Olea europaea L.) are a high-value food commodity with an increasing production trend over the past two decades. In an attempt to prevent fraud issues and ensure quality, the International Olive Council (IOC) issued guidelines for their sensory evaluation. However, certain varieties, geographical origins and processing parameters are omitted. The aim of the present study was the development of a method for the quality assessment of edible olives from the Konservolia, Kalamon and Chalkidikis cultivars from different areas of Greece processed with the Spanish or Greek method. A rapid NMR-based untargeted metabolic profiling method was developed along with multivariate analysis (MVA) and applied for the first time in edible olives’ analysis complemented by the aid of statistical total correlation spectroscopy (STOCSY). Specific biomarkers, related to the classification of olives based on different treatments, cultivars and geographical origin, were identified. STOCSY proved to be a valuable aid towards the assignment of biomarkers, a bottleneck in untargeted metabolomic approaches.Edible olive drupes (from  Olea europaea L.). Olives, based on cultivar, can either be used for the production of the well-known and high-valued olive oil or undergo processing and become an edible product. Consumption of edible olives worldwide has doubled since the year 2000, reaching a provisional 2.7 million tons in 2019, as more people gradually adopt the Mediterranean diet [The olive fruit is a drupe, oval in shape, produced by the olive tree  and exogenous  factors . These pHowever, geographical origin, processing and cultivar fraud issues require additional analyses to be determined. For instance, the significantly less time needed for the Spanish processing method could make it a preferable option against the Greek one, even in black olives. The drastic decrease in the content of nutritional compounds of the processed product is nevertheless not in the best interest of the consumer ,18,19,20In most cases, when quality aspects are implicated, analytical methods and techniques are employed to resolve given issues by suggesting certain methodologies or quality markers ,23. GeneAside from the aforementioned established method of sensory analysis, GC-MS with multivariate analysis (MVA) has been used to determine spoilage issues, where volatile organic derivatives are identified as biomarkers , or to dPaeonia mascula [Cinchona sp. [Isatis tinctorial [Keetia sp. [Furthermore, regardless of the analytical method used, a critical bottleneck in metabolic profiling methods is the identification of relevant compounds that could serve as quality markers. Indeed, one of the most critical steps in metabolomics approaches involving food (foodomics) and natural products in general is the identification of relevant biomarkers, mainly due to the lack of databases and the frequent absence of reference standards, as well as the complexity, high variability and unexpected nature of natural matrices. Thus, statistical techniques and tools have been suggested to assist with this issue . Among t mascula , Cinchonhona sp. , Isatis nctorial  and Keetetia sp. , and a cetia sp. ,49,50, wetia sp. .Consequently, the objective of this study was the development of an NMR-based metabolic profiling method to map edible olives, but also to confront mislabeling and fraud. Chemometrics were employed for the identification of certain marker compounds, which were verified via STOCSY, a tool used for the first time in edible olive analysis.Edible or table olives constitute, directly after olive oil, valuable products mainly for Mediterranean countries, such as Greece. Nevertheless, there are limited studies that report the mapping of Greek edible olives or investigate quality aspects and authentication issues. In Greece, there are approximately fifteen varieties, not all intended for table use, which mainly originate from specific areas of the territory. Primary edible varieties are Kalamon, Chalkidikis, Konservolia and Throumbolia ,53. ThusAll samples were stored, preserved and extracted under the same conditions prior to analysis in order to minimize any induced variability. Furthermore, complete information accompanying each sample (metadata) was registered .in-house developed protocol. Considerable attention was given to parameters such as the selection of the drying process, extraction solvents and yield, required quantity for analysis, internal standard (IS) and deuterated solvent used (data not shown).Over the last several years, NMR is increasingly being introduced in the quality control of foods, offering important advantages, i.e., speed, reproducibility and simplicity ,55. To td4 with Hexamethyldisiloxane (HMDSO) as an IS and a line-shape indicator for the recorded spectra. A total of 60 spectra were acquired using a simple zg pulse sequence.Samples were dissolved in methanol-Representative spectra from two varieties  are given in Due to pH variances, minor shifts were observed in certain peaks running through the spectra. To that end, the icoshift tool in the MATLAB suite was applied with specifically chosen intervals for the alignment of all spectra. IS normalization was applied to eliminate any induced variability originating from sample preparation and acquisition of the spectra. The datasheet was imported into SIMCA v. 14.1 to perform MVA. Geographical origin, tree cultivar and processing type were the studied classes characterizing the observations (samples).In order to form the first visual opinion of the data, detect any inherent pattern within them and determine possible outliers, principle component analysis (PCA) was performed. In the current study, Pareto scaling along with logarithmic transformation were used to construct the statistical models, as weight adjustment was mandatory for major metabolites. As a next step, partial least squares discriminant analysis (PLS-DA) was performed using the same datasheet of variables. Finally, orthogonal partial least squares projections to latent structures with discriminant analysis (OPLS-DA) models were built by taking groups in pairs, in order to identify certain markers responsible for their differentiation. Outliers were not detected in any built model.Three regions with the highest production in Greece—Peloponnese (south), Sterea Ellada (center) and Makedonia (north)—were used for sample collection to study the impact that geographical origin might have on the samples’ nature. Firstly, in the PCA model , four fi2 and Q2 values verified the goodness of fit and predictivity of the models, with the issue between Sterea Ellada and Peloponnese revealing once more the similarities between these samples. To facilitate model interpretation, we constructed the respective S-plot to each OPLS-DA model, that provides visualization of its predictive component loading. However, the use of variable importance for the projection (VIP) values of the features of each OPLS-DA loadings plot was what actually led to the selection of the most relevant features. Features with a VIP > 1 and a larger effect in class discrimination were selected , where they remain for 4–15 h until the solution penetrates the skin and approaches the pit, pushing the mutation of the chemical profile in a more forceful manner [Concerning the processing procedure, edible olives processed with either the Greek or the Spanish method are represented in this study. As mentioned before, the Greek method is a more natural way of processing mainly purple or black olives that is comprised of brining and fermentation, whereas the Spanish includes an initial additional step of lye treatment and is used mostly for green olives. During lye treatment, olives are placed in large tanks containing sodium hydroxide solution  was part of the experimental protocol.As already mentioned, a really critical step in untargeted metabolic profiling approaches is the mining of relevant features followed by their translation into compounds, which could function as possible markers. Nevertheless, the identification process which commonly involves the dereplication of known compounds is a challenging task . Thus, iHT) (Tyr), was identified  a. Using entified b. HT andproducts ,67,68.HT and Tyr, respectively, and the two methylene groups of the side-chain for each compound. The two molecules present strong structural similarities, with HT being a hydroxylated derivative of Tyr, which is reflected in the pseudo-spectra as well. It is important to note that both multiplicity and peak integration are also apparent and in accordance with their reference 1H NMR spectra  is considered a minor compound in edible olives [The same workflow was followed for verbascoside or acteoside, a caffeoyl phenylethanoid glycoside. It is a structurally complex molecule, which is composed of a disaccharide formed by a glucose and a rhamnose unit, to which caffeic acid and e olives , found ae olives . HoweverVer, two flavonoids, Quercetin (Quer) and Luteolin (Lut), were also identified. Both compounds are also minor constituents of table olives with Quer present in lower concentrations compared with Lut, after