It is presumed that structural and functional alterations of biopolymers, which occur during grinding, are caused by a mechanical modification of polymers. As a result, thermally induced changes of flours are neglected. In this study, the impact of thermo-mechanical stress (TMS), as occurring during general grinding procedures, was further differentiated into thermal stress (TS) and mechanical stress (MS). For TS, native wheat flour, as well as the purified polymers of wheat—starch and gluten—were heated without water addition up to 110 °C. Isolated MS was applied in a temperature-controlled ultra-centrifugal grinder (UCG), whereby thermal and mechanical treatment (TMS) was simultaneously performed in a non-cooled UCG. TS starch (110 °C) and reference starch did not show differences in starch modification degree , gelatinization onset ) and hydration properties , respectively. However, TS led to an elevated gelatinization onset and a rise of water absorption of flours (Z-kneader) affecting the processing of cereal-based dough. No differences were visible between MS and TMS up to 18,000 rpm regarding hydration properties . Consequently, mechanical forces are the main factor controlling the structural modification and functional properties of flours during grinding. Wheat flours display unique interactions of water, gluten proteins and starch fractions during dough preparation and bread making, which enable the production of porous foods such as white bread and cakes . Wheat fThe physical modification of cereal polymers can thereby occur under dry conditions or in the presence of water through the treatment of polymer suspensions , left.Although a lot of research was related to physical starch modification in suspensions, as heat moisture treatments (HMT), annealing and ultrasonic treatments ,13,14,15The most common physical modification process—the grinding procedure of cereals—combines thermal and mechanical forces . NeverthAs a result, the distinction between thermally and mechanically induced alterations is a prerequisite to purposively modify the functional properties of the natural biopolymer starch during the grinding procedure. The physical modification during grinding presents a comparably cheap method and offers benefits compared to a chemical modification, since physically modified starch does not have to be declared and is free of chemical agents . To enable the elucidation of the impact of isolated thermal and mechanical forces during grinding on polymer modification, flour  as well as gluten and wheat starch were subjected either to a thermal treatment, to a combined thermal and mechanical treatment (heat and impact/shear) in a grinder, or to a mechanical treatment (impact/shear) by a temperature-controlled grinding procedure. Subsequent analysis of hydration properties , starch modification degree (AACC 76-31), particle size distribution and characteristics of starch crystal melting provided information concerning the modification on structural and functional levels of cereal biopolymers. Dough made from (modified) flours were analyzed by a Z-kneader system to gain detailed knowledge of correlations between flour treatments and functionality. The aim of the study was to differentiate between mechanical and thermal effects on flour during grinding in order to achieve a targeted modification of wheat flours or to standardize the grinding process. Based on this knowledge, physically modified flours or starches can be purposively deployed in the food industry and replace chemical modified starches. Pure variety wheat grain ‘Akteur’ , wheat starch  and gluten  were used in this study.n = 3).Furthermore, selected experiments were performed on a second wheat flour variety , rice flour  and rye flour . Moisture content (AACC- 44-01) of reference samples was 11.96 ± 0.03%, 13.87 ± 0.06%, 9.50 ± 0.04%, 13.70 ± 0.02%, 12.49 ± 0.02% and 12.26 ± 0.06%, respectively. The substances were exposed to either mechanical stress (MS), thermo-mechanical-stress (TMS) or thermal stress (TS). To achieve a thermal modification, bio-materials were distributed in thin layers (<5 mm) on baking parchment and thermal stress (TS) was applied for 13 min at 20 °C, 40 °C, 65 °C, 80 °C, 95 °C, and 110 °C in a baking oven. Thermo-mechanical modification was achieved by a non-temperature controlled impact grinder . Flour samples were ground (at room temperature) at rotational speeds of 0, 6000, 9000, 12,000, 15,000 and 18,000 rpm using a 250 µm mesh size sieve, resulting in extensive heat development and a temperature rise. Mechanical treatment (MS) of flours was achieved by using pre-cooled flour (−18 °C). The grinding procedure  was performed in a cooled operational room (+ 4°C) for MS, so that flour temperature did not exceed 60 °C during mechanical treatment. After modification, samples were hermetically sealed and stored at 20 °C ± 2 °C in the dark until the following analysis. All modification procedures were performed in triplicates . The dry dispersion unit enabled the measurement of modified flours, gluten and starch excluding a particle hydration. To calculate the particle size, the measurement principle ‘Mie Theory’ with a refractive index of 1.54 and the general-purpose mode was applied.The starch modification degree was measured by means of an enzyme assay kit . The method determines the percentage of starch granules (on 14% moisture basis) in flour and starch hydrolyzed amylases within a defined time range. Calculation was performed using the following Equation (1):n-decan for a temperature range from +20 to +95 °C. Each sample was mixed with deionized water (flour: water ratio = 1:3), filled in DSC pans (20–35 mg) and hermetically sealed. After 2 min of equilibration at 25 °C, samples were heated to 95 °C at 10 K/min in a nitrogen atmosphere. The gelatinization onset (To), peak (Tp), and conclusion temperatures (Tc), and the enthalpy of gelatinization (ΔH) were determined using the Pyris software.Determination of gelatinization properties of starch and flour samples were carried out using a differential scanning calorimeter (DSC)  equipped with an Intracooler 2P cooling system. Calibration was performed with indium and g based on dry mass.Hydration properties of flour, gluten and starch were analyzed in accordance with the approved method of the AACC 56–11.02 (solvent retention capacity with water = water retention capacity) for reference and modified samples. WRC was calculated as the weight of the water hold by wheat flour samples after 10 min of centrifugation at 1000× Dough was with  wheat flour (100 parts) and distilled water. According to the AACC method 54.21 a torque measuring Z-kneader system  was used to determine the optimum water addition of flours. The optimum was reached when torque was between 480 and 520 FU (farinograph units).® Pro . To estimate the proportion of variation in the response, coefficient of multiple determination (R2) was calculated as (sum of squares (model))/).The statistical analysis was performed with JMP50,3 describes the medium, volume particle size of each flours and decreases triggered by an increase in the extent of certain physical treatments, such as high-pressure application [Particle size distribution (PSD) of flours provides important knowledge to predict the functional behavior of modified flours. In general, wheat flours show a three-modal distribution: the first peak (<10 µm) is formed by small, spherical B starch granules, the second peak (around 10–41 µm) by disc-shaped A starch granules and the largest peak (41–300 µm) by starch granule-protein agglomerates , wherebylication . In thislication .To elucidate the impact of thermal stress in combination with mechanical stress on flour particle size, wheat flour was either subsequent ground in a temperature-controlled ) or non-temperature-controlled  impact grinder. The temperature development during MS and TMS processes is illustrated in No differences between particle size distribution of exclusively mechanical stress (MS) and thermomechanical stress (TMS) were noticed compare B. Thus, Beside the particle size distribution of flours, the starch modification degree (SMD) is an important factor for the evaluation of flour quality, as is the baking capability. The measurement of the starch modification degree (SMD) is based on the enzymatic digestibility of starch in flour particles by amylolytic enzymes. Thus, the accessibility and surface/volume ratio of particles significantly affect the SMD. The fragmentation of particles leads to the enlargement of the particle surface, which in turn promotes the digestion by enzymes ,29,30. TIsolated mechanical stress (MS) in temperature-controlled grinder did not lead to differences in SMD in comparison to thermo-mechanical stress (TMS) in non-temperature-controlled grinder at low rotation speed, where a low heat development took place. However, grinding in the ultra-centrifugal grinder at higher rotation speeds  resulted in a significantly lower SMD of mechanically stressed flours in comparison to thermo-mechanically stressed flours. Since the combination of thermal and mechanical forces during grinding caused a significantly higher starch modification degree (SMD) as isolated mechanical forces, TMS flours had a higher accessibility of starch for an enzymatic digestion, resulting in altered functional properties during fermentation.Reasons for the enhanced enzymatic degradation (higher SMD) could be due to transformations in amorphous and the crystalline structures ,34 causeH)—measure for the degree of starch crystallinity or rather the transition of crystal structure into amorphous structure—and the gelatinization onset—starting temperature of transition of crystalline into amorphous structure—are important parameters in predicting the functional properties of flours. In this present investigation, no continuous changes in gelatinization enthalpy of thermally treated flour or isolated starch were noticed with rise in temperature of TS  of thermally stressed (TS) pure starch and wheat flour are summarized in re of TS , probablTpeak of starches, however, Tonset seems to be less affected by heat-moisture treatments [However, a steady increase in the gelatinization onset was determined for thermally treated wheat flours . Alterateatments . In parteatments ,43 makesFurthermore, a rise in gelatinization onset was solely noticed for wheat flour, although the gelatinization onset of pure starch remained constant. Thus, a reorganization of crystalline parts of starch by heat treatment is unlikely. Rather, it is assumed that changes in gelatinization onset are due to alterations of water distribution between starch and gluten caused by a water barrier of denatured protein, as illustrated schematically in On the other hand, starch granules of pure starch are freely accessible for water, since proteins were removed during the extraction of pure wheat starch. Consequently, heat treatment does not affect the gelatinization onset of starch. To investigate in more detail changes in polymer functionality due to the different physical treatments, water retention capacity (WRC) and dough functionality were analyzed.In the baking industry, the hydration properties of wheat flour are of special interest in determining the dough processability and affecting the quality of bread . As showThese insights bring advantages for the construction of modern mills and the control of grinding processes, since flour functionalities and qualities can be set during grinding, depending on the purposed usage. If hydration properties are in the foreground, heat development during grinding can be neglected and consequently costs for cooling units can be economized. However, if the gelatinization