This study aims to determine the therapeutic efficacy of exercise interventions for patients undergoing hemodialysis (HD) on fatigue and health-related quality of life (HRQoL).This review will only include randomized controlled trials (RCTs). The search strategy will be performed in 4 English databases, 4 Chinese databases, Clinical Trials.gov, and the Chinese Clinical Trial Registry. All English or Chinese RCTs, published from inception to May 31, 2020, will be sought. Two reviewers will screen, select studies, extract data, and assess quality independently. Primary outcome is fatigue assessed by questionnaire. The methodological quality including the risk of bias of the included studies will be evaluated using the Physiotherapy Evidence Database scale. Stata 12.0 software will be used for heterogeneity assessment, generating funnel-plots, data synthesis, subgroup analysis, and sensitivity analysis.We will provide some more practical and targeted results investigating the effect of exercise interventions for patients undergoing HD on fatigue and HRQoL in the current meta-analysis, and point out the main limitation of previous studies.The study will provide recent evidence for evaluating the therapeutic efficacy of exercise interventions for patients undergoing HD on fatigue and HRQoL.INPLASY202050071 (DOI: 10.37766/inplasy2020.5.0071) In China, the prevalence of HD was 402.18 and the corresponding number of HD was approximately 553,000. Although ongoing technical improvement in HD has ameliorated the long-term prognosis of ESRD patients, patients still suffer a complex array of clinical symptoms.Globally, end-stage kidney disease (ESKD) is one of the leading causes of death and disability, and the number of patients is increasing steadily. It is a distressing and debilitating symptom that affects patients’ daily activities and leads to highly burdensome, impaired functioning, and health-related quality of life (HRQoL). Moreover, fatigue is associated with increasing risk for cardiovascular morbidity and mortality.,7 It can be extremely weakening and intrusive both mentally and physically, which makes patients undergoing HD often experience limitations at work, in social interaction, and physical activity.,9 The high prevalence and severe impact of fatigue on overall health and well-being may explain why fatigue is one of the top-priority issues for HD patients and health providers. To reiterate the universality of fatigue symptoms, Standardized Outcomes in Nephrology-Hemodialysis (SONG-HD) Consensus Workshop has established it as a critically important outcome to be reported in clinical studies in HD patients. Despite this, fatigue remains challenging to manage and under-recognized.Fatigue is one of the most common symptoms and studies suggested a prevalence ranging from 60% to 97% in HD patients.1.2 Epidemiological reports suggesting insufficient exercise amount is the leading risk factor for premature mortality in HD patients. Current Chinese guideline recommends that exercise interventions should be a component of integrated management. Also, trials assessing exercise interventions for fatigue have been published and add to the evidence base of the association of exercise and fatigue in HD patients. However, on one side, some patients, caregivers, and health providers considered that exercise would aggravate fatigue symptoms, on the other hand, most patients prefer to a sedentary lifestyle at home because they may experience post-dialysis fatigue providing a barrier to participating exercise. Therefore, a systematic review and meta-analysis will be performed to determine the therapeutic efficacy of exercise interventions for patients undergoing HD on fatigue and HRQoL, to provide further evidence for health providers and patients.The term exercise is defined as “planned, structured, repetitive, and purposive in the sense that improvement or maintenance of one or more components of physical fitness.”1.3The aim of this study was to conduct a systematic review and meta-analysis determining the therapeutic efficacy of exercise interventions for patients undergoing HD on fatigue and HRQoL.2The protocol was registered on the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY202050071). The preferred reporting items for systematic review and meta-analysis protocols (PRISMA) will serve as guidelines for reporting present review protocol and subsequent formal paper.2.12.1.1We will only include randomized controlled trials (RCTs), quasi-RCTs, non-RCTs, and other types of studies will be excluded.2.1.2 were included, regardless of age, sex, race, region, education, and type of dialysis.Patients with ESRD undergoing HD were diagnosed according to Kidney Disease Improving Global Outcomes (KDIGO)2.1.3The patients will receive exercise interventions, including aerobic exercise, resistance training, or combined, and balance training, flexibility training. Considering the diversity of exercise, we will include all types of exercise interventions without any restrictions on frequency, intensity, duration, and mode. Comparisons will include: placebo control, sham exercise, and stretch training; exercise interventions plus one treatment compares with the same treatment; exercise interventions compares to drug therapy.2.1.42.1.4.1Fatigue was measured subjectively by questionnaires such as fatigue-specific scales, for example, the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), Piper Fatigue Scale (PFS), Brief Fatigue Inventory (BFI), Chalder Fatigue Scale (CFQ), or fatigue subscale as part of a measure assessing a broader construct, for example, SF-36 or Kidney Disease Quality of Life Instrument, or visual analogue scale. We will consider all patient-reported outcome measures for fatigue given the lack of validation work conducted in the dialysis population.2.1.4.2Secondary outcomes mainly involve HRQoL throughout the follow-up period.2.1.5We