hydrolysis of the respective glucosides due to processing [Similar to ocessing . FurtherOA) and Maslinic Acid (MA). A wide range of biological activities has been linked with them over time, like antitumor, antidiabetic, antioxidant, cardioprotective, antiparasitic and even neuroprotective [MA being a hydroxylated derivative of OA without any other structural differences. It is worth mentioning that in the generated pseudo-spectrum, all methyl protons, which resonate between 0.7 and 1.2 ppm, were clearly revealed, showing in this case too the average relative concentration of these compounds in the analyzed samples  constitute typical metabolites of processed table olives. Specifically, in the samples at hand, Formic (FA), Acetic (AA), Propionic (PA) and Lactic (LA) Acids accompanied by Succinic Acid (SA) had their 1H NMR spectra clearly projected through STOCSY. Their correlation spectra are truly indicative, enabling their identification with high confidence  was generated using the peak at 5.27 ppm as a driver. Mainly TAGs, but also diacylglycerols (DAGs) and free fatty acids (FFAs) are highly abundant in drupes [Short-chain fatty acids .In parallel, a targeted approach, based on the literature search, led to the identification of linoleic acid and glycerol . An exteFraudulent practices in edible olives are numerous with the common denominator being adulteration and mislabeling. Organoleptic assessment suggested by the IOC, even though it provides a somewhat quality classification based on sensory attributes, cannot identify differences in other quality parameters, such as geographical origin, cultivar or processing method. Thus, the identification of specific markers or patterns indicative of these traits would be of utmost importance in the quality assessment and authentication of edible olives. In the current study, special attention has been given to the identification of specific measurable chemical markers indicative of analyzed cultivars or geographical origins, as well as processing methods supporting Greek PDO and PGI products.HT and Tyr, the two main phenylethanoids found in processed edible olives, have been extensively investigated for their biological and pharmacological properties [HT and its derivatives were correlated with a protective effect over blood lipids from oxidation resulting in cardio-protection for the consumer. The health claim concerns olive oils with certain concentration levels of these compounds [Thus, following the initial experimental design, our team, in turn, focused on the identified and statistically significant compounds by monitoring their relevant concentration in the different classes. operties ,80. Wortompounds . TherefoHT and Tyr, compared with Konservolia, the other black olive variety of the present study, agreeing with the literature [HT, whereas no effect was reported in the levels of Tyr. The respective t-test applied in this study reveals the possible significance of HT with a p value of 0.06, while the respective value for Tyr was 0.82, showing no statistical significance . Moreover, Ver was detected in Peloponnese samples and in lower levels in Sterea Ellada, while being absent in samples from Makedonia , as not even traces were found in samples from Makedonia and the Chalkidikis cultivar .A total of 10 olives per sample were picked randomly from the ones provided by the producers. They were lyophilized until the total loss of water content. Afterwards, pits were removed and the flesh was turned into powder manually.v/v) , was added as an extractant solution. The mixture was shaken for exactly 1.5 min in a vortex agitator . Then, the tube was placed in the ultrasonic bath for 15 min at room temperature (RT) and centrifuged  at 4000 rpm for 25 min at RT. The supernatant was filtered with a 5 mL plastic syringe through a 0.45 μm PVDF filter and transferred to a new tube . A glass syringe of 2.5 mL,  was used to transfer 15 mL of the extract to a glass spherical flask and evaporate to dryness with a RotaVapor at 40 °C under vacuum . The solid extract was transferred to a 2 mL eppendorf  and dried using a centrifugal evaporator with vacuum . Samples, after their extraction, were stored in a freezer at −20 °C until analysis.Official extraction protocols for edible olive analysis have yet to be issued by the IOC. Consequently, the respective official one for olive oil analysis was used in this study and developed to match the different nature of the samples . Specifid4  with 0.02% v/v HMDSO  as an IS and a line-shape indicator was prepared. Each sample was dissolved within the eppendorf in 700 μL of the deuterated stock solution, sonicated for 30 s and centrifuged  at 14,000 rpm for 5 min. The supernatant (550 μL) was transferred via a 1-mL Hamilton glass syringe and placed in a 5 mm NMR tube (D400-5-7) with a PTFE cap .A stock solution of methanol-1H NMR experiments were recorded at 305 K on a Bruker AVANCE III 600 NMR spectrometer  operating at the proton frequency of 600.13 MHz (B0 = 14.1 T) and equipped with a z-gradient inverse detection 5-mm probe and a BCU for temperature control. Spectra were recorded with the help of a 60-place sample changer (B-ACS-60), using the IconNMR automation software by Bruker. The following conditions were used for the acquisition: number of scans, 64; π/2 pulse, ~8 μs; time domain (TD), 64k data points; acquisition time, 2.73 s; relaxation delay, 3.17 s; spectral width, 12019.2 Hz; and mixing time, 0.060 s. The spectra were obtained by the Fourier transformation (FT) of the free induction decay (FID) by applying an exponential multiplication with a line-broadening factor (lb) of 0.3 Hz and zero-filling (size = 256K) procedure. Resulting spectra were manually phased and baseline-corrected using a polynomial function in the Bruker TopSpin software (version 4.0.6). Chemical shifts were reported with respect to the IS’s signal set at 0 ppm.in-house routine and the area of the IS’s peak was used as a reference value. Solvent signals  and the IS signal, as well as the region from 8.5 to 9.6 ppm were removed for MVA. In particular, bins of the last segment mentioned were summed together as there were only minor peaks surfacing from the noise. As a result, 753 variables were obtained after data processing in MATLAB.NMR raw data were inserted in the MATLAB suite (version R2018b) for further processing. Initially, spectra were aligned using the icoshift tool (Version 3.0 beta) and a targeted selection of intervals optimized depending on the sample type. NMR spectra  were segmented into 1,111 bins with a bin size of 0.01 ppm for MVA and into 11,101 bins with a bin size of 0.001 ppm for the STOCSY experiments. Data were normalized using an k is the standard deviation of the k variable. With mean-centering, the average value of each variable is calculated and then subtracted from the data, hence improving the interpretability of the model.Data with the 0.01 ppm bin size were extracted and imported into SIMCA v. 14.1 , where they were subjected to MVA and specifically the PCA, PLS-DA and OPLS-DA methods of analysis . Prior tin-house routine in the MATLAB suite. In theory, given that the spectrometer conditions are kept identical between samples, because the different resonance intensities from a single molecule will always have the same ratio, the relative intensities will be totally correlated. STOCSY utilizes this principle to generate a pseudo-NMR spectrum that displays the correlation among the intensities of various peaks across the entire sample [Data with the 0.001 ppm bin size were used for the 1D STOCSY analyses with an e sample . SpecifiCorrelation analysis and heat maps were performed for data visualization using MetaboAnalyst 4.0. A “warm-to-cool” color spectrum was used in order to reveal the metabolic alterations of the selected metabolites in the samples set in a column hierarchical cluster structure of the data matrix .Edible olives, a valuable element of the Mediterranean diet, keep rising as a nutritional choice worldwide, while the level of fraud has always been an issue in the food industry. From geographical origin to variety and processing method, quality should be a prerequisite for the last border, the consumer. The protocols issued by the IOC so far only cover the important, yet inadequate, sensory aspect. In the current study, NMR-based metabolomics was combined with MVA for the first time in the quality assessment of edible olives. Several markers were successfully identified for the discrimination between samples with a different geographical origin, variety and processing method. Furthermore, the—novel in natural products—tool of STOCSY was applied, revealing certain peak correlations, which in collaboration with the literature led to an unprecedented peak assignment for these compounds in the total extract of such samples. This work is an important contribution to the confrontation of food mislabeling, intentional or accidental. More samples from different varieties, regions and processing methods should be analyzed to expand the versatility of the models created and more unknown compounds have to be identified in the future.