properties are relevant, for instance in flours for bread production, heat development in grinders should be controlled during the grinding procedure to achieve a standardization of the flour quality.However, results from the applied WRC method (AACC 56–11.02), which determine the hydration properties of flours in excess water, cannot be directly transferred to a matrix with limited water content, as for doughs . To inveWater absorption of thermally stressed flours increased slightly from 61.0 ± 0.3% (+20 °C) to 62.0 ± 0.3 (+110 °C) suggesting a modification of wheat polymers. Significant alterations occurred solely for a temperature of 110 °C, hence above the denaturation temperature of proteins. This denaturation can result in a higher water binding capacity of proteins and consequently firmer dough, if the water amount is not adapted. Since WRC is performed in excess water, slight changes in hydration properties might not be visible. However, even small alterations in hydration properties may significantly alter the functional properties of doughs. The dough development times for TS flours stayed constant for temperature up to 110 °C.To summarize, in limited water systems (DoughLab) and as well in excess water (DSC gelatinization onset), the impact of thermal treatment on polymer functionality was visible. The onset temperature and furthermore the water absorption was elevated. Thus, thermal stresses of flours during grinding or transportation of flours influence the processing of wheat dough and the manufacturing of wheat products. Consequently, temperatures during the processing of grains, the transportation and storage of flours should be controlled to a enable a constant flour quality.However, wheat functionality and the ability to enclose high amounts of gas is mainly attributed to the unique properties of the gluten network. To clarify if findings are generally valid for further cereals, TS , MS  and TMS  was applied to rice, rye and a second wheat variety .In accordance to previous findings, TS did not affect WRC or SMD of a second wheat flour (wheat 2). Thus, results from previous chapters are transferable to other wheat varieties.However, thermal stress of rice and rye flour caused a rise in WRC from 95.97 ± 0.68% dm to 104.58 ± 0.70% dm and from 124.74 ± 0.30% dm to 135.60 ± 0.97% dm, respectively. Interestingly, TS of rice and rye flour caused higher WRC, but did not elevate the starch modification degree. Since accessibility of starch is not altered by TS, a higher amount of water is retained by non-starch components of rice and rye flours. Further research should focus in detail on the alterations of proteins and arabinoxylans from rice and rye caused by thermal stress.Mechanical treatments  with or without thermal stress caused significant alterations of the WRC and SMD within all flours. For rye flours, the combination of thermal and mechanical stress (TMS) caused significantly higher WRC and SMD than pure mechanical stress MS (compare TMS rye and MS rye). The rise of WRC of TMS rye could be explained by the enhanced swelling of heat treated arabinoxylans , howeverPhysical treatments (for instance during grinding) can be used to gain more detailed knowledge of structure-function relations in complex cereal matrices, as wheat flour, and to produce flours with altered functional properties for the use in clean label products. This study showed that functional alteration of wheat starch is exclusively based on mechanical stress during grinding, however, thermal stress can cause a modification of the gelatinization onset of wheat flour. Thus, proteins and/or other components of flours have to exhibit changes in structure or conformation leading to a rise in gelatinization onset and flour hydration. This indicates that wheat proteins are thermally modified even in dry matrices, which necessitates a detailed investigation of the functional alteration of gluten caused by thermo-mechanical processes. Depending on the field of application, thermal forces should therefore be monitored and, moreover, controlled during the grinding process and any further processing of grains.


Dextrins are widely used for their functional properties and prepared by partial hydrolysis of starch using acid, enzymes, or combinations of both. The physiochemical properties of dextrins are dependent on their molecular distribution and oligosaccharide profiles. In this study, scanning electron microscopy (SEM), X-ray diffractometry (XRD), rapid viscoanalysis (RVA), high-performance Liquid Chromatography (HPLC) and gel permeation chromatography (GPC) were used to characterize dextrins prepared by common neutral and thermostable α-amylase hydrolysis. The dextrin granules displayed irregular surfaces and were badly damaged by the enzyme treatment. They displayed A-type X-ray diffraction patterns with a decrease of intensity of the characteristic diffraction peaks. The RVA profiles showed that the viscosity of dextrin decreased with the increase of its Dextrose Equivalent (DE) value. According to HPLC analysis, the molecular weight, degree of polymerization and the composition of oligosaccharides in dextrins were different.  Native starch granules are semi-crystalline and can resist hydrolysis by amylases. When gelatinized, they are readily hydrolyzed and converted to sugars and dextrins, thereby reducing the molecular weight of starch molecules (amylose and amylopectin) ,2. Dextri.e., the degree of polymerization (DP), and is an indicator of the degree of hydrolysis. Thus, glucose has a DE value of 100, while intact starch maybe have an effective DE of zero [The extent of hydrolysis is normally expressed in terms of the ‘‘dextrose equivalent’’(DE), a quantity usually determined by titration and a measure of the total reducing power of the sugars present relative to a dextrose (D-glucose) standard, on a dry mass basis. The DE value is inversely related to molecular weight,  of zero . Starch  of zero  and thisBacillus sp. [Though partial hydrolysis of starch has traditionally been carried out by using acids, acid hydrolysis is being replaced by enzymatic hydrolysis for the production of maltodextrins . The mosllus sp. ,9,10,11.More information is available about starch structure and properties, but there is less information on dextrins. As has been stated, dextrins with different DE values can have different properties and hence different functionality in a particular desired application. Thus, a complete oligosaccharide profile, in addition to DE, is desirable in order to better understand a dextrin’s physical and biological functionality. This paper focuses on the characteristics of dextrins with different DE by the use of scanning electron microscopy (SEM), X-ray diffractometry (XRD), rapid viscoanalysis (RVA), gel permeation chromatography (GPC) and high-performance liquid chromatography (HPLC). The dextrin granules were comparable in size when viewed by a SEM instrument. SEM photographs of the native starch and the dextrins with DE 9, DE 15 and DE 22 are displayed in The X-ray diffraction patterns of the corn starches and the dextrins are shown in Viscosity data for commercial dextrins are normally available from their manufacturers. In this study, the viscosity of dextrin solutions with the concentration 10% (w/v) was determined at 30 ºC. The apparent viscosity values obtained are shown in It was reported that the viscosity of commercial dextrins increased approximately linearly or curvilinearly with both the DP and the concentration of the saccharide , but thaet al. compared the molecular weight of two kinds of dextrins with DE values of 11.4 and 11.9, and found that their DPn were 152.2 and 133.7, respectively [Molecular weights of dextrins with different DE value were determined by HPLC. ectively . These rAs mentioned above, the DPn of Peak 2 in all dextrins were not higher than 6, but the area of Peak 2 were above 75. This means that there were lots of oligosaccharides with DP < 6 in the dextrins. Commercial corn starch was purchased from Changzhi Jinze Biological Engineering Co. Ltd . The common neutral α-amylase (2000 U/mL) and thermostable α-amylase (20000 U/mL) were from Zhengzhou Fuyuan Bio-products Co. Ltd . Sephadex G 50 and standards with molecular weight 2,000,000, 133,800, 41,100, 21,400, 4,600, 180 were products of Sigma. Acetonitrile was of chromatographic grade. Other reagents were of analytical grade. 2, successively. Then, common neutral α-amylase and thermostable α-amylase were added into the above solution to hydrolyze the starch. After enzyme denaturing by high pressure (Shanghai Tocan Science and Technology Co. Ltd) and cooling, the DE-value of the product was determined. The dextrins with different DE values were obtained under the following conditions: reaction temperature, 90 ºC; hydrolysis time, 3, 6, 9, 12, 15, 18, 21, 24 min ; addition of common neutral α-amylase, 4 U/g starch; and addition of thermostable α-amylase, 7 U/g starch. Obtained products were centrifuged, freeze-dried and used to property evaluation.Corn starch (50 g) was mixed with distilled water (150 mL) and stirred for 10 min. This solution was mixed with 0.1 mol/L NaOH to pH 6.0 and 0.1 % (w/v) CaClThe sample (1 g) was diluted to 50 mL and filtered. Then the above solution (0.5 mL) was mixed with 3,5-dinitrosalicylic acid to measure the content of reducing sugar with glucose as standard. DE value was calculated in the following formula:Scanning electron microscopy (SEM) micrographs was recorded with a Quanta 200 environmental scanning electron microscope . The samples were evenly distributed on SEM specimen stubs with double adhesive tape. The micrographs were obtained with an accelerating potential of 15 kV under low vacuum. The micrographs obtained were used to detect any damage to the starch granules.Monochromatic Cu Ka radiation (wavelength = 1.54056 Å) was produced by a D/MAX 2500V/ PC X-ray diffractometer . The samples were incubated in a chamber at 100% RH for 24 h and then packed tightly in a rectangular aluminum cell. The samples were exposed to the X-ray beam from an X-ray generator running at 36 kV and 20 mA. The scanning regions of the diffraction angle, 2θ, were 0–30º, which covered most of the significant diffraction peaks of the starch crystallites. Other operation conditions included: step interval 0.02, scan rate 4º/min, Sollet and divergence slit 1º, receiving slit 1º, and scattering slit 0.16º. Duplicate measurements were made at ambient temperature. Radiation was detected with a proportional detector.The measurements of viscosity of the diluted dextrin solutions  were evaluated with a Rapid Viscoanalyzer . Measurements were made at 30 ºC for 3 min, Speed 160 rpm. Gel permeation chromatography (GPC) profiles were obtained according to the method described by Manhanta and Bhattacharya  with som3. The column temperature was 45 ºC. The flow-rate was 0.9 mL/min and the injection volume was 10 μL. The molecular weight calibration curve was standardized by standards with molecular weight 2,000,000, 133,800, 41,100, 21,400, 4,600 and 180. HPLC was performed with a Waters 600 chromatograph  equipped with a 2401 refractive index detector. The column used was a Ultrahydrogel™Linear (300 mm × 7.8 mm id × 2). The mobile phase consisted of 0.1 M NaNOBefore testing the oligosaccharide composition of dextrins, the samples had been treated by desalination and micro-filtration membrane. HPLC was performed with a Waters 600 chromatograph  equipped with a 2401 refractive index detector and model M730 Data Processor. The column used was a Sugarpak1 with water as mobile phase and the column temperature was 85 ºC. The flow rate was 0.4 mL/min and the injection volume was 10 μL.etc.) and the composition of oligosaccharides in dextrins with different DE value were greatly different. Scanning electron microscopy showed that dextrin granules were irregular on the surface and most of the starch granules were badly damaged by enzyme treatment. X-ray analysis showed that the intensity of the characteristic diffraction peaks of the dextrins decreased, as perhaps the crystalline regions were partially hydrolyzed by the enzymes or partially gelatinized. The RVA profiles indicated that the viscosity of dextrin decreased with the increase of DE value. Molecular distribution (molecular weight, degree of polymerization


