of the datasets.


Frontiers in Immunology was the most popular journal in this research area. Keywords “multiple sclerosis,” “inflammation,” “regulatory T cells,” “neuroinflammation,” “autoimmunity,” “cytokines,” and “immunomodulation” were identified as high-frequency keywords. Additionally, “gut microbiota” has recently emerged as a new topic of interest. The study of Treg cells in neurological diseases continues to be a hot topic. Immunomodulation, gut microbiota, and cytokines represent the current research hotspots and frontiers in this field. Treg cell-based immunomodulatory approaches have shown immense potential in the treatment of neurological diseases. Modifying gut microbiota or regulating cytokines to boost the numbers and functions of Treg cells represents a promising therapeutic strategy for neurological diseases.This bibliometric study aimed to summarize and visualize the current research status, emerging trends, and research hotspots of regulatory T (Treg) cells in neurological diseases. Relevant documents were retrieved from the Web of Science Core Collection. Tableau Public, VOSviewer, and CiteSpace software were used to perform bibliometric analysis and network visualization. A total of 2,739 documents were included, and research on Treg cells in neurological diseases is still in a prolific period. The documents included in the research were sourced from 85 countries/regions, with the majority of them originating from the United States, and 2,811 organizations, with a significant proportion of them coming from Harvard Medical School. Howard E Gendelman was the most prolific author in this research area. Considering the number of documents and citations, impact factors, and JCR partitions,  The nervous system serves as the body's command center, and interruptions or impairments of its function can induce neurological diseases, including stroke, spinal cord injury, traumatic brain injury, multiple sclerosis (MS), Alzheimer's disease (AD), and Parkinson's disease (PD) . Unfortu+ T cells defined by constitutive expression of IL-2 receptor alpha (CD25) and forkhead box p3 (Foxp3) (+CD25+ marker (Regulatory T (Treg) cells are a minor subpopulation of CD4alpha CD2 and fork+ marker . This la+ marker . In addiEarly findings suggested that Treg cells could modulate neuroinflammation and attenuate disease progression , 8. HoweBibliometric analysis can analyze and visualize scientific outputs, research hotspots, and trending topics of a certain field in public literature databases . Bibliom*” OR “regulatory T-cell*” OR “Treg*” OR “T-reg*”) AND TS = (“neuro*”)  . Subsequhttp://www.bioinformatics.com.cn) was used to plot the annual document output. Software Tableau Public was applied to draw the geographic distribution of documents. VOSviewer v.1.6.1 and CiteSpace v.6.1.R6 were applied to perform the bibliometric analysis and network visualization, including co-authorship analysis of countries/regions, organizations and authors, co-occurrence analysis of keywords, citation analysis of journals and documents, and co-citation analysis of references.We analyzed relevant data in the following aspects: the annual number of documents, countries/regions, organizations, authors, journals, keywords, and references. Online platform . After excluding disjointed organizations, the remaining 59 organizations were visualized . The topA total of 13,859 authors were involved in Treg cells in neurological diseases, and 185 met the threshold (minimum number of documents of an author: 5). The largest set of connected items consisted of 51 authors . The topFrontiers in Immunology with 134 documents, Journal of Immunology with 90 documents, and Journal of Neuroinflammation with 75 documents were the most prolific journals. Impact factors of the top 11 journals ranged from 3.3 to 15.1, of which Brain Behavior and Immunity was the highest, and Journal of Neuroimmunology was the lowest. Of the top 11 journals, 6 journals belonged to Q1, 4 journals belonged to Q2, and the remaining 1 journal belonged to Q3. Notably, the Journal of Experimental Medicine, with the most citations , was not among the top 11 journals. The document “HIF1 alpha-dependent glycolytic pathway orchestrates a metabolic checkpoint for the differentiation of T(H)17 and T-reg cells” published in this journal in July 2011 was cited 1215 times, which ranked second in terms of the number of citations. Although the number of documents published in the Journal of Experimental Medicine was relatively small, the quality of documents was relatively high, which had conspicuously pushed forward the progress in this field. In short, both the number and the quality of documents need to be considered in the evaluation of prolific journals. Considering the number of documents and citations, impact factors, and JCR partitions, Frontiers in Immunology was the most popular journal in this research area.A total of 859 journals published 2,739 documents concerning Treg cells in neurological diseases. The top 11 journals are listed in A total of 4,868 author keywords were involved in 2,558 documents, and 354 met the threshold (minimum number of documents of a keyword: 5). The overlay visualization map showed the co-occurrence relations of keywords , in whicAs shown in The top 30 keywords with the strongest citation bursts are shown in The top 10 documents with the most citations are listed in The co-citation analysis of cited references was performed by VOSviewer. A total of 162,113 cited references were involved in 2,739 documents, and 131 met the threshold (minimum number of citations of a cited reference: 40). The density