Before and after the shift-work cycle, maximum voluntary contraction (MVC) force and muscle activation, neuromuscular control, and muscle fatigability were measured in the finger flexor muscles. Activity level and sleep quality during the shift-work cycle were recorded with a wrist actigraph. After the shift-work cycles, MVC force and muscle activation were decreased  as was neuromuscular control , whereas muscle fatigability was increased  in the NS5 and the NS10 group. Sleep quality was lower in the NS5 and the NS10 group , while the activity level for the three groups was similar. There was a clear reduction in neuromuscular control and an increase in muscle fatigue in the nurses working the night shift. These findings may inform of work schedule planning or recommendations for devising new recovery strategies to counteract neuromuscular alterations in night shift nurses.The effect of sleep–wake rhythm disruption on neuromuscular control and muscle fatigue has received little attention. Because nurse shift work is so varied, including overnight duty, rotating shift schedules, early awakening, and interrupted nocturnal sleep, it offers an interesting model to study this paradigm. It has been investigated so far using only subjective markers. A combined approach based on the simultaneous analysis of surface electromyographic (sEMG) and force signals can objectively detect possible deficits in neuromuscular control and muscle fatigue. With this study we investigated neuromuscular activation and muscle contraction capacity at submaximum and maximum level in nurses working two night-shift schedules and compared them to levels in nurses working entirely in day shifts. Sleep quality and activity levels were also assessed. The study sample was 71 nurses grouped by their shift work schedule: night shift for 5 days (NS Hospital organizations operate around the clock and so must rotate staff between day shifts and night shifts. But because night-shift workers routinely work against their internal biological clock, they can suffer from sleepiness caused by desynchronization of their internal circadian clock and disturbance in their sleep-wake pattern. Nursing staff work shifts to provide continuous 24 h care . Nurse sA leading cause of shift-work disorders is the decrease in sleep quantity and quality. Many biological functions besides sleep are regulated by circadian rhythms and their expression can differ across individuals. Circadian preferences are governed by internal factors  and environmental factors  . In humaLiterature exists on the relationship between sleep restriction/deprivation, muscle force generating capacity and fatigue, with often conflicting findings: on one hand, no changes in maximum force were retrieved after one night of sleep deprivation , on the With this in mind, we investigated neuromuscular activation and submaximum and maximum muscle contraction capacity in nurses working two different night-shift schedules and compared them to data from nurses working entirely day shifts. Sleep quality and activity levels were also assessed. Our hypothesis was that we would find larger deficits in force generation capacity and in neuromuscular control possibly associated with poor sleep quality in the nurses working night shifts.ClinicalTrials.gov (registration number: NCT03453398), and conducted in accordance with the ethical standards of the latest Helsinki Declaration.The study involved the nursing staff of the Galeazzi Orthopedic Institute . The inclusion criterion was continuity of shift work cycle for at least 1 year. Exclusion criteria were: no present cardiovascular, endocrine, and neuromuscular disease, no pharmacological therapy potentially affecting sleep quality, and no pregnancy (self-reported). Ninety-one of the potentially eligible 120 nurses met the study criteria. After receiving a full explanation of the scope of the study and the possible cost/benefit ratio, 71 nurses provided written informed consent to participate in the study. They were free to withdraw from the study at any time. 5)]; 24 h shift, with shifts changing every 2 days ; and only day/swing shift (DS) for 5 days (from 7.00 to 14.00 on 1 day and from 14.00 to 21.00 the next day) followed by 2 days of rest. A total of 46 participants composed the NS5, 9 the NS10, and 16 the DS group  for assessment of physical activity .5 and the NS10 group had worked for 48 h equally divided in morning, swing, and night shifts. Differently, the DS group worked entirely in morning and swing shifts. All participants underwent actigraphic monitoring to assess their physical activity level and sleep quality. They wore a wrist actigraph the day of the first force test and removed it the day of the second force test, i.e., at the end of the work shift. They were instructed to wear the actigraph on the non-dominant arm all day and to remove it only when bathing, swimming, or engaged in combat sports.On a different day, maximum and submaximal force tests were conducted immediately before the participants entered their work-shift cycle  and after the NSDuring the monitoring period, the participants compiled a sleep diary in which they recorded the hours they went to bed, fell asleep, arose, and any time intervals during which they were not wearing the actigraph. During monitoring, they carried out their routine work and maintained their usual daily activity and their habitual food and beverage intake. In addition, we allowed participants to consume caffeinated or other energetic drinks not to alter their habitual behavior, especially during night work.inactive, METs 701-2519 sufficiently active, and METs > 2520 active.Assessment of physical activity. The Italian version of the IPAQ questionnaire comprises four sets of items that investigate four different aspects of the level of physical activity in relation to health. The participants were asked to record the total amount of time (min) per week they spent performing activities of high and moderate intensity, walking, and sitting. The minutes devoted to each activity were multiplied by a coefficient expressed as metabolic equivalents (MET): METs < 700 indicates Maximum and submaximal force tests. Force and surface electromyographic (sEMG) signals were recorded during isometric contractions from the finger flexor muscles of the dominant arm. The force tests were conducted on a purposely designed ergometer  consistiflexor digitorum superficialis and profundus) by two round Ag/AgCl electrodes with solid hydrogel . The electrodes were fixed to a probe  that wirelessly transmitted the sEMG signal from the subject to the acquisition base . The skin area under the sEMG electrodes was shaved, cleaned with ethyl alcohol, abraded gently with fine sandpaper, and prepared with a conductive cream  to achieve an interelectrode impedance < 2000 Ω. Following the European Recommendations for Surface Electromyography  at all levels of effort with the aid of the visual feedback (target force displayed on the computer screen). Force and sEMG signals were recorded during each contraction and synchronized via a digital push-button. On measurements completion, the sEMG electrodes position was marked on a transparency sheet, together with some skin landmarks  to minimize site-to-site variability in sEMG signals during repeated measurements on different days, permitting a similar repositioning of the electrodes, which were placed by the same experienced operator. This approach was shown to provide very high reliability in sEMG data .The sEMG signal was analyzed in time and frequency domain: the root mean square (RMS) and the mean frequency (MF) were then calculated in the central 1 s of the MVC force, in the middle of the force plateau. MF of the sEMG signal was calculated by means of the Fast Fourier Transform approach, which was implemented in the sEMG signal acquisition software. sEMG RMS, and MF were calculated in continuous 250 ms time windows during submaximal contractions. The values were normalized for the sEMG RMS and MF recorded during the first s of contraction. The temporal trend of the two parameters was used as a marker to monitor possible changes in neuromuscular activation level of the finger flexors muscles. In particular, the changes over time of the sEMG RMS and MF at 80%MVC force were used as markers of myoelectric fatigue manifestation .vice versa. CV and DT over time provided an index of muscle contraction stability and accuracy, respectively  was estimated by calculating the ratio (expressed as a percentage) between the standard deviation of the force samples and their average in the time window. The distance of the force signal from the target (DT) was calculated in the same time windows as CV. DT was expressed as a percentage to permit the comparison of these variables among participants. Negative DT values described a force output lower than force target and ectively . The staAssessment of activity levels and sleep quality. An actigraph equipped with a triaxial accelerometer  was used to record activity levels and sleep quality parameters. The Motion Ware software 1.2.28 (CamNtech) was used to process the activity data, expressed in activity counts, and recorded every 30 s throughout the monitoring period. The data were processed to measure the activity levels of each participant.Time in bed, total time spent in bed between bed time and get up time; (ii) Assumed sleep time, time between the beginning and the end of sleep; (iii) Actual sleep time, the amount of time between sleep Start and sleep End; this is determined by summation of the number of epochs below the sensitivity threshold and then multiplying that value by the epoch length in min; (iv) Sleep Efficiency, the percentage of time in bed actually spent sleeping; (iv) Sleep Latency, the amount of time required for sleep onset after retiring to bed; the period between bed time and sleep start is automatically calculated by an algorithm based on lack of movement; (v) Movement and Fragmentation Index, the addition of the movement index (% time spent moving) and fragmentation index (% of immobile phases lasting 1 min). The Movement and Fragmentation Index was used as an index of restlessness. For all these parameters, an average of the entire monitoring period was calculated for each participant. Moreover, a CR-10 scale  was used to assess the level of perceived tiredness in all the participants, before and after the monitoring period.The software for sleep analysis  allowed us to extrapolate six parameters indicative of sleep quantity and quality: (i) 5, NS10, and DS) using two-way repeated-measure ANOVA. Three-way repeated-measure ANOVA was used to calculate the differences over time (20 s contraction) in CV, DF, sEMG RMS, and sEMG MF between the three groups, before and after monitoring. Multiple comparisons were performed using Bonferroni’s correction. Significance was set at a p < 0.05. Changes are reported as a percentage change with 95% of confidence interval (95% CI). Cohen’s d effect size (ES) was calculated and interpreted as follows: trivial 0.00–0.19; small 0.20–0.59; moderate 0.60–1.19; large 1.20–1.99; very large ≥ 2.00  < 0.1; low 0.10–0.30; moderate 0.31–0.50; high 0.51–0.70; very high 0.71–0.90; nearly