will search Pubmed, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, Embase as well as four Chinese databases, namely China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Biomedical Literature Database (CBM), and Chinese Science and Technology Journal Database (VIP). All the English and Chinese literature published from inception to May 31, 2020 will be retrieved. In addition, we will also undertake a targeted gray literature search on Clinical Trials.gov and the Chinese Clinical Trial Registry to gain unpublished or in-progress trials or completed but prepared for publication. Meanwhile, the reference list of previous clinical studies and reviews will be searched as supplementary sources. The search strategy for CENTRAL is shown in Table 2.22.2.1Two independent reviewers (YB and XZ) will develop the literature search and screening the titles and abstracts to the inclusion criteria to exclude irrelevant studies. If necessary, the full text will be read for further assessment. The discrepancies in the process will be discussed and solved with a third author (HCZ). For unclear data, we will contact the first author to determine whether to include this literature. The detail of the study selection will be presented in a PRISMA flow diagram Fig. .2.2.2A detail data and information extraction form will be made by following: basic information ; participants’ characteristics ; interventions ; comparisons (control mode); outcomes .2.2.3If the data is incomplete or missing, we will contact the author by E-mail for information. When impossible, we will perform a sensitivity analysis to determine the impact of included studies in the overall assessment of results.2.2.4 will be used to evaluate 11 domains of quality of each included literature by 2 independent reviewers (YB and XZ). Each item was scored with a maximum score of 10 (criterion 1 is not scored), including random allocation; allocation concealed; baseline similarity; subject blinding; therapist blinding; assessor blinding; adequate follow-up; intention-to-treat analysis; between-group comparisons; point measures and variability. Any divergences will be resolved through discussion and consultation with a third author (HCZ).Physiotherapy Evidence Database (PEDro) scale2.2.5If possible, funnel plots will be used to assess for the potential existence of small study bias. Egger test and Begger analysis will be used to assess the reporting bias if necessary.2.2.6I2 statistic to assess heterogeneity in included studies. The degree of heterogeneity is based on: I2 = 0%∼40%, might not be important; I2 = 30%∼60%, may represent moderate heterogeneity; I2 = 50%∼90%, may represent substantial heterogeneity; I2 = 75%∼100%, considerable heterogeneity.We will quantify statistical heterogeneity using the 2.2.7I2 < 50%, the fixed-effect model will be selected to calculate the MD, otherwise use a random-effect model. Where the included literatures are clinically heterogeneous we will perform a narrative synthesis.We used Stata 12.0 for meta-analysis to synthesize included data. For dichotomous variables, such as adverse events, we will calculate risk ratio with 95% confidence interval (CI). For continuous variables, mean difference (MD) or standard MD with 95% CI will be calculated. When 2.2.8If there is significant heterogeneity among the outcomes, we will perform a subgroup analysis: the types of exercise interventions (resistance training vs aerobic exercise); duration of treatment (<8 weeks vs ≥8 weeks); dialysis vintage (< 5 years vs ≥ 5 years).2.2.9We will conduct a sensitivity analysis by excluding merged studies one by one and observe whether the synthesis result changes significantly. If yes, the removing study may influence the overall synthesized result, so, we will reassess it and decide cautiously whether to merge it.2.2.10We will use the Grading of Recommendations, Assessment, Development, and Evaluation to evaluate the quality of these outcomes. The outcomes will be divided into very low, low, moderate, or high.2.2.11The systematic review does not require ethical approval, because it is not individual data that is used. The results will provide evidence for the efficacy of exercise intervention in the patients undergoing HD, which will also have an importance for clinical practice and research.3 which is common and potentially debilitating, and often unrecognized in HD patients. Although numerous studies on dialysis-related fatigue have been reported and the mechanisms behind the development of fatigue are relatively well understood,,24 there is a lack of high-quality evidence on exercise interventions in the management of fatigue in HD patients. A recently systematic review states that exercise interventions probably have a positive effect on fatigue in adults with chronic fatigue syndrome compared to usual care or passive therapies. However, how strong the effects of exercise are for HD patients is still unclear. We hope to provide more practical and targeted results to identify the therapeutic efficacy of exercise intervention for HD patients on fatigue and HRQoL in the current systematic review and meta-analysis.Fatigue is a feeling of exhaustion, tiredness, weariness, or lack of energy,The strength of this systematic review and meta-analysis will include: search a comprehensive range of databases, including Chinese and English databases, more rigorous and detailed concerning quality assessment and data extraction. The limitation is that we will only consider the literature written in Chinese and English, which may omit some relevant articles published in other languages, and there may be a large heterogeneity, which may bias the results.FZ developed the search strategy and drafted the protocol, and revised by LYH and YZ; YB and XZ independently worked on study selection, quality assessment, data extraction; YZ worked on data synthesis; HCZ resolved any divergences.