Osteopathic Manipulative Treatment (OMT) is a therapeutic approach aimed at enhancing the body’s self-regulation focusing on somatic dysfunctions correction. Despite evidence of OMT effectiveness, the underlying neurophysiological mechanisms, as well as blood perfusion effects, are still poorly understood. The study aim was to address OMT effects on cerebral blood flow (CBF) in asymptomatic young volunteers as measured by Magnetic Resonance Arterial Spin Labeling (ASL) method. Thirty blinded participants were randomized to OMT or placebo, and evaluated with an MRI protocol before manual intervention (T0), immediately after (T1), and 3 days later (T2). After T0 MRI, participants received 45 min of OMT, focused on correcting whole body somatic dysfunctions, or placebo manual treatment, consisting of passive touches in a protocolled order. After treatment, participants completed a de-blinding questionnaire about treatment perception. Results show significant differences due to treatment only for the OMT group (OMTg): perfusion decreased (compared to T0) in a cluster comprising the left posterior cingulate cortex (PCC) and the superior parietal lobule, while increased at T2 in the contralateral PCC. Furthermore, more than 60% of participants believed they had undergone OMT. The CBF modifications at T2 suggest that OMT produced immediate but reversible effects on CBF. Osteopathic Manipulative Treatment (OMT) is a therapeutic approach aimed at enhancing body’s self-regulation aligned with the principles of practice and application of five models of the structure-function relationship . These mDespite the evidence of OMT effectiveness, the neurophysiological mechanisms underlying clinical improvements are poorly understood. A recently published crossover study  showed tHowever, neuroimaging evidence of OMT-induced brain changes is still scarce. A study assessing the brain cortical activity by electroencephalography after osteopathic intervention in the cranial field showed an increase in the absolute power of alpha rhythm , thus inAlthough Positron Emission Tomography (PET) represents the gold standard to investigate cerebral perfusion, Arterial Spin Labeling (ASL) involves the utilization of an endogenous tracer, thus avoiding the risks associated with exogenous radioactive ones. ASL uses magnetically labeled arterial blood water and changes in its decay as a measure of cerebral perfusion. ASL is specific to intravascular changes and can provide absolute quantification of perfusion values. Since this involves the pair-wise subtraction of control and tagged images, baseline drift and motion artifacts do not affect ASL, making it suitable for long term repetitive studies or for those with low-frequency changes . ASL basFor this reason, we hypothesize that using an ASL based technique we could deeper understand which are the possible neurophysiological OMT effects in pain-free subjects. Our hypothesis is that OMT could induce cerebral perfusion effects through a parasympathetic/sympathetic modulation. While the neurophysiological aspects of several types of touches were largely studied , the effThis randomized-controlled single blinded study was approved by the Fondazione Santa Lucia’s local ethic committee with protocol number CE/PROG.625 and was conducted in accordance with the Declaration of Helsinki.All interventions were performed at the outpatient clinic of Fondazione Santa Lucia  from September 2017 to June 2018. Participants were recruited at the Tor Vergata University of Rome. The recruitment document explained that participation was voluntary, without incentives for participants, and dependent on the inclusion and exclusion criteria. All interested participants received information about the project by telephone and were briefly interviewed by a clinician not involved in the intervention sessions, to assess eligibility according to the inclusion and exclusion criteria (see below). Before participating, volunteers provided written informed consent. Forty-four asymptomatic, non-smoker, osteopathically naïve volunteers were recruited. No subject was under any pharmacological treatment during the previous 4 weeks, or suffered from pain within the 6 months before the enrolment.The inclusion criteria were: age between 18 and 40 years and suitability for MRI scanning. Exclusion criteria included: (i) cognitive impairment, based on Mini Mental State Examination (MMSE)  score ≤ Participants were randomly divided into two groups: the OMT group (OMTg) and the placebo group (Pg). Block randomization was performed according to a computer-generated pseudo-randomized list. Participants were unaware of the study design and outcome, as well as of group allocation. A researcher not involved in the intervention sessions performed the randomization. He was the only responsible for the process and securely stored the randomization list.All participants underwent an MRI session before the intervention (baseline or T0), immediately after (T1), and after 3 days (T2). Between T0 and T1 each participant received a single session of 45 min of OMT or placebo manual treatment.The OMT session was performed by two female healthcare professionals who had completed a training program in osteopathy aligned with Italian Core Competencies in osteopathy  and withSomatic dysfunctions were addressed according to tissue alteration, asymmetry, range of motion and tenderness parameters (TART) which guided the osteopathic evaluation and intervention . SomaticNo adverse effects were reported for any participant.After each treatment session, participants were asked to complete the de-blinding questionnaire administered exclusively by a treatment-blinded external trained psychologist not involved in the intervention. The questionnaire consisted of three consecutive questions about subjects’ perception of the treatment received. After being questioned on whether according to their perception, they thought they have received “OMT” or “Placebo” treatment, subjects were asked on a 0–10 numeric rating scale (NRS), where 0 represented absolutely uncertainty and 10 represented absolutely certainty , how cer1. Perfusion data were transformed, first, from ASL space to individual subjects’ structural space using FLIRT (FMRIB’s Linear Registration Tool) and then non-linearly to a standard space  using FNIRT (FMRIB’s Non-Linear Registration Tool).Data were recorded using a Philips Achieva 3T MRI scanner using a 32 channels receive-only head coil. Whole brain cerebral blood flow (CBF) was measured using pseudo-continuous arterial spin (pcASL). Sixty- tag-control image pairs with 29 axial slices  were acquired . A separate single shot EPI (M0) scan was acquired (TR = ∞) with the same parameters to measure the equilibrium brain tissue magnetisation for calibration purposes. A high-resolution T1-weighted whole-brain structural image was also recorded (1 × 1 × 1 mm voxels). In-house software was used to calculate the mean difference of the motion-corrected (MCFLIRT ) tag-cont-tests, power analysis was conducted using G∗Power (d = -0.5). Using alpha equal to 0.05, power equal to 0.80 and Cohen’s d equal to 0.55  the desired sample size for the difference between 2 dependent means resulted to be 22 subjects in total.To determine a sufficient sample size for two sample  G∗Power  based ont-tests (two-tailed) were used to compare between-group differences for gender and for age and education, respectively.The statistical analyses on demographic and clinical data were performed using SPSS 21.0 for Windows . Chi-square test and two-sample 2).Voxel-wise statistical analyses of MRI data were carried out using SPM12 , and the effect of “administering” the treatment (P or OMT) was modeled. The interactions are described by the contrast (OMT_T1 – OMT_T0) – (P_T1 – P_T0) for the comparison post vs. pre, (OMT_T2 – OMT _T1) – (P_T2 – P_T1) for the comparison follow up vs. post, (OMT_T2 – OMT_T0) – (P_T2 – P_T0) for the comparison follow-up vs. pre, representing treatment’s effects controlled by baseline scans, and delayed effect, respectively. We also calculated the correlation between the number of dysfunction and cerebral perfusion changes both for the contrast (OMT_T1 – OMT_T0) – (P_T1 – P_T0) and for (OMT_T2 – OMT_T1) – (P_T2 – P_T1). Data of the de-blinding questionnaire were calculated as percentages like the dichotomous “OMT” and “Placebo” data; the continuous 0–10 NRS outcomes were calculated as means for each treatment group .Following registration of the CBF maps to the common standard space of the Montreal Neurological Institute (MNI), whole brain voxel-wise two-sample N = 15 participants in each group). No significant differences (p > 0.05) were found between groups in terms of age, sex, and education. Participants’ demographic characteristics are reported in Forty-four participants were screened for eligibility. According to the inclusion/exclusion criteria, 30 participants were enrolled and randomized to OMTg and Pg groups  – (P_T1 – P_T0) contrast revealed that perfusion decreased in a cluster of 67 voxels within the left PCC [ MNI space coordinates] and in a cluster of 47 voxels within the left superior parietal lobule (SPL) [ MNI space coordinates]  MNI space]  in the comparison between T0 and T1, and on 24 participants (12 OMTg and 12 Pg) in the comparison between T1 and T2. Pre- and post-intervention changes in perfusion maps were considered as statistically significant at dinates] . ConversI space] . AccordiFurthermore, concerning the correlation between the number of dysfunction and cerebral perfusion changes both for the contrast (OMT_T1 – OMT_T0) – (P_T1 – P_T0) and for (OMT_T2 – OMT_T1) – (P_T2 – P_T1), no significant correlation emerged.Regarding the de-blinding questionnaire, 75% of the OMTg and 41,6% of the Pg answered that they underwent OMT. Ratings mean of both groups regarding treatment allocation certainty was 7.0 ± 1.6 and ratings means of treatment usefulness was 7.5 ± 2.6 for OMTg and 6.6 ± 1.9 for Pg.This randomized controlled neuroimaging trial evaluated, for the first time, the immediate and short-term effects of OMT on cerebral perfusion. We found that OMT, but not placebo treatment, induced changes in resting cerebral perfusion measured by ASL in asymptomatic young volunteers.In spite of the accumulating evidence suggesting OMT to be responsible for changes in the central nervous system functioning, including reflex excitability, cortical processing, as well as for changes in brain plasticity and functional connectivity , the relIn line with our hypothesis assuming a larger OMT effect, as treatment in this study was not restricted just to the cranial field, we found similar results on resting cerebral perfusion, thus reinforcing the notion that whole-body OMT interventions can locally modify the vascular activity . Our intFurthermore, the present results support the theoretical basis of the interrelationship between neurologic and biomechanical osteopathic structure-function models  since thThe theoretical functional model underlying OMT assumes a dynamic balance between the parasympathetic and sympathetic nervous