perfect 0.91–0.99; and perfect for R = 1 . The Shapiro-Wilk test was applied to check normal distribution of the sampling. The between-group differences in the level of daily activity and sleep quality parameters were calculated by one-way analysis of variance (ANOVA) or by equivalent non-parametric Kruskal-Wallis one-way ANOVA on the ranks test if the sample data did not have a normal distribution. The differences in MVC force, sEMG RMS, sEMG MF, and CR-10 scale before and after the monitoring period were calculated for the three groups (NSe ≥ 2.00 . The 95%or R = 1 . Unless 5 = 5219 (887) METS/w, NS10 = 4450 (1069) METS/w, DS = 5526 (1245) METS/w, F = 0.10, p = 0.903]. In detail, 26 were classified active, 12 sufficiently active, and 8 inactive in the NS5 group; 6 were classified active, 3 sufficiently active, and 0 inactive in the NS10 group; and 11 were classified active, 4 sufficiently active, and 1 inactive in the DS group.Level of physical activity (IPAQ SF questionnaire responses). ANOVA revealed no differences between the groups for the physical activity level assessed before the work shifts , sEMG RMS , and sEMG MF .Maximum and submaximum contraction. F = from 5.35 to 12.77, p = from 0.007 to < 0.001) and sEMG RMS . Moreover, after an initial decrease in CV in all groups after the first 2 s , CV remained stable until the end of contraction, without differences in time and between groups. Unlike DS, DT significantly increased after the monitoring period in the NS5 and the NS10 group for all submaximum contractions . Post hoc analysis revealed significant increases in sEMG RMS in the NS5 and the NS10 but not in the DS group after monitoring for contractions at 60% MVC force from the 9th and 8th s of contraction until the end of the task . At 80% MVC force, the same pattern was found from the 12th s of contraction until the end . No changes were observed for sEMG MF. In summary, CV and the sEMG MF over time remained similar among groups after the monitoring period; DT was reduced (force target underestimation) in NS5 and NS10 compared to DS; sEMG RMS behavior over time was higher during submaximal contraction at 60 and 80% MVC force.5 and the NS10 vs. the DS group for time in bed , assumed sleep time , actual sleep time , sleep efficiency , and in sleep latency  . Post hoc analysis revealed no between−groups differences in CR−10 scale values before the monitoring period , while they significantly increased after the monitoring period in NS5 and NS10 but not in DS .Daily activity levels and sleep parameters during the monitoring period. Activity levels were similar for the three groups . Except 6/2.55)] . ANOVA rR ranging from −0.201 to 0.041, p from 0.095 to 0.873).Correlations. With this study we investigated the effects of two different night shift schedules compared to only day/swing shift schedule on neuromuscular activation and muscle contraction during non-fatiguing and fatiguing contractions. The main findings demonstrated that, irrespective of the shift schedule, working night shifts: (i) affected both muscle maximum activation and maximum force generating capacity; (ii) reduced force accuracy when the muscle was involved in tasks requiring submaximum force output; and (iii) increased the occurrence of neuromuscular fatigue. All these findings are accompanied by an overall decrease in both sleep quantity and quality. Interestingly, the percentage reduction in muscle maximum activation and force correlated with the sleep quality parameters describing the total amount sleep time and its quality. Although the correlations reported here present a low (but significant) correlation coefficient, they indicate that the nurses reporting low sleep quality had the greatest decrease in force generation capacity. These aspects need to be considered when planning work schedules.Our data show a clear reduction in maximum activation and muscle force output of the finger flexor muscles for two night shift groups compared to the day/swing shift group. Moreover, muscle function was correlated with sleep quality measured after different shift cycles. This reduction in MVC force suggests that working nights impaired maximum muscle activation and maximum force generating capacity, as demonstrated by the reduction in EMG RMS and EMG MF during MVC force after night shifts. During sustained contractions at 80% MVC force, a significant increase in EMG RMS was observed in the NS groups compared to the DS group. Such an increase is typically considered a marker of myoelectric fatigue manifestation . Indeed,Together with increased muscle fatigability, the present findings show a reduction in force accuracy  irrespective of the force output level, which suggests difficulty to reach the force target and a reduction in neuromuscular control. Furthermore, this decline in neuromuscular control occurred also during low-intensity contraction that may best mimic the force intensity required for work-related tasks. This finding provides direct evidence for the previously reported increase in occupational injuries and health risks for both nurses and patients .One of the main issues in shift work is the difficulty to maintain an adequate level of daily activity, possibly predisposing to earlier onset of disorders typical of sedentarism . There w5 and the NS10 group. These outcomes were further corroborated by the information provided by the CR-10 scale, which evidenced an increase in the perceived tiredness level in the NS groups, but not in DS group. The parameters of the amount of sleep  were higher for the DS than either NS group. Also, sleep latency (the amount of time required for sleep onset after retiring to bed) differed between the NS groups and the DS group, with a shorter length noted for the DS group. The parameter sleep efficiency, which measures the percentage of time in bed actually spent sleeping, was > 85% in the DS group and < 85% in the NS groups. This finding indicates that due to sleep disruption sleep quality was lower in the NS groups than in the DS group.The actigraphy data indicated a better quantity and quality of sleep in the DS compared to the NSThis finding indicates that the recovery strategies the nurses adopted after working the night shift were only partially efficient in promoting restoration and in limiting the effects of sleep disruption. Poorer sleep quality has been previously reported  and ofteAltogether, the decrease in MVC force and EMG activity, the increase in neuromuscular fatigue, and the reduction in muscle force accuracy may be interpreted as different markers that reflect the same scenario: impaired muscle performance ascribable to a disruption in the sleep-wake cycle, which is a common complaint in night-shift workers. Sleep-wake cycle disruption leads to a decrease in sleep quantity and quality, resulting in acute and/or chronic fatigue often accompanied by impaired neuromuscular control . The corAlthough the present study does not identify the exact mechanism/s underlying the observed decrease in muscle performance after night shift work, a hypothesis for the difference between the NS and the DS groups may be advanced nonetheless. A variety of neuroendocrine mechanisms may be cited to explain the occurrence of reduced maximal muscle activation, force generating capacity and altered neuromuscular control in night shift workers, including (i) impairment in circadian secretion of melatonin and cortisol and in regulation of core body temperature ; (ii) moThe present study comes with some acknowledged limitations. Firstly, the combined analysis of the EMG and force signals represented a good compromise between (i) providing non-invasively objective and physiological parameters to detect possible night shift-induced impairments in neuromuscular activation and force generating capacity, and (ii) having an approach that would be feasible in an ecological context and compatible with a hospital environment. However, this approach did not permit the discrimination of the exact mechanism/s involved in the reduction of neuromuscular activation and motor control in night shift nurses. Secondly, EMG data should be used with great caution when making assumptions about motor control strategies at a motor unit level . ThirdlyIn conclusion, our findings clearly demonstrate an impairment in neuromuscular function in both NS groups independent of their shift cycle schedule. The neuromuscular alterations, i.e., reduction in maximum muscle activation level, muscle force output of the finger flexor muscles, earlier and greater myoelectric fatigue manifestation, and lower muscle accuracy, were accompanied by poorer sleep quantity and quality. Nonetheless, the NS groups were able to maintain an adequate level of physical activity, as measured by IPAQ responses. These findings may inform recommendations to improve recovery strategies that can counteract neuromuscular alterations in nursing staff working the night shift.The datasets generated for this study are available on request to the corresponding author.The studies involving human participants were reviewed and approved by the Ethical Committee of San Raffaele Hospital (CE: 156/int/2017). The patients/participants provided their written informed consent to participate in this study.EC, ER, AMo, PD’A, GB, and FE conceived and designed the study. EC, ER, LG, LC, and AMu performed the experiments. EC, LG, LC, and AMu analyzed the data. EC, CD, ER, AMo, LG, LC, and AMu interpreted the results. EC, CD, LG, LC, AMu, and FE prepared the figures. EC, CD, and FE drafted, edited, and revised the manuscript. All authors contributed to the article and approved the submitted version.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.