visualization map of cited references based on citations is shown in Reference burst detection can help find the most influential cited references and discover research frontiers and trends. In total, seven references with the strongest citation bursts were obtained . The refFrontier in Immunology was the most popular journal, playing an active role in promoting the development of Treg cells in neurological diseases. Howard E Gendelman, currently affiliated with the University of Nebraska Medical Center, has published the most documents on Treg cells in neurological diseases. These documents primarily focused on neuroimmunity, neuromodulatory, immunomodulation, and neuroprotection. Among these documents, the document “Regulatory T cells attenuate Th17 cell-mediated nigrostriatal dopaminergic neurodegeneration in a model of Parkinson's disease” has achieved the most citations. This study highlighted the potential of Treg cells in regulating neurodestructive immunity and laid the foundation for immunization strategies for PD , can stimulate Treg cell generation to promote immune suppression. Therefore, the immunomodulatory effects of gut microbiota may be mediated primarily via the Th17/Treg axis (Acinetobacter calcoaceticus extract was shown to alter lymphocyte differentiation in healthy individuals, resulting in an increase in Th1 cells and a decrease in CD25+Foxp3+ Treg cells, while exposure to the Parabacteroides distasonis extract increased Treg cell differentiation (Substantial evidence has indicated that the gut–brain axis likely plays a crucial role in neurological diseases, with an altered gut microbiota potentially having significant implications on immune responses in both the gut and distal effector immune sites such as the central nervous system . A studyreg axis . Exposurntiation . Patientntiation . Myasthentiation . Butyratntiation . Patientntiation . Therefo+CD25+ Treg cells are regulated by Foxp3, while peripheral CD4+CD25− T cells can acquire suppressor function through ectopic Foxp3 expression. This discovery opens up a new way for cell-based therapies for autoimmunity (+ natural Treg cells, playing a critical role in maintaining Treg cell homeostasis (Cytokines are under active investigation as immune modulators to boost the numbers and functions of Treg cells in neurological diseases. The development and function of CD4immunity . IL-2 iseostasis , 38. Stueostasis , 67. Theeostasis . Consequeostasis . Tocilizeostasis . TherefoThis study is the first bibliometric analysis to systematically analyze documents related to Treg cells in neurological diseases. Nevertheless, there are still some deficiencies here. First, only English language articles and reviews published in the Web of Science Core Collection were collected, which may lead to language and publication bias. As bibliometric analysis is closely linked to timeliness, it is essential to continuously update the results and trends of research on Treg cells in neurological diseases to keep pace with ongoing scientific exploration. This will enable a more comprehensive understanding of the topic as well as provide more precise predictions of future trends. Finally, this review discusses from the perspective of the neuroprotective role of Treg cells, however, varying perspectives exist in certain studies. Therefore, before reaching a consensus, it is important to consider multiple aspects when targeting Treg cells for the treatment of neurological diseases and exercise caution in their use. However, given the large enough number of documents in this analysis, we believe that this study provides an instructive perspective for the research of Treg cells in neurological diseases and guides future research in this field.Frontiers in Immunology was the most popular journal in this research area. Immunomodulation, gut microbiota, and cytokines represent the current research hotspots and frontiers in this field. Treg cell-based immunomodulatory approaches have shown immense potential in the treatment of neurological diseases. Modifying gut microbiota or regulating cytokines to boost the numbers and functions of Treg cells represents a promising therapeutic strategy for neurological diseases. Hence, we can conclude from these documents that future therapeutic strategies for neurological diseases should leverage the therapeutic potential of Treg cells, with an emphasis on modulating their activity to promote neuroprotection.Through VOSviewer, CiteSpace, and Tableau Public software, we have carried out a bibliometric analysis on Treg cells in neurological diseases. The study of Treg cells in neurological diseases continues to be a hot topic. The United States was the largest contributor among 85 countries/regions, and China was the most potential country. More than half of the top 10 most prolific organizations were located in the United States, and Harvard Medical School was the most important organization in this field. Nearly half of authors who make major contributions belonged to the United States organizations when publishing documents. The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.QG: Conceptualization, Data curation, Writing—original draft, Writing—review and editing. XL: Conceptualization, Validation, Writing—review and editing. YL: Funding acquisition, Project administration, Validation, Writing—review and editing. JL: Methodology, Writing—review and editing. MP: Formal analysis, Writing—review and editing. JW: Visualization, Writing—review and editing. FY: Software, Writing—review and editing. YZ: Funding acquisition, Project administration, Supervision, Writing—review and editing.