systems, while hypothesizes a potentially disruptive influence of biomechanical strain on both systems, and on the dynamic equilibrium between them . BiomechIn our study, a decrease of the resting cerebral perfusion was found immediately after OMT intervention in a cluster comprising the PCC and the SPL, while PCC perfusion increased significantly after 3 days post-OMT. Crucially for the present evidence, the PCC is a critical node of the central autonomic network  that coGiven the PCC key role in the CAN context, we speculate that the observed change in its perfusion might index a sympathovagal modulation with a shift toward a relatively larger sympathetic  predominance, as a consequence of the OMT effect in overcoming the sympathetic tone. These results are apparently in contrast with previous studies that reported an increase in vagally mediated hearth rate variability (HRV) indexes during or immediately after OMT. These differences could be attributed to different factors. First, the effects on vagal activity could depend on the osteopathic techinique used . PreviouIn our study, we used ASL technique, and the mechanisms behind the relationship between HRV and cerebral blood perfusion are still an open question . ConsequIndeed, while the cingulate cortex is a pivotal region for emotion recognition and pain perception , the PCCPerfusion was reduced at T1 assessment also in the SPL, an area primarily involved in visuomotor functions and spatial cognition, and specifically implicated in processing the spatial configuration of the body . SpecifiThe results of the de-blinding questionnaire could support the hypothesis of a specific bottom-up OMT effect respect to the manual-placebo intervention. The 42% of participants of Pg group believed they underwent OMT, and both groups were sufficiently sure of the usefulness of the treatment received. According to a previous study , the plaIn conclusion, in asymptomatic young volunteers OMT generates a significant effect on PCC perfusion, starting from a CBF reduction immediately after manipulation, followed by an increase of CBF as short-term effect. This opposite effects may be related to a parasympathetic/sympathetic modulation induced by OMT.Even if we provide evidence to support OMT effects on CBF, the inclusion of asymptomatic young volunteer participants may limit the implications of the present study findings regarding the relationship between OMT and brain perfusion. More obvious responses to general OMT intervention might be elicited in participants with pain, and further studies should investigate whether these changes correlate with beneficial clinical outcomes, thus being interpretable as a consequence of somatic dysfunctions correction, and ascribable to the normalization of aberrant afferent input to the CNS. However, the present is the first study in which a follow-up examination was performed 3 days after OMT, allowing the direct observation of short-term treatment effects on CBF in asymptomatic subjects, and thus favoring the notion that the osteopathic treatment has the potential to determine lasting effects beyond that of manipulation itself. Indeed, the fact that perfusion changes were observed in a cortical area involved in the dynamic balance between the sympathetic and parasympathetic systems would suggest an OMT corrective effect on the disruptive influence that biomechanical strains have on those systems. Forthcoming studies will address the potential OMT effects on both HRV and CBF, as to more thoroughly investigate manipulations consequences.Furthermore, it would be interesting to investigate the effect of personal beliefs on CBF and to submit de-blinding questionnaires also immediately after OMT or placebo treatment, given the opposite OMT treatment responses at T1 and T2 in terms of cerebral perfusion. As a matter of fact, since blinding perception may change during the study, it would be important to ascertain whether changes in perfusion in areas involved in internally directed attention (like the SPL) might be ascribable to changes in attributed self-relevance and efficacy of the received treatment. Future studies are needed to clarify these aspects.This randomized-controlled single blinded study was approved by the Fondazione Santa Lucia’s local ethic committee with protocol number CE/PROG.625 and was conducted in accordance with the Declaration of Helsinki.FT and MT organized the database. FT, FaP, and TG performed the statistical analysis. MT wrote the first draft of the manuscript. FT, FeP, FaP, BS, and TG wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version. All authors contributed to the conception and design of the study.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Organ shortage leads to using non-optimal liver grafts. Thus, to determine the graft quality, the Donor Risk Index and the Eurotransplant Donor Risk Index have been proposed. In a previous study we showed that neither could be validated on the French database. Our aim was then dedicated to propose an adaptive Donor Quality Index (DQI) using data from 3961 liver transplantation (LT) performed in France between 2009 and 2013, with an external validation based on 1048 French LT performed in 2014. Using Cox models and three different methods of selection, we developed a new score and defined groups at risk. Model performance was assessed by means of three measures of discrimination corrected by the optimism using a bootstrap procedure. An external validation was also performed in order to evaluate its calibration and discrimination. Five donor covariates were retained: age, cause of death, intensive care unit stay, lowest MDRD creatinine clearance, and liver type. Three groups at risk could be discriminated. The performances of the model were satisfactory after internal validation. Calibration and discrimination were preserved in the external validation dataset. The DQI exhibited good properties and is potentially adaptive as an aid for better guiding decision making for LT. In France, the number of donors older than 65 rose by a factor of 20 between 1998 and 2014. This is partly due to the increase of vascular causes of death and the decrease of traumatic death since 2005.Liver transplantation (LT) is the major therapy to cure end stage liver disease and hepatocellular carcinoma. In France, the “Agence de la Biomédecine” (ABM) is responsible for managing the waiting list and distributing the grafts. A major impediment to LT is the persistent shortage of donors. Although donor age is one of the strongest risk factor of graft failure2 have been proposed. These two scores appeared to have an impact on the post transplantation survival2. However, they could not be validated on the French LT database6. Major donor and recipient differences observed between those databases may explain this result5. If re-calibrating a model can be considered8, however, the discrimination cannot be altered6. Thus, the current DRIs could not be used on our dataset.To assess the quality of a graft, scores such as the Donor Risk Index (DRI) or the Eurotransplant Donor Risk Index (ET-DRI)3. Looking for the best match between a graft and its recipient requires preliminary information about the graft quality prior to LT. A donor quality index (DQI), which scores the characteristics of the graft prior to LT, is one of the key factors for a better matching to a recipient. The covariates used to compose the DQI are those available at the time of the harvest in order to define the intrinsic characteristics of the graft. More, in order to better orient the decision of using a graft, a liver donor quality index is of importance. In 2014, in the United-States, 9.6%9 of donor livers were not transplanted. This rate was 6.7% in France for our study period (2009–2013) . The study was conducted with the approval of the Independent Ethics Committee . Authorization was also obtained from the “Commission Nationale de l’Informatique et des Libertés” (agreement No. 915206). The data provided were de-identified beforehand.Information relating to LT performed in France between January 4, 2009 and December 31, 2013 was obtained from the ABM . The recipients’ follow-up began at LT and ended with the occurrence of one of the following events: lost to follow-up, death, graft-loss (re-transplantation) or at the end of the study, as of December 31, 2014. The outcome was death or graft-loss. Patients with incomplete covariates were not retained as specified in the flow diagram presented in Fig. 4.In accordance with previous worksFor the external validation, we used 1048 LT performed in France between January 1, 2014 and December 31, 2014, followed-up up to December 31, 2015. Patients with incomplete covariates were not retained  Table : the lowThe liver type covariate was retained since it was present in both DRI and ET-DRI. The MELD exception and donor and recipient’s blood groups covariates were also retained, but for adjustment only.Hence the proposed DQI score was as follows:In Table Three groups were obtained according to the following values Fig. : 1.00 < The calibration of the score was assessed. As shown in Fig. Of note, the 95 band was wide at the higher probability of death/graft loss due to few remaining patients at risk in this category. However, despite a slight underestimation, the longer the follow-up, the lower was the underestimation of the risk of death/graft loss.As the predictive performances of the different models were close Table , the cov6 was performed in the validation dataset, which was constituted of grafted patients in 2014. This dataset was independent of the derivation dataset. To calculate the DQI in the validation dataset, we followed the same procedures as proposed previously in the construction steps. Calibration and discrimination were evaluated through seven steps.An external validationThe comparison of the datasets appears in Table The slope on the PI, i.e. ln(DQI), was 0.88 (SD = 0.29). The slope was not different from 1 ; thus, the discrimination was preserved in the validation dataset.Since the estimation of the β* coefficients were not different from 0 (p = 0.64), no adjustment on the DQI covariates was needed.We tested if there was a lack of fit for the adjustment covariates, i.e. recipients’ covariates. The The assumption of proportional hazards was verified for all covariates.The Harrell C-index was 0.587 (0.545–0.628), the Gönen and Heller K statistic was 0.612 (0.594–0.630) and the Royston and Sauerbrei Failure free survival per DQI categories for derivation and validation datasets is given in Fig. First, Kaplan Meier curves were well separated. Then, the risk groups were discriminative. Second, Kaplan Meier curves from derivation and validation data were close. For greater accuracy, we then plotted three survival curves for each risk group with confidence intervals in derivation and validation datasets Fig. . ValidatHRs between groups at risk are presented in Fig. The Kaplan Meier curves  and 0.64 (SE 0.16) for the DRI and ET-DRI respectively, P-value < 0.001) and miscalibrated  according to our datasetFive donor covariates were retained in the final model: age, cause of death, intensive care unit stay, lowest MDRD creatinine clearance, and liver type .10. Too many groups could be unstable and consequently discrimination becomes insufficient. The recommendation is to create three to five groups, not necessarily of the same size, in order to highlight extreme groups10. After construction of the risk groups, HRs  were not different (p = 0.06). The performance of the model remained satisfactory even after correction by the optimism. Finally, an external validation6 was performed; discrimination and apparent calibration were preserved in the