In the IPF group, the percentage of AED and the presence of PPFE-like lesions in the upper lobes were examined radiologically, but no correlation was found. We showed that AED frequently occurred in PPFE. AED was less frequent in IPF, which, in combination with imaging data, suggests that PPFE may have a different pathogenesis from IPF.The pathogenesis of pleuroparenchymal fibroelastosis (PPFE), a rare interstitial lung disease, remains unclear. Based on previous reports and our experience, we hypothesized that alveolar epithelial denudation (AED) was involved in the pathogenesis of PPFE. This multicenter retrospective study investigated the percentage of AED and the features of the denudated areas in 26 PPFE cases, 30 idiopathic pulmonary fibrosis (IPF) cases, and 29 controls. PPFE patients had lower forced vital capacities and higher residual volume/total lung capacities in pulmonary function tests compared to IPF and control patients. Histopathologically, subpleural fibroelastosis was observed in PPFE, and AED was observed in 12.01% of cases in the subpleural or interlobular septa regardless of fibroelastosis. The percentage of AED in the PPFE group was significantly higher than that in the IPF group (6.84%;  Aspergillus [Pleuroparenchymal fibroelastosis (PPFE) is a rare type of interstitial lung disease characterized by the formation of lesions in the upper lobe of the lung ,2,3. Hisergillus ,8, chemoergillus , hyperseergillus , and occergillus . Idiopatergillus .The pathogenesis of PPFE has not yet been identified. We reported that alveolar epithelial denudation (AED) was prominent in a case of PPFE caused by hematopoietic stem cell transplantation . This AETherefore, we aimed to morphologically investigate AED in PPFE and IPF and compare the frequency of AED between the two. Normal lung tissue was included as a control.This study was a multicenter, retrospective, cross-sectional study. From January 2013 to March 2019, 26 patients diagnosed with PPFE by surgical lung biopsy or autopsy at our institution with histopathological criteria described in the guidelines  were enrA single representative slide from each case was selected by two expert pulmonary pathologists blinded to the clinical information and diagnosis. Hematoxylin and eosin (H&E) and immunohistochemical staining for cytokeratin AE1/AE3 were performed on sections from each case. The sections were scanned at 40× using an Ultra Fast Scanner , and whole-slide images (WSI) were generated. These WSIs were evaluated by six pathologists comparing H&E staining and pan-cytokeratin AE1/AE3  immunohistochemical staining. To minimize interobserver variation, initial training on the evaluation of AED was given to all participants by two expert pulmonary pathologists. For that, six cases including two WSIs from each study group were reviewed and discussed to reach an agreement on AED evaluation by all pathologists. In addition, the proportion of the pathologists in charge of each group was assigned equally to minimize bias.First, with low magnification at 10×, the subpleural and paraseptal zones were identified and marked using annotation tools in the IMS viewer (Philips). The subpleural zone was defined as the border between the normal alveolar zone and the subpleural part of the subpleural connective tissue, including subpleural fibroelastosis and dense fibrosis. The paraseptal zone was defined as the border between the normal alveolar zone and the connective tissue of the interlobular septum of the bronchovascular bundle. Each area was annotated, and their lengths were measured on WSI. Then, with medium magnification at 100×, sites of AED in subpleural and paraseptal zones were identified, annotated, and their lengths were measured on WSI. H&E-stained slides were used to identify fibrous lesions and distinguish subpleural and paraseptal zones, and AE1/AE3 staining was used to define AED. Finally, the denudated ratios were calculated from the subpleural zones, paraseptal zones, and their measured sum by dividing the length of the AED areas by the overall lengths of the zones. The ratios (expressed as percentages) were compared between groups. The characteristics and longest range of AED were also recorded. Examples of annotated WSIs are shown in p < 0.05, and all statistical analyses were performed using JMP 14.0 .All numerical data are presented as median values with a 25–75% interquartile range. Statistical significance of the difference between two or three groups was analyzed using the Wilcoxon rank-sum test, Mann–Whitney U test, or Fisher’s exact test, where applicable. Statistical significance was defined as p = 0.012) and smokers . Blood biochemistry tests showed that Krebs von Lunge-6 (KL-6) in the PPFE group was 424 IU/mL, which was lower than that in the IPF group . However, surfactant protein-D (SP-D) did not differ between the PPFE group (221 IU/L) and the IPF group (222 IU/L). In pulmonary function tests, the PPFE group had a significantly lower percentage of forced vital capacity (%FVC) of 70.9% (p = 0.016) and a significantly higher residual volume/total lung capacity (RV/TLC) of 43.1% (p < 0.001) compared to the IPF group, which was consistent with previous reports [p = 0.02).Patient characteristics for each group are shown in % p = 0.0 and a siThe histopathological images of the three groups were reviewed in all cases and confirmed to be consistent with the diagnosis. Briefly, all cases in the PPFE group had fibroelastosis, a fibrotic lesion consisting mainly of a dense growth of elastic fibers in the subpleural space A,B, and In cases of PPFE, the border between subpleural fibroelastosis and the normal lung tissue  was well defined, and there was conspicuous AED in the area, which was highlighted by AE1/AE3 immunohistochemical staining A,B. Unlip = 0.003), which was 6.84%, and the control group (p < 0.001), which was 1.19%. Compared to that in the IPF and control groups, AED in the PPFE group was higher in the paraseptal and subpleural zones. AED was significantly higher in the IPF group than in the control group (p < 0.001), but less than that in the PPFE group.The denudated ratios were calculated for each of the three groups  from thep = 0.08)  .In this study, we showed that AED in PPFE was statistically more extensive than in IPF or normal lung tissue. It is known that AED precedes fibrin deposition and hyaline membrane formation in diffuse alveolar damage, which is a major histological pattern of acute lung injury. Fukuda et al. reported that in 16 cynomolgus monkeys in which lung injury was induced by intravenous injection of paraquat, AED occurred as the first histopathological change 2–3 days after injection, which lead to subsequent fibroblast induction and fibrosis . This reAnother important point in the current study is that AED was observed not only in the fibroelastotic area, but also in the subpleural zone, which was unaffected in PPFE. This is another reason why AED did not occur owing to PPFE lesions, but AED may be the first event leading to PPFE. AED was expected to cause a localized decrease in blood circulation and surfactant within the alveoli, leading to alveolar collapse. Alveolar collapse is an important trigger in the pathogenesis of PPFE, as recently reported by Kinoshita et al. , which sRecent genome-wide association studies have shown that the alveolar epithelium of IPF patients was affected by genetic variation in telomerase  or MUC5BOur current study showed that AED was significantly more prominent in PPFE than in IPF. This proves the hypothesis that AED was a histopathological change in early-stage PPFE and IPF. Although AED developed more prominently in PPFE than in IPF cases, it did not lead to abnormal proliferation of mesenchymal cells, such as fibroblasts and myofibroblasts, or a marked increase in extracellular matrix deposition, as seen in IPF ,27. Thisn = 10) and a group without PPFE-like lesions (n = 19)  . The resThe following four points are mentioned as limitations of this study. First, this was a retrospective study, and the number of examined cases was limited. For example, one of the largest reported series of PPFE included only 62 patients . On the We showed that PPFE frequently displayed alveolar denudation, which was significantly more prominent compared with IPF and normal lungs. The pathogenesis of AED in PPFE may be different from that in IPF.


The supplement in the breakfast group was provided at 08:00 with breakfast, and in the post-exercise group it was provided just after the exercise session in the afternoon at 14:00–18:00. In both groups, the exercise intervention was performed with two sessions a day for two months. The main effects were observed in body fat mass : 13.2–17.7), leg press strength , and Berg balance scale , but no interaction with intake timing was observed. Although the effect of the timing of supplementation on skeletal muscle mass was similar in both groups, BCAA intake with breakfast was effective for improving physical performance and decreasing body fat mass. The results suggest that a combination of BCAA intake with breakfast and an exercise program was effective for promoting rehabilitation of post-stroke patients.The combination of exercise and nutritional intervention is widely used for stroke patients, as well as frail or sarcopenic older persons. As previously shown, supplemental branched chain amino acids (BCAAs) or protein to gain muscle mass has usually been given just after exercise. This study investigated the effect of the timing of supplemental BCAAs with exercise intervention on physical function in stroke patients. The participants were randomly assigned to two groups based on the timing of supplementation: breakfast ( In recent years, combinations of exercise and nutritional interventions for frail older persons or sarcopenia patients have been actively investigated ,5,6,7,8.On the other hand, Foley et al.  reportedSuch combined therapy has been widely used, not only for frail or sarcopenic older persons, but also for stroke patients. As previously shown, BCAA or protein supplementation for the purpose of gaining muscle mass has generally been given just after exercise. A comparison study of supplementation before and after exercise  showed tThis study, therefore, investigated the effect of the timing of BCAA supplementation with exercise therapy on improving skeletal muscle mass, muscle strength, and physical function in stroke patients.The eligible patients were 69 persons who were admitted to a ward for rehabilitation during convalescence after a stroke. The inclusion criteria were as follows: cerebral infarction, cerebral hemorrhage, or subarachnoid hemorrhage; age 40 years and older; no deglutition disorder; and able to walk independently or under supervision using an assistive device if needed.The exclusion criteria were as follows: deglutition disorder; renal disease or diabetes mellitus needing dietary restriction; and cardiac pacemaker, dementia, untreated cardiovascular disorder, depression, or a schizophrenic disorder.Recruitment was conducted at Showa University Fujigaka Rehabilitation Hospital from 15 April 2019 to 31 March 2020. The follow-up was conducted during the discharge period. All subjects gave their informed consent prior to participating in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Showa University Fujigaka Ethics Committee (ID: F2018C74).A single-blind randomized experimental design was used, with 2-month periods of supplementation at breakfast or immediately after exercise in the afternoon.n = 23), who ingested a BCAA-rich nutritional supplement at breakfast; and the post-exercise group (n = 23), who ingested a BCAA-rich nutritional supplement immediately after exercise in the afternoon. The chief researcher (IK) was informed of the allocation by age group using the number container method by AK. One co-investigator (AK) created the assignment list using computer-generated random numbers in advance. Each participant was allocated a code number in order of recruitment. Randomization was performed using stratified randomization by age group as follows: 40–49 years old, 50–64 years old, and ≥65 years old. The subjects were randomly divided into two groups: the breakfast group , diagnosis, Brunnstrom recovery stage (BRS), and Charlson comorbidity index values.Evaluations were conducted before and 2 months after starting supplementation. Investigators were not blind to group allocation. Investigators determined skeletal muscle mass, lower limb isometric strength, grip strength, timed up-and-go test (TUGT), Berg balance scale (BBS), functional independence measure (FIM), energy consumption and intake, number of combined therapy sessions, and nutritional status. •Skeletal muscle mass and body fat massMeasurements of skeletal muscle mass and body fat mass were performed for all patients using the bioelectrical impedance method . The participants were measured after 2 min of rest in a supine position, and were instructed not to move or speak during the measurement.