Critical welfare points were extremely low dissolved oxygen in water, fin and skin lesions, prolonged air exposure during pre-slaughter handling and non-humane slaughter techniques, as decapitation or asphyxia. The protocol presents practical viability and it is an initial step for the development of a tilapia welfare strategy, where the prioritization of critical welfare points, implementation of corrective actions and monitoring of the results is part of a permanent welfare management system.The aim of this study was to develop and test a tilapia on-farm welfare assessment protocol, based on Brazilian semi-intensive production systems. The study included two mains steps: the elaboration of tilapia welfare protocol and its on-field feasibility test. The protocol, including the potential indicators organized into health, environmental, nutritional, and behavioral categories, was tested on three farms. Skin, eyes, gills, jaws, fins, and vertebral spine were individually examined in 139 individual tilapias. Water physicochemical parameters and production system were considered. The overall nutritional status of individuals was assessed through body condition factor, feed conversion ratio, feed crude protein ratio, and feed ingestion behavior. During massive capture, signals of stress, level of crowding, and duration of air exposure were registered. Time required for loss of consciousness was evaluated by clinical reflexes and other behaviors during slaughter. Eye, jaw, and gill scores were different across farms (Kruskal-Wallis test,  Oreochromis niloticus, the most produced fish species in Brazil, may be considered a primary goal, as the number of individual animals involved is a criterium for priority in terms of animal welfare attention , of the Federal University of Paraná, Brazil.The tilapia welfare assessment protocol was organized in four categories as per the literature on farm welfare assessment protocols for terrestrial animals : (a) hea−3)100 to estimate tilapia nutritional status, where W is the weight (g) and L is the length (cm)  , 27. Thegth (cm) . For allgth (cm) . The ind2. The circulation system was maintained by a diversion canal and the water flow was controlled through a water intake and drained by a drainage canal. On farm B there was one 2.115 m2 excavated pond, that was supplied from the water-table by seepage into the pond. On farm C there were six excavated ponds as well as fish cages placed inside them or directly in a river channel. For farm C, the study was performed in two different excavated ponds of an area of ~10.000 m2, each containing 28 and 42 floating steel fish cages of 4.8 m3 (2.0 ×2.0 ×1.2 m) with a 20 mm steel galvanized mesh covered with PVC and four floaters: The water was renewed both indirectly by gravity and pumping through a diversion canal from a reservoir. There was were an individual inlet and an individual an outlet for each pond.The welfare was assessed using potential indicators during summer  at two different grow-out excavated pond farms (A and B) located in Joinville, Santa Catarina, and one grow-out tilapia cage facility (farm C) in Guaíra, São Paulo, respectively, in Southern and Southeastern Brazil. Farm A was composed by six rectangular excavated ponds built on flat ground, and the assessment of water quality and massive capture was performed in two of them, with areas of 3.385 and 5.050 mIndicators were measured always by the same researcher, during each farm routine schedule and with minimum interference to the daily management and procedures. The samples sizes were defined according to the farm dynamics, assessing the maximum number of individuals without disturbing farm routine. On farms A and B, it was possible to access a larger sample, derived from massive capture. On the other hand, due to the complexity of tests performed on farm C, where slaughter was performed, the number of individuals was reduced, avoiding disturbances to the slaughter processing line. In total, 139 tilapias were physically scored: 72, 40, and 27 animals on farms A, B, and C, respectively. Fish were removed from water, placed in a desk covered with soft material, identified using numbered waterproof cards, which were placed beside the animal for bilateral photographic registration. Sequentially, the tilapias were weighed, measured, and physically assessed according to the scores set in the protocol. On farms A and C, fish were in the end of final grow-out stage phase and thus destinated to slaughter after individual scoring. On farm B, as tilapia were in the intermediate grow-out phase, they were returned to the pond of origin.4), nitrite (NO2) and alkalinity , tested immediately after collection. Non-ionized ammonia (NH3) was estimated using a specific formula considering water temperature and pH  or for biometrics (farm B). The physicochemical indicators were measured directly in the water by insertion of an equipment for multi-parameter measurement . The depth of 30 cm was standardized for measuring water temperature, pH and dissolved oxygen (DO). Water samples were collected for conducting the colorimetric test of total