validation dataset.We obtained three groups of different sizes Fig. . Of noteHRs Fig. , betweenHRs Fig. . The thiOur study has some limitations. First, it is very difficult to evaluate the intrinsic quality of a graft since the allocation procedure associates to each donor a recipient. The quality of the graft is a function of the graft/recipient survival. We then assume that the allocation was appropriate. The graft/recipient survival provides an indirect measure of the donor quality and thus can be considered as a surrogate variable. We assumed that this variable was highly correlated to the target variable, i.e. the graft quality, and was thus able to be extrapolated as an outcome.12 which the case in our study. This occurred even when models were built in large datasets13.In prognostic models, discrimination measures are generally not highχ2 tests). Of note, only the covariate “presence of a liver cancer” was not part of the score. However, the frequency of liver cancer was higher in the analysis data set, which limits the occurrence of a potential bias.A selection bias has been explored as 858 recipient/donor pairs were removed from the model due to missing data. We compared the distributions of data of this subset to the data used in the analysis either for covariates belonging to the recipient  or donor . After Bonferroni correction, only the presence of a cancer and medical condition before LT were significant . This result is consistent with the DRI and ET-DRI modelsMacro/micro-steatosis was not included in the model since this information was not available in the French database.14, even though this covariate has an impact on post LT survival, such as post- and peri-operative period covariates. These covariates are not known at the time of graft procurement and can’t contribute to the creation of a score, which qualifies the donor. The aim of this work was to characterize the donor independently of the potential recipient. Of note, the distance between the organ procurement center and the transplant center was not correlated to the cold ischemia time (Pearson correlation coefficient r = 0.08), indicating that cold ischemia time is highly dependent on the logistic of transplantation. Moreover, the introduction of perfusion machines may completely reverse the paradigm of organ preservation, and thus will modify the impact of the cold ischemia time. In effect, increasing the duration of organ conservation in standardized conditions may consistently improve the chances for a patient to benefit from the most compatible graft. Whatever the case may be, cold ischemia time did not go through the three different ways of selecting the covariates. About the exploration of a potential bias, as CIT is not known at the time of graft procurement, it does not lead to a selection or misclassification bias. However, there may be confounding bias because CIT exerts an effect on the recipient/graft survival. We went back and found that this was not the case. In effect, CIT had no specific weight in the Cox model , therefore we did not include it in the final model even as an adjustment covariate. Adding CIT in the final model as adjustment covariate would have decreased both the quality and discrimination performance of the model (i.e. increase in Akaiké criterion (AIC) and decrease in c-index, respectively). For example, in case-control studies, when a model is forced to include non-retrained covariates, the model often becomes overfitted. An overfitted model has a weak validity and the model would have insufficient discriminative power in new patients.Since the cold ischemia time (CIT) is only known at the time of LT, this covariate was not retained as an intrinsic characteristic of the graft in the model, as outlined in1 and the French database5. Since age below 70 did not exert a significant effect on recipient survival in our database, the first three quartiles were grouped together. Age over 70 represented 25% in the French database compared to 4.3% in the OPTN database.Donor age was different between the Organ Procurement and Transplantation Network (OPTN) database1, which was 44%, 9% and 45%, respectively (p < 0.001). The risk of death/graft loss was increased in patients who experienced a CVA compared to anoxia . This result was consistent with Feng et al.1 and Singhal et al.15.For COD, 60% of patients presented a CVA, 12% an anoxia and 25% a trauma. This distribution was quite different from that observed in the OPTN database17. However, Cuende et al.18 included 3429 LT and showed that an ICU stay of more than six days represented a moderate risk (RR: 1.21 [1.1–1.4]).In three out of four stays in ICU the donor’s length of stay was less than 4 days. Shorts stays in ICU were associated with an increased risk of death/graft loss. As outlined above, CVA was associated with an increased risk of death/graft loss compared either to anoxia or trauma COD. Given that the length of ICU stays of donors with a CVA was ≤4 days in more than 80% of cases (which represents 49% of the donors), it supports the fact that short stay in ICU in our series was related to a shorter recipient survival. In the literature the donor ICU stay did not appear to influence graft survivalβadjustment for recipient covariates were not included). But, baseline survival was calculated on the original model, which includes all the covariates; both donor and recipient. The weights of the recipients’ covariates then may play an important role in the estimation of Finally, concerning the external validation, the survival among the different DQI groups was not significantly different . This may be due to a lack of power given that the follow up was shorter than in the derivation dataset. We were thus truncated the derivation data follow-up. A longer follow-up period would have been beneficial to obtain a better accuracy. Nevertheless, Kaplan Meier curves were well separated Fig. . Then, ttes Fig. , the app14 suggested that the donor score might benefit from being updated. We aimed to create a flexible DQI model and adaptable. Indeed, if calibration is lacking with a validation dataset comprising a longer follow-up, and if the discrimination is preserved, a re-calibration of the DQI will be possible. This type of procedure is dedicated to be repeated yearly using LT data from subsequent years. This adaptive procedure will enable to gradually take into account the modifications of the graft allocation system. This approach may be of interest for other countries. Indeed, French allocation system is quite similar to most European systems. Moreover, in France, the MELD and MELD exceptions are also used such as in the United States of America. For an international-reader, our model is a tool for decision support in countries with a high activity of harvest of brain dead organ donors, with a significant increase of elderly donors, such as in Italy, Spain or Portugal thanks to a political proactive organ census and collection of old or very old donors. We encourage these countries to make an external validation of the DQI on their own datasets. Furthermore, recently, another national index was introduced, the DLI  creatinine clearance for which thresholds are recommended) we defined four groups according to the quartiles of the covariate distributions. Graft survival curves were plotted according to the product limit method. They were then compared using hazard ratios (HR). In the absence of significant difference between groups, they were re-grouped together. If no groups at risk were identifiable for a given covariate, then this covariate was kept in the model for adjustment as a quantitative variable , according to the three models presented below.In France, all donors are cared for in intensive care units (ICU). The ICU length of stay is based on the number of nights spent, rather than the number of days. Therefore, a stay of 0 day is observable.For recipient, decompensated cirrhosis was identified in the database using the MELD score and the Child-Pugh score. All recipients presenting with cirrhosis of the liver, a MELD score ≥16 and a Child-Pugh B or C were considered as decompensated cirrhosis.Patients first-transplanted, without cancer and cirrhosis, were considered as having a non-cirrhotic liver disease.The estimated distance between donor and recipient centers was calculated on the basis of a geographic model taking into account road distances in minutes.19.MELD exceptions were identified and resulted in extra points while on the waiting listThe variable named “Hors tour” means that after at least five consecutive refusals by the transplant teams, the graft is then supplied to a transplant team who have identified an appropriate candidate.1 and Braat’s2 studies we used a Cox model with adjustment on recipient characteristics to create the score , in contrast to parametric models6. However, absolute risks can be calculated by focusing on a fixed time point . Thus, we plotted three calibration plots at months 3, 6 and 12. We calculated the corresponding baseline survival rates, estimated according to Breslow’s estimator on the complete model21. Then we calculated the predicted probabilities of death/graft loss20 as:S0(t) is the baseline survival, βi the donor coefficients of the Cox model and Xi the donor covariates. Note that the estimated parameters of the score did not change according to the baseline survival. Coefficients are those estimated at the previous step.A calibration plot reports graphically predicted outcome probabilities (on the x-axis) against observed outcome frequencies (on the y-axis)22, the K statistic of Gönen and Heller23 and the 24. Harrell’s C-index is defined as the proportion of all usable patient pairs for which the predictions and outcomes are concordant22. Gönen and Heller’s K statistic is used to evaluate the discriminant power and the predictive accuracy of nonlinear statistical models. It is a function of the regression parameters and the covariates distribution only, and is therefore asymptotically unbiased23. Royston and Sauerbrei’s 24.To evaluate the model performance, we calculated several indices: the C-index of Harrell25, although according to Moons20, internal validation quantifies the predictive ability of a model on the derivation data . Internal validation was assessed using a bootstrapping procedure in order to quantify the optimism associated with the performance of the model. A seven-step procedure was performed according to the method described in Moons20. The confidence intervals of the 3 indices were calculated using 200 bootstrap estimates.Internal validation is a necessary part of model development24; the method described in a previous study4. The external validation of a model explores the assessment of its performance on an independent database6. Model performance was evaluated considering two fundamental aspects: discrimination and calibration. Discrimination, known as “separation”, allows differentiation of patients’ prognoses through risk estimates from the model. Calibration reflects the prediction accuracy; if well-calibrated, a score assigns the appropriate probability at each level of the predicted risk6.We performed an external validation of the DQI according to Royston and Altmanχ2 tests or ANOVA, when appropriate.A comparison of the construction dataset with the validation dataset was performed using Xadjustment the covariates used in the DQI and βadjustment fixed at the estimated value in the original model. The discrimination is considered good when this coefficient is equal to 1 and poor if the slope is lower than 1; a coefficient >1 is considered very good.In order to obtain the prognostic index (PI), we used ln(DQI). The model fitted was as follows:In order to