•Muscle strengthThe measurement of muscle strength was conducted by a co-investigator (RM) who was independent of the recruitment, intervention, and other data collection.Lower limb isometric strength measurement was performed on all patients using a muscle training machine . Leg press strength was measured while the subject was sitting on the muscle training machine with knees and hips at 90° of flexion. After participants were familiarized with the test procedure, 2 trials at maximum effort were performed. The higher value was used for analysis.He grip strength of all patients was measured on the dominant side using a Smedley type grip dynamometer . Two trials at maximum effort were performed, and the higher value was used for analysis.•Balance abilityDynamic balance ability was measured by the BBS and TUGT. Each test was done twice, and the higher value of the BBS and lower value of the TUGT were used. The BBS was recommended in combination with another balance scale .•Energy consumption and intakeEnergy consumption was measured using a triaxial accelerometer . The accelerometer was attached on the patient’s trunk for 5 days, measurements over 24 h for 3 days were used (excluding the dates of attachment and collection), and the median value was used for analysis.Energy intake was evaluated by the nurse after each meal and divided into a staple food and a side meal. The intake ratio was recorded as a percentage in the nursing records. Energy intake per day was calculated from the prescribed energy amount and the intake ratio of staple foods and side dishes. Participants were provided nutritional assessment and advice by a dietitian. This study was conducted without restricting the intake of food and drink other than those provided by the hospital.The energy sufficient ratio was calculated by the quotient of energy consumption and intake.•BCAA supplementationA leucine-enriched nutritional supplement was provided every day for participants in the breakfast and post-exercise groups. Both groups ingested 125 mL of supplement . The supplement contained 3.5 g of amino acids and 6.5 g of protein, along with 40 IU of vitamin D per 125 mL. The ratio of amino acids was as follows: 1.6 g leucine, 0.9 g isoleucine, 1.1 g valine; the content percentage of leucine was 44.8%.Supplementation in the breakfast group was provided at 08:00 with breakfast, and in the post-exercise group just after the exercise therapy session in the afternoon at 14:00–18:00.•ExerciseThe exercise intervention was performed in the rehabilitation hospital, with 2 sessions every day for 2 months for both groups. The rehabilitation sessions contained a 2-session exercise menu: 1 session of physical therapy  and 1 session of occupational therapy .n = 23) and post-exercise group (n = 23).Statistical analysis was conducted by a co-investigator (AK) who was independent of the recruitment, intervention, and data collection. Based on a study by Yoshimura , the minAn intention-to-treat analysis was conducted for the groups. The data for participants who dropped out of the intervention were replaced by the “last observation carried forward” method.t-test was used to determine the significance of differences between the groups in age, BMI, comorbidity index, energy efficiency ratio, FIM score, number of exercise and nutritional interventions, and nutritional status. The chi-squared test was used to determine the significance of differences between the groups in sex, diagnosis, and BRS. An unpaired t-test. All data were analyzed using JMP software .Skeletal muscle mass, body fat mass, leg press strength, grip strength, BBS, TUGT, and FIM scores were analyzed by two-way repeated-measures ANOVA (group × time). The interaction was evaluated by time of supplementation and exercise therapy. Items for which a main effect in a group was observed were compared between the groups using the unpaired 2). A total of 46 persons participated in this study; 23 persons were excluded, since 20 met the exclusion criteria, and 3 declined participation. Six participants were unable to complete the study after randomization , and no There were significant or marginally significant differences in the main effects between the groups in body fat mass : 13.2–17.7; average (avg.): 15.5 kg), leg press strength , and BBS  . There wThe amount of change from baseline body fat mass  was significantly greater in the breakfast group , and theRecently, combined intervention has been viewed with keen interest; the effects of combined intervention on improving muscle strength or mass has gained a certain consensus ,6,17,18,In the present study, whether the time of BCAA ingestion was at breakfast or post-exercise, there was no significant main effect or interaction with skeletal muscle mass. This result is in agreement with multiple previous studies ,22,23,24In the area of applied physiology, researchers have begun to find that BCAA or protein supplementation does not need to be given immediately after exercise, which was previously considered the gold standard ,24,26,27In these studies, it was common for supplementation to be combined with exercise intervention, although the time of supplementation varied . Atherton et al. reported the “muscle-full” phenomenon , in whic2), while body fat mass in the breakfast group was significantly decreased. In Japanese patients, obesity is one of the risk factors for stroke [On the other hand, there were significant main effects on lower limb strength and balance ability according to the time of supplementation, which suggests the effectiveness of ingesting BCAA with breakfast for physical ability. In addition, recent research on metabolic syndrome has reported that protein intake before breakfast promotes post-prandial carbohydrate and lipid metabolism . In the r stroke . ConsideThis study has several limitations, including: (1) there was no control group only taking BCAA without exercise or doing exercise training; (2) some participants dropped out during the follow-up; and (3) for some patients, energy intake was controlled because of diabetes mellitus or obesity. First, the design of the present study is lacking in that it does not allow us to separate the effects of nutritional supplementation and exercise, because there was no control group only taking BCAA without exercise or doing exercise training. It has not been possible to examine the effects on body composition and physical function when consuming BCAAs alone, because it is ethically questionable to not provide rehabilitation. The same issue was seen in previous studies ,16. RegaSecond, the data of participants who dropped out of the intervention were replaced by the last observation carried forward method. Even if v intention to treat (ITT) analysis was performed in this study, there might be some biases regarding sample size.Third, in regard to restricted energy intake, the energy sufficient ratio was approximately 90% in the present study. However, there were no cases of malnutrition in the groups. Therefore, the effect of energy deficiency was relatively small.Although the effect of leucine-enriched BCAA supplementation on muscle mass was similar in both groups, a combination of BCAA intake with breakfast and an exercise program was effective at improving physical performance and decreasing body fat mass. The results suggest that ingestion of BCAAs with breakfast is effective for promoting rehabilitation of post-stroke patients. Further study is needed to investigate the ideal timing of BCAA supplementation for stroke.


We aimed to explore the online signature modality for characterizing early-stage Alzheimer’s disease (AD). A few studies have explored this modality, whereas many on online handwriting have been published. We focused on the analysis of raw temporal functions acquired by the digitizer on signatures produced during a simulated check-filling task. Sample entropy was exploited to measure the information content in raw time sequences. We show that signatures of early-stage AD patients have lower information content than those of healthy persons, especially in the time sequences of pen pressure and pen altitude angle with respect to the tablet. The combination of entropy values on two signatures for each person was classified with two linear classifiers often used in the literature: support vector machine and linear discriminant analysis. The improvements in sensitivity and specificity were significant with respect to the a priori group probabilities in our population of AD patients and healthy subjects. We show that altitude angle, when combined with pen pressure, conveys crucial information on the wrist-hand-finger system during signature production for pathology detection. Signatures have long been used for personal biometric authentication. This behavioral modality, widely used in our daily lives, evolves with age. The production of fine motor movements strongly progresses during childhood and for many years until the motor program ruling handwritten movements reaches maturity. At this stage, the signing gesture becomes the outcome of a highly automated ballistic process, involving multiple areas of the brain . AlteratFew researchers have examined handwriting signatures for assessing the health state of individuals. Prior studies on handwriting focused on other handwriting tasks, such as loops, letters, and handwritten texts, for characterizing and detecting different pathologies, such as Parkinson’s disease ,6,7,8,9,Alzheimer’s disease (AD), from the perspective of handwritten signatures analysis, has rarely been studied. To the best of our knowledge, only one study, specifically on online handwritten signatures , proposeIn the present work, we studied the relationship between handwriting movements and early-stage Alzheimer’s disease (AD) by analyzing online signatures captured in a real-life task: signing a check. We propose considering the raw temporal functions acquired by the digitizer and using the sample entropy (SE) measure to quantify the possible alterations in temporal functions induced by the disease in the early stages. Our hypothesis is that since signing is uniquely both an automated and an unconscious writing pattern, a cognitive and motor disorder during the early stage of disease would be detectable by alterations in the raw temporal functions. This analysis may provide insights for early AD diagnosis.Our paper is organized as follows: In Our private dataset was acquired at Broca Hospital in Paris within the framework of the ALWRITE project ,16, a Frx(t),y(t)), pen pressure p(t), and two pen inclination angles, azimuth Az(t) and altitude Alt(t), as shown in Two simulated checks (pseudo-checks) were provided on two different sheets of paper and presented consecutively to each participant. Each person naturally filled out the whole information in the check by appending the amount in words and numeric form, the date, the place, and their signature. Participants filled