test Z were the covariates used to build the DQI.A possible reason for a PI coefficient less than 1 is poor adjustment of one or more covariates. To test whether one or more of the DQI covariates needed an adjustment we fitted:βDQI was set at 1, and the βadjustment was fixed at the estimated value in the original model. Next, we performed a likelihood ratio test to test the following equation: β* = 0. The proportional hazards risk assumption was checked using the Schoenfeld residuals.In this model, the 22, Gönen and Heller K statistic23, and Royston and Sauerbrei 24.As in the construction step we calculated three discrimination indexes: Harrell C-indexWe plotted the survival curves according to the groups at risk using the Kaplan Meier estimates. We also did a visual comparison of these curves with those of the construction dataset to evaluate the calibration. Indeed, if the survival curves of the construction and validation data for each group at risk were superimposable, then the visual calibration was considered as preserved. Discrimination can also be evaluated from the curves; the more the survival curves are separate the better the discrimination.HR were estimated for each group at risk using a Cox model. The higher the discrimination, the larger the HR.21. We then calculated the estimated survival function in the validation dataset as: In this step, we estimated the baseline survival in the derivation: https://www.R-project.org/).All analyses were performed using R software, version 3.3.0 (R Development Core Team, A Language and Environment for Statistical Computing, Vienna, Austria, 2016. The data that support the findings of this study are available from the French Agency for Biomedecine but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. According to the French regulation, upon reasonable request, data are however available with permission from the Agency for Biomedicine.Supplementary tables and figures


The Principles and Practice of Medicine (1892) wrote:In children and in healthy adults the outlook is good. In the debilitated, in drunkards and in the aged the chances are against recovery. So fatal is it in the latter class [i.e. the elderly] that it has been termed the natural end of the old man .The term “old man’s friend” is often used when referring to pneumonia. Searching for it on Google yields 16,400 results in 0.33 s for this combination. The term is attributed to William Osler, who in the first edition of his book In the 9th edition, published after Osler himself already died (in 1919 from pneumonia at the age of 70 years ), this eStreptococcus pneumoniae and the human immune system can be in the focus of one’s scientific interest for 38 years. For a pediatrician, out of the great number of inherited or acquired diseases affecting infants and children, it is not uncommon to specialize in pneumonia. The biomedical researcher in the above example is Professor Ger Rijkers, the pediatrician is Professor Stephen Pelton, and together we will be taking the reins from Professor Allan Cripps AO as the new co-Editors-in-Chief of Pneumonia.Can old men be friends with pneumonia? Is it possible to like, or even be in love with, a disease? As a patient, no! As a biomedical researcher, yes, that certainly is possible. The study of the interaction between Pneumonia, the journal, that is, began in April 2006, when the 5th International Symposium on Pneumococci and Pneumococcal Disease was held in Alice Springs, Central Australia [Musca vetustissima, as well as representatives of the more than 10,000 other species in the order of Diptera, were trying to get into every orifice of the ISPPD participants. Indoors, Streptococcus pneumoniae, as well as other representatives of micro-organisms that can cause pneumonia, were trying to get into the brains of the meeting’s participants. While in this environment, Allan Cripps had the epiphany to start a new journal focused solely on pneumonia [By forming a journal with an exclusive focus on pneumonia, we are dedicated to establishing an international forum for pneumonia in the broadest context and as a means for bringing together knowledge related to pathogenesis, treatment and prevention of pneumonia .The history of ustralia . In the Pneumonia in 2012 [Pneumonia’s Editorial Board members, Professor Ron Dagan, Allan has done a wonderful job taking the Journal from conception to birth through to its adolescence. It is now our time to guide Pneumonia to maturity.“It is with great pleasure that I welcome you to pneumonia” were Allan’s opening words in the inaugural editorial of  in 2012 . Today, Pneumonia was and will continue to be a peer-reviewed, online, open access journal. It was and will continue to be dedicated to providing an international forum for pneumonia in the broadest context and a means for bringing together knowledge related to the pathogenesis, treatment and prevention of pneumonia. To that end, we will publish original research articles, case studies, reviews, critical commentaries, correspondence, highlights and news on all aspects of pneumonia. We will, in this era of rapidly evolving communication concepts, also consider novel ways to disseminate the science of pneumonia. We will also—in this era of misinformation, fake news and science denial—stand firm for science and for scientific integrity.Pneumonia  share the same fate? Although vaccines are available and continue to improve, currently and for the foreseeable future pneumonia (the disease) remains a global burden with high incidence in the youngest and oldest of our globe’s inhabitants and new pathogens are continually emerging [Pneumonia the journal is here to stay.Vaccination has led to the disappearance of smallpox from the world; therefore, a journal with exclusive focus on smallpox would not be viable today. Will emerging , 8. For There are a number of important themes, challenges and areas of interest in the ongoing fight against pneumonia that we look forward to disseminating in the Journal. The differences in disease burden between adults and children, especially infants, is highlighted by the epidemiology that reveals most pneumonia occurs in healthy children and yet is associated with high mortality rates linked to socially determinants. The highest incidence rates and mortality are found in India, South East Asia and Africa, but the Global Alliance for Vaccines and Immunisation (GAVI) has enabled introduction of pneumococcal-conjugate vaccines that are expected to have a substantial impact on the current disease burden. We look forward to new insights into the respiratory microbiome, such as whether the microbiome can predict those infants at risk for pneumonia, chronic pulmonary disease, recurrent wheezing and, if so, whether modulation of the microbiome can change these outcomes.The distribution of pathogens causing pneumonia still remains challenging. We look forward to new diagnostic approaches that will deconstruct the pathogens that cause pneumonia. The limitation of current diagnostic tools is best demonstrated by the discord between vaccine probe studies and those that assign a more limited role to the pneumococcus in studies of pneumonia. It is hoped that molecular tools may reveal pulmonary pathogens by deciphering the specifics of the host response to bacteria compared to viruses.One of the most common cause of respiratory disease in children remains respiratory syncytial virus (RSV). Despite decades of effort, no proven treatment or prevention strategies have emerged. We appear to be on the precipice and look forward to new and expanded treatments for respiratory syncytial virus infection as well as an RSV vaccine that will further reduce the burden of respiratory disease.Pneumonia? Perhaps chronic inflammation leading to silent progression of cardiac disease is an underlying mechanism.Today, pneumonia still affects many ‘old’ men. Medical progress made since William Osler’s time has resulted in survival rate for hospitalized pneumonia that now sits above 90–95%. However, longer-term mortality is high. The reasons for this are still largely unknown. A hypothesis from the editors of We, as the new co-Editors-in-Chief, standing on the shoulders of Allan Cripps, welcome submissions on any of the themes indicated above, or other aspects of pneumonia that could ultimately lead to a better understanding of the disease, and thus better diagnosis, prevention and treatment.


Crosslinked chitosan forms an interpenetrating polymer network with starch chains, producing microparticles with a very low solubility. The thermal stability of the microparticles is in agreement with their polysaccharide composition. An XRD analysis showed that they have crystalline fraction of 32% with Va-type structure, and have no tendency to undergo retrogradation. The tensile strength, Young’s modulus, and toughness of thermoplastic starch increased by the incorporation of the crosslinked starch/chitosan microparticles by melt-mixing. Toughness increased 360% in relation to unfilled thermoplastic starch.Microparticles of corn starch and chitosan crosslinked with glutaraldehyde, produced by the solvent exchange technique, are studied as reinforcement fillers for thermoplastic corn starch plasticized with glycerol. The presence of 10%  Environmental concerns have increased public awareness over the use and disposal of common petroleum-based plastics, especially those used in manufacturing short-lifetime products, like disposable eating utensils, food packaging, bags, and so on. ,2,3. Biod-glucose units linked by (1→4) bonds, and amylopectin, a highly branched polymer of d-glucose units linked by (1→4) bonds, branched with (1→6) linkages at intervals of approximately 20 units  degree of acetylation 90%) from Golden-Shell Pharmaceutical Co., Ltd. , glutaraldehyde solution (25% in Hw/w starch and 10% w/w chitosan) was added to 25 mL of distilled water under mechanical agitation . To ensure complete dissolution, chitosan was added in the form of an acidic aqueous solution with 5% chitosan and 6% propionic acid. The aqueous mixture of the starch and chitosan was heated to 80 °C, the vessel’s external jacket was connected to a recirculating thermostatic bath for a period of 20 min, and was kept at that temperature for an additional 20 min, under mild agitation. The obtained gel was then cooled to 50 °C over a period of 1 h. Then, 100 mL of ethanol was added and the mechanical stirring increased to 700 rpm and was maintained for 15 min. A visibly agglomerated precipitate was formed. In order to promote deagglomeration, the dispersion was kept for 15 min under high shear, using an IKA T-18 Ultra-turrax  at 10,000 rpm. The precipitate was then filtered using qualitative filter paper  in a Buchner funnel under a vacuum, in order to remove the excess water. To promote crosslinking, the particles were re-suspended in 100 mL of 90% (v/v) ethanol and the dispersion was heated to 50 °C under mechanical stirring (500 rpm). Glutaraldehyde was added in different amounts, from 0 to 10 g per 100 g of polysaccharide, mixed with 0.5 mL of HCl 1 M, after which the dispersion was kept at 50 °C for 1 h. The resulting particles were filtered as described above and re-suspended in 100 mL of ethanol under mechanical stirring at 300 rpm for a period of 15 min, to remove any unreacted glutaraldehyde. The particles were finally filtered and dried to constant weight at 105 °C.The starch/chitosan microparticles (SCM) were prepared using the solvent exchange technique, adapting procedures previously described in the literature for microparticles composed solely of starch ,33. The The thermoplastic materials were prepared by melt mixing, using 30% glycerol as a plasticizer, as described by Carvalho et