out both pseudo-checks on a Wacom Intuos Pro Large tablet with an inking pen. The digitizing tablet sampled the pen trajectory at 125 Hz and captured five raw temporal functions: pen position . Examples of online signatures from one healthy subject and one early-stage AD patient are displayed in The used dataset contained online signatures of 70 participants. From the overall dataset, 31 participants were patients with early-stage AD (AD patients), and 39 were healthy controls (HC subjects). The concept of entropy has been widely and successfully exploited in the literature for the analysis of biomedical signals, especially for electroencephalography analysis ,21. MoreN {u(1), u(2),…, u(i),…, u(N)}, an integer value m < N is fixed, specifying the length of the subsequences to be compared , and let r be a positive real number.On-time series of length N − m + 1) vectors mX(1), mX(2), …, mX(i), …, mX(N − m + 1) are defined as:m-dimensional space, since each contains a subsequence of length m starting at point i. The distance between two different vectors (i ≠ j) is defined as the maximum difference of their scalar components:The following (A is the number of template vectors pairs less than r far in the embedded dimension (m + 1), and B is the number of template vectors pairs less than r far in the embedded dimension m. With construction A ≤ B, we have SE is then:m and r are often fixed empirically with the objective to maximize the discrimination between the groups: between Parkinson patients and healthy subjects [m and r. The objective was to analyze the behavior of this measure independently of fixed values of its hyper-parameters.In the literature ,21,22,23subjects ,21, and m = 1, …, 9 and r = 0.1 to 0.9 in 0.1 steps. The SE of a signature was computed considering the whole signing gesture, including both in-air and on-paper trajectories during the signing process.We computed the SE values of all signatures for different values of To gain initial insights into its behavior, we computed the SE for each raw temporal function separately. Then, we analyzed the correlation between the obtained values and two metadata, the MMSE and age, and that for the 70 participants independently of the group (AD or HC).m and r are reported in y coordinate, pen pressure, and altitude angle compared with those computed for x coordinate and azimuth angle. This result shows that persons with normal cognitive profiles (high MMSE) tend to produce signatures with more information content.The correlation values between the MMSE and the average SE on both signatures of each person for all values of m and r. We noticed a negative correlation between age and SE for each temporal function. This reveals that, in general, with age, entropy tends to decrease, meaning that temporal functions show less intrinsic irregularities and variation in the signature gesture. The handwritten production loses information content with age.We observed a very low correlation between age and azimuth compared with other temporal functions (−0.1). Aging appears to have no effect on this parameter. Azimuth could be used to distinguish a right-handed from a left-handed person, for example, for biometric authentication purposes. In our dataset, except one person, all the other participants were right-handed and thus had a similar azimuth profile. Therefore, our dataset had less variance in azimuth between persons compared with all other temporal functions. This is why we did not consider the azimuth temporal function in the following studies.SE quantifies the intrinsic information content in signatures, and thus, may be sensitive to signature style. For this reason, we addressed whether HC and AD patients produced different signature styles and studied the influence of style on SE.Signatures of HC and AD patients were labeled visually by three experts into text-based signatures , mixed , and stylized  . These tWe first noticed that text-based signatures do not exist in our dataset; no signatures were completely legible.x coordinate (SE(x)) and y coordinate (SE(y)), and that for all signatures, independent of the group (AD or HC). Then, we performed k-means clustering, with k = 3, on both entropy values simultaneously  clustering). To study the influence of signature stylization, we considered the SE values computed for the x(t) and y(t) temporal functions. Also, two persons with stylized signatures had the highest SE values, which means that an illegible signature could be complex.We observed that 77.14% of persons with stylized and mixed signatures were grouped together in Group 2 (54 of the 70 participants). This means that most signatures with such styles have similar information content in the These results indicate that SE quantifies irregularities in temporal functions independent of the stylization in the form. Given these findings, we then studied the relationship between SE and AD independent of signature styles.m and r on SE values computed for the four temporal functions (x(t), y(t), p(t), and Alt(t)). In the surfaces displayed in both r and not to m. Also, for low values of r, SE values were much higher for HC compared with AD patients. This means that the intrinsic variation in the temporal functions in the signature was much stronger for healthy persons compared with AD patients. We, thus observed a significant information loss in Alzheimer’s signatures for low values of hyper-parameter r in x(t) and y(t) pen coordinates) and p(t) and altitude angle Alt(t)).In each group (AD vs. HC), we analyzed the impact of the hyper-parameters r compared with m. We observed that when r increases, entropy values decrease for both AD and HC groups and their variances for persons in each group decrease. When m increases, the entropy values and their variances for persons in each group tend to remain stable.To gain further insight into the impact of the two hyper-parameters on SE, y(t) compared with x(t), indicating that signatures vary more vertically than horizontally. The same occurs for altitude: it had higher information content than horizontal movement (x(t)). The altitude angle conveys information about how the writer holds and moves the pen when signing, and is particularly modified by the intensity of vertical movement (y(t)). Therefore, strong variations in y(t) also appear in the temporal function Alt(t).We also noticed that entropy values are much higher for temporal function r in m = 3 and r/std = 0.1. For pen pressure and pen altitude, we found a relative increase of entropy of around 50% and 37% between signatures of AD patients and those of healthy subjects, respectively.Concerning information loss in Alzheimer’s signatures relative to healthy subjects, observed for low values of r, we performed classification for m = 3 and r/std = 0.1. For these values of hyper-parameters, the Mann–Whitney test in To study the discriminative power of SE between AD and HC subjects, as SE is sensitive to Based on this result, we considered two linear classifiers, a linear support vector machine (SVM) and linear discriminant analysis (LDA), using a two-fold cross-validation protocol for training and testing. For each raw temporal function considered, the SE values of both signatures for each person were used as inputs to the classifier.We noticed that for both SVM and LDA classifiers, the combination of SE values computed for pen pressure and pen altitude improved the sensitivity relative to taking each of those features separately. As the a priori group probability for the dataset here used was 0.44 for the AD group (31 AD patients out of 70) and 0.56 for the HC group (39 subjects out of 70), the improvement obtained was significant. For sensitivity, which was our main objective, the relative improvement was 45.7% with both classifiers (64.52% in sensitivity relative to 44.28% of a priori Alzheimer’s group probability).When combining the SE values of the four temporal functions, the sensitivity (up to 74.19%) and specificity (up to 76.92%) improved significantly for the SVM classifier. For the LDA classifier, sensitivity also improved (up to 67.74%) but the specificity decreased by almost 26%.Thus, in the best-case scenario, with the SVM and the combination of the four SE values, the relative improvements in sensitivity and specificity were 67.54% and 37.35% relative to the a priori group probability of correct classification of AD patients and HC subjects, respectively.In this work, we analyzed the sample entropy (SE) of raw temporal signals captured by a digitizer on signatures appended during a simulated check-filling task. Our extensive study of the effect of the hyper-parameters when measuring SE added to a statistical analysis highlighted that online signatures written by early-stage AD patients show significantly lower entropy values, and thus, lower information content, than those of healthy persons. We verified that SE is not influenced by different signature styles, only represented in our dataset by mixed and stylized signatures .We observed that the difference in the information content between signatures of AD patients and those of HC was high for pen pressure and altitude angle time sequences. This result that the pen altitude angle allows characterizing the pathology with pen pressure on signatures is a novel finding. Notably, in former work on biometrics, pen inclination angles were found to be misleading for characterizing a writer (with or without pressure) . Our anaFinally, the experiments and analyses conducted to support our claim—quantifying the alterations in raw temporal functions with SE, describing the complete signing gesture in the air and on paper, provides insight into early AD diagnosis. Our study shows that online signature, despite being a signal of short duration, conveys valuable information about a writer’s motor and cognitive skills through its ballistic, automated, and unconscious process, and is thus a possible modality for the automatic health state assessment.However, our study has some limitations: (1) our dataset is small, both in terms of the number of persons in each group (AD and HC), and the number of signatures per person; (2) we exploited only the raw temporal functions captured by the digitizer for the data description; and (3) even if we showed that a statistically significant difference exists between AD and HC populations with the SE measure, we obtained a misclassification rate of around 24% with a linear SVM classifier. The rather low correlation coefficients between SE and the MMSE suggest that SE is not sufficient alone to finely characterize the alterations in temporal functions induced by the disease.In the future, we will address these limitations by (1) incorporating other online signatures captured during another task in the ALWRITE project; (2) considering other kinematic parameters such as speed, acceleration, and jerk, successfully used for AD assessment based on handwriting after a local point-wise feature extraction ,16; and We also aim to extend this transversal study to signatures acquired in different sessions. The longitudinal framework will contribute to evaluating the viability of our approach for quantifying the degradations in signatures according to dementia severity.


In order to evaluate brain changes in young children with Pierre Robin sequence (PRs) using machine learning based on apparent diffusion coefficient (ADC) features, we retrospectively enrolled a total of 60 cases (42 in the training dataset and 18 in the testing dataset) which included 30 PRs and 30 controls from the Children's Hospital Affiliated to the Nanjing Medical University from January 2017–December 2019. There were 21 and nine PRs cases in each dataset, with the remainder belonging to the control group in the same age range. A total of 105 ADC features were extracted from magnetic resonance imaging (MRI) data. Features were pruned using least absolute shrinkage and selection operator (LASSO) regression and seven ADC features were developed as the optimal signatures for training machine learning models. Support vector machine (SVM) achieved an area under the receiver operating characteristic curve (AUC) of 0.99 for the training set and 0.85 for the testing set. The AUC of the multivariable logistic regression (MLR) and the AdaBoost for the training and validation dataset were 0.98/0.84 and 0.94/0.69, respectively. Based on the ADC features, the two groups of cases  could be well-distinguished by the machine learning models, indicating that there is a significant difference in brain development between children with PRs and normal controls. Pierre Robin sequence (PRs) is a congenital condition characterized by an abnormal development of craniofacial features. The incidence of PRs is as high as 1/8,500–1/14,000 , even liDiffusion-weighted imaging (DWI) has features such as greater sensitivity for ischemia and water molecule diffusion compared to other MR imaging techniques. However, a strong T2 shine-through effect limits its application in the quantitative evaluation of neonates. ADC maps have been proposed to evaluate neonatal hypoxic-ischemic brain injury, tumor and brain development, etc. , 10, whiMachine learning, however, can uncover important information that is undetected by routine clinical medical imaging, thus revealing potential biological information –13. GiveOur research group focuses on studying the brain development of congenital heart disease (CHD) and cerebral palsy (CP) based on ADC. To date, there are few other studies on this subject, and most previous research on ADC has focused on adults or positive lesions. In these instances ADC is only used for diagnosis, without extracting its inherent imaging features. Based on this, our research combines them and can also reveal methodological advantages to studying the brain development of children with PRs. We think that this study could also be applied to conventional “lesion negative” MRIs in the future, such as pediatric epilepsy, Autism, and Attention Deficit Hyperactivity Disorder (ADHD).A retrospective comparative dataset study was conducted, ultimately consisting of 30 children diagnosed with PRs and 30 healthy controls . A ratioThe control group, which had a mean age of 45.2 ± 18 days (range: 7–74 days), consisted of 12 males and 18 females. There were 45 cases in the control group initially. After screening, 30 cases were selected. The inclusive criteria of this group were: (1) normal brain MRI of brain, as confirmed by two experienced pediatric radiologists. The admission history of these patients included pneumonia, trauma, brachial plexus injury, and facial hemangioma; and (2) age <3 months; (3) No definite brain developmental diseases were found during follow-up; (4) MRI images meet the diagnostic requirements (no obvious motion artifacts). The exclusion criteria include: (1) suspected or confirmed neurologic disease in labor; (2) children with known genetic abnormalities  or other congenital diseases ; and (3) other serious diseases that may have substantial effects on the brain; (4) MRI images cannot be diagnosed (there are obvious motion artifacts).This study was approved by the hospital ethics committee. Before the MRI scan, parents were informed of the precautions before the examination, read and signed the informed consent form, and explained the examination method and process to children. As a retrospective study, each participant was followed up by telephone, informed of the significance of participating in this study, and agreed to participate. On follow up each patient was informed of the significance of the study for the child's future brain development, and their consent was obtained.2 at TR/TE = 2,408/82 ms. The layer thickness was 4-mm, and the layer spacing was 1-mm. The field of view = 180 × 180 mm. There were three orthogonal diffusion gradient directions and two excitations. The phase encoding direction was anterior to posterior (AP).MR imaging of the brain was performed using a 3-Tesla (T) whole-body clinical MRI scanner , with a digital head coil. The patients were sedated with 5% chloral hydrate (1 mL/kg) 20 min before the scan, and the MR examination was performed while the patients were asleep. An SE-EPI sequence was used in the DWI scan, and axial DWI sequences were generated at b values of 0 and 1,000 s/mmThe ADC images were exported from a PACS workstation in DICOM format, and single ADC map slices were selected at the level of the thalamus/basal ganglia, the most sensitive injury site for children, as previously illustrated .ImgJ software. Single ADC images were imported into the image processing software (MRIcron), after which the complete brain area  was sketched  as the region of interest (ROI), and saved in NIFTI format. In addition, ADC feature extraction was implemented using an open source Python software package (www.python.org). Importing Feature Analysis module as well as library functions included numpy, pre-process to realize the feature extraction in the python code  was used based on the feature distribution. Finally, LASSO regression was performed for dimensionality reduction and feature selection. Parameter adjustment could then improve the prediction accuracy and credibility of the machine learning model. After optimization, the minimum deviance criterion was implemented in our study to adjust the regularization parameter (λ), and 10-fold cross-validation was used to enhance feature robustness. Because principal component analysis (PCA) is a type of unsupervised learning, LASSO is more universally applied than either PCA or SVD , and the model complexity can be adjusted by the parameter λ (lambda) to avoid overfitting  and 10∧(0:3), respectively. These best performing signatures were realized by10-fold cross-validation, which enhanced model robustness. Logistic regression was then used with backward elimination to construct a linear classifier model. Newton's method was employed to determine the maximum point of the logistic log-likelihood function. The number of iterations was set to 10. In addition, the AdaBoost algorithm was introduced. By plotting the error line chart, it was revealed that when the number of weak classifiers was two, the minimum error value was achieved , support vector machine (SVM), and AdaBoost, were introduced to investigate whether the ADC features of the two groups  were linearly separable, which would provide conclusive evidence of the brain development differences. SVM with Gaussian kernel function was constructed as the non-linear classifier. The ranges of the optimal kernel function γ(gamma) and hyperparameter C(cost) were 10achieved . Finallyt-test and chi-square-test were used to analyze the age and gender to ensure that there was no difference between the two groups.SPSS25 statistical software was utilized. The https://www.r-project.org/) was employed to analyze the ADC characteristic values. Zero-mean normalization was applied to eliminate the impacts of dimensions and value range differences, and 70% of the cases were randomly selected as the training dataset without replacement . For texture features, the values of contrast and dissimilarity in the PRs group are lower than those in the normal group. However, for the values of homogeneity, ASM, and energy, the PRs are higher than the normal group, and the differences are statistically significant. [See the t-value and p-value after t-test.]For ADC histogram characteristics, except for t-tests. Of the remaining 65 features, 60 features were found to be significant by mean of the Mann-Whitney U-test. Therefore, LASSO regression was performed on these 99 features . Furthermore, the optimal signatures were revealed when the binomial deviance achieved its minimum values. Finally, the seven features  were pruned using LASSO regression, with the optimal regularization parameter λ of 0.0627 under the minimum norm tuned by 10-fold cross validation , a discrimination accuracy of 98% by the tuned best kernel function (Gaussian kernel γ) of 0.1, and a hyperparameter (C) of 10. The AUC of the multivariate logistic regression model was confirmed to be 0.980 (95% CI: 0.942–1) for the training dataset, the discrimination accuracy of the MLR was 93%. The performance of the AdaBoost algorithm was confirmed to have an AUC of 0.94 (95% CI: 0.872–1) and a discrimination accuracy of 86.0% for the training dataset. The testing dataset also achieved good performance using all of the machine learning models, although not as good as the training group .In the ML (machine learning) models, the features selected after dimension reduction all played an important role in the discrimination of PRs from controls. Among them, the 29th represents dissimilarity, the 30th represents homogeneity, the 39th and 56th represents the first order wavelet, the 62nd represents the second order wavelet, and the 92nd and 96th represent the third order wavelet. The histogram was made to show the importance about each feature . For theAt present, ADC features have been widely used in tumor research and show good repeatability . HoweverAs an effective tool for data classification and prediction, machine learning has been widely used in medical imaging, especially in the research of radiomics. Based on features of the ADC, machine learning was utilized, which was simpler and more accurate than traditional statistical methods , 24, espIn our research, the SVM and MLS models were found to exhibit better classification accuracy and performance than the AdaBoost models, which was consistent with previous research on the performance of machine learning models . The SVMThe reasons for the relatively weak results of the validation set may be as follows:As infants are still young, it is more difficult to perform magnetic resonance examinations. Therefore, it is difficult to collect the normal control group. This leads to a small amount of data being entered into the group, resulting in relatively weak results for the validation set.When using cross-validation to divide the data set, the parameter setting of the random seed makes the random allocation of data biased.At the same time, this research had some limitations. Firstly, due to limited resources, the data were acquired from a single medical center and the same type of MRI scanner. It will be more convincing to acquire images from multiple institutions or MRI models. Multivariate clinical case information would also be an improvement. Secondly, to facilitate clinical operations, only one level of ADC map was evaluated, which may not be comprehensive. Thirdly, the control group may have included children with abnormal brain development simply due to a lack of totally healthy volunteers. Fourthly, no scale was used to evaluate the development of neurological function in PRs patients. Finally, although this study achieved good discrimination based on ADC features, there were no specific predicted results to explain the prediction trends due to a lack of follow up research data.In conclusion, in this study, we focused on ADC features in the pediatric brain, confirming that the two groups of cases, which included PRs patients and normal controls, were well-distinguished by machine learning models, indicating that there was significant brain development abnormality in PRs children. Compared with other statistical methods, machine learning models are simple and clear, with effective operability and high accuracy.The datasets generated in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by Children's Hospital of Nanjing Medical University Ethics Committee. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.YW prepared the data and drafted the medical sections of the manuscript. FY analyzed the data using machine learning and drafted the manuscript. MZ extracted data features. MY guided and revised the manuscript throughout. All authors contributed to manuscript development, read, and approved the final manuscript.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.