al. . The SCMfinalm is the final mass after drying and initialm is the initial mass of the particles, as follows:The water solubility and boiling water solubility of the SCM were evaluated in terms of the total soluble matter (TSM). Both tests were performed in an aqueous solution with two pH values (4 and 7). The water solubility was evaluated for a period of 24 h under constant magnetic stirring. The boiling water solubility was evaluated for a period of 1 h. Previously dried and weighted particles were used. After the conclusion of the tests, the liquid was filtered and the residue was dried to a constant weight. The TSM value, in a percentage, is given by Equation (1), where −1 with a resolution of 4 cm−1.An FTIR analysis of the native starch and microparticles was performed on a VERTEX 70 FTIR spectrometer  in absorbance mode, with a high sensitivity DLaTGS detector at room temperature. The samples were measured in ATR mode, with a A225/Q PLATINUM ATR Diamond crystal  with single reflection accessory. The spectra were recorded from 4000 to 500 cmλ = 1.540593 Å in Bragg-Brentano geometry. All of the samples were measured at room temperature over the range 2θ = 5–30° in rotation mode.X-ray diffraction (XRD) measurements were performed on native cornstarch granules and on SCM crosslinked with 7.5% glutaraldehyde, after one day and 30 days after production. The experiments were performed at the IFIMUP-IN facilities, in a Rigaku SmartLab diffractometer  that operates with 9 kW power (45 kV and 200 mA) and a Cu source with a wavelength hklD is considered on the direction perpendicular to the lattice planes, hkl is the Miller indices of the planes being analysed, λ is the wavelength of the source Cu Kα (λ = 1.5406 Å), hklB is the full-width at half-maximum (FWHM) of the principal peak, and (002) θ is the Bragg angle [The mean diameter of crystallite was calculated with the Debye-Scherrer equation, as follows: gg angle .TI is the total area under the intensity curve and AI is the area under the amorphous halo.The volumetric crystallinity fraction was determined accordingly to the following equation :(3) CrysThe FWHM/area determinations were performed using the free license Fityk software (version 0.9.8) .The morphological characterization of SCM was performed using a scanning electron microscope , FEI QUANTA 400 FEG ESEM/EDAX Pegasus X4M, property of CEMUP-Centro de Materiais da Universidade do Porto. The sample was placed on carbon tape and coated with a gold–palladium (Au-Pd) layer to ensure conductivity, and then analyzed at a voltage of 15 kV.−1, in aluminum pans under a nitrogen flow.The thermogravimetry analysis (TGA) of SCM was performed in a STA 449 F3 Jupiter . The samples weights were about 10 mg. The runs were carried out from 30 to 550 °C, at a rate of 10 K·minThe tensile tests of the composite thermoplastic starch specimens were performed in a Tinius Olsen H50KT universal tensile testing machine equipped with a load cell of 10 kN at crosshead speed of 2 mm/min, according to the standard of ISO 527-1. The tension tests were conducted at ambient conditions  on dog-bone shaped samples (80 mm × 11 mm × 2 mm), according to ISO 527-2. The ultimate tensile strength, percentage elongation at break, and tensile modulus values were recorded using the software Tinius Olsen Horizon, according to ISO 527-1.Water absorption of thermoplastic starch was evaluated as described by Prachayawarakorn et al. . The speThe use of dialdehydes, such as glutaraldehyde or glyoxal, as crosslinking agents for polysaccharides is commonly mentioned in the literature ,40,41. AThe SCMs prepared without the glutaraldehyde addition are completely soluble at both pH values. The physical interaction between the starch and chitosan did not prevent solubility, even at a neutral pH. However, the addition of crosslinker greatly decreased the solubility. For both room temperature and boiling water immersion tests, increasing the glutaraldehyde concentration up to 7.5% tends to increase the water resistance. A further increase to 10% does not improve the results. Therefore, 7.5% seems to be an appropriate value for attaining effective crosslinking. TSM is higher when SCM is in contact with boiling water, as expected for such extreme conditions. Somewhat unexpectedly, however, TSM tends to be higher for pH 7 than for pH 4. The opposite could have been predicted, considering that chitosan is soluble only under an acidic pH. However, reaction of aldehydes with amines is known to be catalyzed by acids. Therefore, the lower TSM observed at pH 4 is probably a consequence of further crosslinking taking place between residual unreacted glutaraldehyde and amino groups in chitosan. When chitosan is not used, the microparticles become completely soluble, despite of the presence of glutaraldehyde. This suggests that only chitosan’s amino groups, and not starch’s hydroxyl groups, intervene in the crosslinking process with the aldehyde.2 group (1650–1580 cm−1) are not present in the corresponding spectrum. As a consequence, the bands that would result from the reaction of chitosan with glutaraldehyde are also not detectable. Nonetheless, in order to demonstrate the feasibility of this chemical reaction under the conditions used for production of the microparticles, the same synthesis procedure was followed using chitosan alone, with and without the glutaraldehyde addition. The resulting spectra are also shown in −1, confirming the formation of the N=C linkage, originated by the reaction between an amine group from chitosan and an aldehyde group from glutaraldehyde. In addition, a small band around 1720 cm−1 is visible, which may be attributed to the unreacted aldehyde groups from glutaraldehyde. It must be noted that glutaraldehyde was already known to be an efficient crosslinker for chitosan [A FTIR analysis was performed on native starch and on starch microparticles containing 7.5% glutaraldehyde, both with and without the chitosan addition. The results are show in chitosan ,43. It cThermogravimetric curves were obtained for the native corn starch, chitosan, and SCM crosslinked with 7.5% glutaraldehyde. These are shown in All of the materials show an initial mass loss consistent with the desorption of water molecules, corresponding to roughly 6% of the original mass. The onset of thermal degradation for corn starch is 311 °C, which is consistent with the literature ,45. On tθ = 10–30°.X-ray diffraction measurements were performed on native cornstarch granules and on SCM crosslinked with 7.5% glutaraldehyde. For the SCM, the XRD spectra were obtained one day and 30 days after production, in order to evaluate the possible changes in the crystallinity over time. A 30-day period is considered sufficient for retrogradation to occur in starch molecules after gelatinization ,47,48. Fθ = 15.1°, 17.6°, and 23.0°, consistent with a crystalline structure with A-type polymorphism that is usually found in cereal starches [θ = 13.2° and 20.2°, resembling the Va-type microstructure [h-type crystals as a result of exposure to humidity. This process is called retrogradation and is associated with an increase in the brittleness of the starch material [h-type structure (2θ = 18.3°), indicating that the Va-type crystalline structure is maintained. Therefore, one may conclude that crosslinking with glutaraldehyde after gelatinization/precipitation induces the stability of the crystalline structure, hindering the retrogradation of starch chains. The mean diameter of the crystals was determined by the Debye-Scherrer equation (Equation (2)), and the fraction of crystallinity by peak area integration (Equation (3)). In native starch, the crystals presented diameters around 6.7 nm and a 55% crystallinity. The crosslinked particles presented a smaller crystal mean diameter and lower crystallinity of 4.1 nm and 32%, respectively, on day 1. On day 30, the crystal size and fraction of crystallinity were similar, as expected in the absence of retrogradation.Native corn starch exhibits diffraction peaks at 2starches ,50. The tructure ,51. Thismaterial ,53. The Thermoplastic starch samples containing different amounts of SCM crosslinked with 7.5% glutaraldehyde were produced by melt mixing followed by injection molding. Their mechanical performance was evaluated in stress-strain tests. The shape of the stress-strain curves indicates that linear elastic behavior is present only at low strains, and is followed by extensive ductile deformation, without a defined stress yield point. This behavior is typical of thermoplastic starches . Strain corr), that is, the UTS divided by the fraction of thermoplastic starch present in the sample. If the SCM acted as an inert filler, having no effect on mechanical reinforcement, UTS would decrease with the increasing SCM content, but the UTScorr should remain constant.The ultimate tensile strength (UTS) obtained for the thermoplastic starch samples is plotted in corr stabilizes, showing that no additional reinforcement is obtained by incrementing the amount of SCM. This is probably due to microparticle agglomeration within the thermoplastic starch matrix, which does not contribute to an increase in the interfacial stress transfer.The combined increase in rigidity, strength, and elongation at break for the SCM contents up to 30% leads to a significant improvement in toughness, computed as the area under the stress-strain curves. Toughness is about 360% higher for the 30% SCM content than for the neat thermoplastic starch, which translates into a much higher capacity to absorb energy without fracture. Above this filler content, all of the properties decrease, due to an inefficient dispersion of the microparticles, as mentioned before. Agglomerates behave as defects with lower cohesion than the rest of the material.The water absorption was measured along the time under 100% relative humidity for all of the materials . In all w/w chitosan is used, the soluble fraction becomes relatively low, tending to decrease as the crosslinker content is increased. For 7.5 g glutaraldehyde per 100 g of polysaccharide, the total soluble matter is lower than 10% after one day of immersion in water at room temperature, and lower than 20% after one hour in boiling water, even under acidic conditions that facilitate the chitosan dissolution. The presence of chitosan’s amino groups is therefore essential for a reaction with glutaraldehyde, forming an interpenetrating polymer network with starch chains. Thermogravimetry indicated that the microparticles’ thermal degradation behavior is coherent with their polysaccharide composition. An XRD analysis showed that the microparticles have 32% crystalline fraction with Va-type structure, and do not exhibit a tendency to undergo retrogradation.Microparticles composed of corn starch and chitosan were produced by solvent exchange and crosslinked with glutaraldehyde. The effectiveness of crosslinking was evaluated by measuring the fraction of soluble matter. The results showed that the microparticles are completely soluble when composed of only starch. When 10% The crosslinked starch/chitosan microparticles were melt-mixed with corn starch plasticized with glycerol and were able to reinforce its mechanical properties in terms of tensile strength, rigidity, and toughness. Contrary to what is usual, elongation at break also increases with the filler content, probably due to the microparticles being able to deform while providing an interfacial stress transfer. An optimum weight fraction of 30% was identified, leading to 58%, 87%, and 84% increases in ultimate tensile strength, Young’s modulus, and elongation at break, respectively. The materials toughness increased 360% in relation to unfilled thermoplastic starch.


The aim of this study was to investigate the influence of sodium caseinate, starch, lactose and lactic acid bacteria as ferment on the syneresis in a mixed model system, and to evaluate their impact on the acid gel formation throughout pH and zeta potential monitoring. Accordingly, a protocol was designed to perform an experimental design by using a mixture of the selected factorsA significant decrease of syneresis was detected in all mixtures at 8% of sodium caseinate, ranging between a minimum of 1.8% and a maximum of 20.6% compared to the mixtures at 3% of sodium caseinate in which the syneresis decrease had ranged between a minimum of 22.2% and a maximum of 47.8%. The addition of starch had a significant impact on the acidification profile and on the syneresis of the fermented mixed model. Moreover, the monitoring of pH and zeta potential during the lacto-fermentation process has also led to a better understanding of the acid gelation and the syneresis variations..Syneresis varies very closely with sodium caseinate concentration, starch concentration and also with their association, regardless of the concentrations of lactose and ferment. In fact syneresis could be reduced to an optimum level if a sodium caseinate-starch mixed system is employed: Less syneresis gels could be obtained at a sodium caseinate concentration above 5% if starch is used above 1% Fermented food products have a great nutritional value and beneficial effect on host gut micro-biome due to their probiotics bacteria, which have a positive impact on human health . In factMilk, is a natural source of human nutrition and functional food materials that serves as the raw material for the production of dairy products, but also used as foodstuffs in their own right and as ingredients in the manufacture of formulated and processed foods: proteins, lactose, water-soluble vitamins and minerals. Milk proteins can be divided into two species: casein and whey proteins. The beneficial health properties resulting from the consumption of fermented milks have been known for many years. Fermented dairy products are usually made from standardized milk with fat content of 0–3.5% and non fat milk solid content of 11–13% through acidification with lactic acid bacteria. Among the different process parameters such as heat treatment, fermentation, storage conditions and starter culture, milk base composition has a big impact on the gel structure formation.β-lacto-globulin-κ-casein interactions, which improve the texture of the product and reduce syneresis [To improve and optimize the properties and the nutritional qualities of fermented dairy and non-dairy products, a variety of ingredients are used on both the process and the composition. It was also reported that casein-based products tended to produce firmer gels with less syneresis than yogurts fortified with whey protein . Damin eyneresis . Conventyneresis . Consumeyneresis . TherefoThe matrix or the gel formation is a key property in the acid induced gel formulation, in which casein plays a major role due to its structure properties . It is aLactobacillus delbrueckii ssp. Bulgaricus combined to streptococcus thermophilus [The purpose of this study was to use sodium-caseinate as the main substrate to create physical building blocks within the continuous phase that may substitute the functionality of the removed milk ingredients and to produce an acid gel-network capable of holding the serum phase with an optimal stability. Accordingly, an experimental design was proposed by using a mixture design of the following selected factors: sodium caseinate, starch, lactose and lactic acid bacteria to determine the effect of these formulations on the syneresis of the formed acid gel protein network. The effects of these formulation variables on syneresis were evaluated through pH and zeta potential monitoring. Fermentation was performed with lactic acid bacteria mophilus , 24. ThiThis study demonstrated that sodium caseinate considerably decreased syneresis in the fermented model system compared to starch while ferment and lactose did not affect significantly syneresis. Moreover, the effect of starch on the acidification profile was revealed as an important element to consider in the understanding of the impact of reduced fermentation time on syneresis. It could be concluded from our trials that the optimum syneresis was achieved when the model bases were sodium caseinate and starch. Based on our results, we also showed that syneresis in the proposed model gel system could be reduced to an optimum level if a sodium caseinate-starch mixed system was employed: Less syneresis gels could be obtained at a sodium caseinate concentration above 5% if starch was used above 1%. Therefore, it will be worthwhile to make an exhaustive study integrating the most convenient combination of the selected factors and different steps of yogurt like manufacturing.Strep. Thermophilus and Lactobacillus delbrueckii ssp. Bulgaricus (provided by Trade Bio-Industries Morocco). Freshly distilled water was used for preparation of all samples.The main ingredients used in this study were: Sodium caseinate (provided by Trade Bio-Industries Morocco); Native starch ; Lactose (LOBAL Chemie Laboratory) and Lactic acid bacteria: commercial yogurt starter culture freeze-dried containing The main equipment used in this study was: Water bath set at 42 °C (Memmert WNE14); Fine test sieve (mesh width 0.25 mm); pH meter (Inlab sensor dairy Electrode. Mettler Toledo); Nanosizer ZS analyzer  and Design Expert 10 software edited by Stat-Ease inc.The ranges of the studied factors proposed for the determination of functional design space, showed in Table To define the formulation space for sodium-caseinate mixtures, a Complete Factorial Plan performed with Design Expert was selected to evaluate and to model the effects of sodium caseinate, Lactose, ferment and starch on decreasing the syneresis of the fermented product. This provides maximum information from a limited number of experiments as indicated in Table Each of the nineteen formulas has been prepared as follows: Lactose was dissolved in pure distilled water; sodium-caseinate was added slightly under agitation at 250 rpm, and all heated in a bath at 70 °C to obtain a homogeneous solution. When starch was required, it was added once the glass beaker was removed from the water bath and cooled at the ambient temperature. The final mixture was water bath heated at 82–85 °C for 30 min to pasteurize and to allow hydration and swelling of starch granules. Samples were then cooled to 42 °C, which is the incubation temperature that is recommended for the starter culture, and inoculated with different doses of ferments. All these mixtures were poured into glass-jars and placed in a water-bath heated at 42 °C for 5–6 h. All samples were then stored at 4–5 °C for 24 h before proceeding to the evaluation of their syneresis. The analysis was carried out for 24 h. The volumes of the whey released by the fermented samples were measured by inverting each sample on fine test sieve (mesh width 0.25 mm) that was placed on top of a beaker. The volume of whey drained was collected in a graduated cylinder for each sampling time within 24 h weighed and recorded as index of syneresis (%) .In this experiment, the design was adapted to a fixed dry matter. The concentration of the ferment was fixed to 0.35 g/l and the dry matter content of all samples was set at 12% with respect to the same proportions used in the experimental design 1 as showed in Table The purpose of this experiment was to evaluate the impact of starch and lactose on the acidification profile of the fermented mixed model system. According to the syneresis results of the second experimental design Table , four saThe selection of samples for zeta potential monitoring was based on the variation of the acidification profile observed in the four samples during their pH monitoring. The Samples that exhibited the shortest (sample i) and the longest (sample iv) time of gelation were selected. Zeta potential measurements were determined on dilute dispersions of each sample, the type of diluent greatly influenced the obtained results. Samples were diluted 100-fold with ultra pure distilled water before conducting the measurement while dilution with buffer solutions was unsuccessful.design of experiments (DOE). In order to reduce scattering effects and to compare the samples, all physical results had been normalised. The Residual analysis, the coefficient of determination (adjusted R2), the significance of the models and the lack of fit were used to check the quality of the model. The robustness of the models was evaluated by determining the squared correlation coefficient (R2) for predicted versus measured values in cross-validation and the ratio of standard deviation (SD) to root mean square error of calibration (RMSEC) of the data sets. The ratio of the SD to the RMSEP, called the ratio of prediction to deviation (RPD), is the factor, by which the prediction accuracy has been increased compared to using the mean composition for all samples. This ratio is desired to be larger than 2 for a good calibration. An RPD ratio less than 1.5 indicates poor predictions and the model cannot be used for further prediction.All statistical analyses were performed using the Design Expert version 10, a statistical software package from Stat-Ease Inc. that is specifically dedicated to performing Casein gels are dynamic by nature . The excThe first global experimentation with Design Expert 10 software showed a large variations ranging between a minimum of 1.8% when all factors were at their high level and a maximum of 47.8% when they were at their low level as showed in Table The separate influence of the four parameters also showed the dominant influence of A & B factors Fig. . The SynThe significant impact of starch on syneresis confirms the fact that starches along with other hydrocolloids have been evaluated in yoghurt formulations as ways to achieve a desired viscosity and reduce syneresis . The ranTo understand the effect of these formulation variables on syneresis, and their impact on the quality characteristics of the formed acid gel, a pH and zeta potential monitoring of four designed samples were conducted for controlling the fermentation process and predicting the physical properties of the final product in terms of acidification and ionic strength. Based on our results, syneresis was reduced when sodium caseinate concentration was used above 5%. Therefore, to evaluate starch and lactose impact on the acidification process all samples were designed to be set at 5% of sodium caseinate, while lactose and starch concentrations ranged between 4–9%) and (0–2%) respectively as indicated in Table –9% and . The difFurthermore, the two samples showing the highest and the shortest acidification profile were zeta potential monitored. This later had been used as an indicator of the electrical charge of caseinates’ particles to measure the strength of repulsion or attraction between particles in both samples along the fermentation process. It was observed that the zeta potential decreased in both samples during acidification as showed in Fig. In fact as the pH decreased, the proteins charges got neutralized, which minimized their electrostatic and steric forces inducing a new arrangement of micelles. Consequently, the system became unstable earlier in the sample with starch and tended to flocculation because of the less repulsive forces with the DLVO classical theory . Therefo