ammonia . In sequence, the slaughter technique was assessed, based on the registration of the time needed for the loss of consciousness . The OR p < 0.05), using Statistica Statsoft 10.0. T-test was applied to compare farms A and B in relation to factor K results, because it was the only variable with normal distribution (p = 0.054). The non-parametric results obtained for health indicators on farms A, B, and C were compared by Kruskal-Wallis, aiming to detect some uniformity of results in relation to the critical welfare points observed in different facilities. Correlation analyses (p < 0.05) were conducted aiming at an enhanced comprehension of the relationship between environment conditions and health results.Descriptive analysis was used to estimate the welfare scores obtained during the field evaluations. The normality of data was tested by Shapiro-Wilk test ((Lernaea sp.), attached to the skin was detected. Light gill lesions without color alteration were observed in 33 fish (45.8%); five animals had moderate lamella fusion and excessive mucus production. From that, 36 animals (50.0%) showed splitting of caudal fin, and seven of these had additional necrotic dorsal fins. The main alteration observed in skin was subcutaneous hemorrhage, usually located in fish face between snout and operculum. On farm B eye damages, vertebral deformations or ectoparasites were not registered. However, from the 40 tilapia, 11 animals (27.5%) showed jaw lesions and 14 (35.0%) presented red spots on other areas of face skin. Gill splitting and excessive mucus production were observed in 10 fish (25.0%). Caudal fins of 19 animals (47.5%) were splitted and one tilapia presented dorsal fins light necrosis (2.5%). On Farm C, one tilapia (3.7%) from the total of 27, presented lesions on the jaw and operculum. When examining the eyes, two animals (7.4%) had unilateral hemorrhagic lesion and three animals presented bilateral exophthalmos. Gills of nine animals (33.3%) showed partial damage (lamella fusion) and two animals (7.4%) exhibited severe damage, including two positives for parasites with macroscopic signals suggestive of the monogenoid Dactylogyrus sp. Future work may include microscopic diagnosis of eventual parasite species. As for the fins, 21 animals (77.8%) presented light erosion of one or more fins, and one of them presented severe necrosis (3.7%). Coherent with clinical findings for the fins, when examining body skin, different alterations were observed, as lack of scales , ulcerations , necrosis , and body skin darkening . No skeletal deformities were detected. Blood glucose level, measured on farm C immediately before slaughter, was 86.44 ± 13.75 mg/dL, distributed in scores 1 (5.6%), 2 (27.8%), and 3 (66.7%). According to farm staff data, the mortality along the production cycle on farms A and C was 5 and 10%, respectively, and both were classified as 1; on farm B reported mortality was 15%, thus equivalent to score 2.Health indicator results are summarized in 2 on farms A and B, respectively. On farm C, an excavated pond accommodated 28 tilapia cages measuring 4.8 m3 (2.0 ×2.0 ×1.20 m), totaling 134.4 m3 of area available to the animals. Even though stocking density was not considered elevated, farms A and B presented critical levels of DO, of 1.6 and 1.7 mg/L respectively (The excavated pond areas were 3.385 and 5.050 mectively . On farmp = 0.000), scored as 2 and 3, respectively.In all scenarios, fish were self-fed twice daily with commercial extruded pellets. Even for fish maintained in the pond for an extended period on farm A, according to stocking density informed by staff and the amount of feed provided, the FCR calculated was considered adequate . In farmFarms A and B adopted similar protocols for delivering the feed, where handling was made manually from just one pond margin side. The superficial swimming efforts of fish in the direction of the feed pellets were noticeable. Farm A had a larger swimming area, being difficult for all the fish to achieve the pellets during feeding time. In both situations, the consumption occurred within 5 min. Feed management on farm C cages was carried quickly by canoe, taking around 20 min to cover all the 28 cages. The feed intake was quite fast, ~1 min for total feed consumption. This may be an indicative of underestimation of the amount of feed to offer, especially for days of intense heat, when the metabolism of the fish is accelerated. However, an excess of feed was observed in cages near the margins, probably due to low consumption associated with the stress of massive capture, as fish caught and considered underweight, called rejects, were reallocated into these same cages.−1) containing pond water for ~15 min. Even if its duration was faster than the massive capture observed in other scenarios, animals from farm B showed attempts to escape and acceleration of opercular beating. Considering the period of capture, handling, and its consequent air exposure and crowding, farms A and B were classified as score 2 and Farm C was classified as score 4. As for pre-slaughter and slaughter, on farm A fish were placed in crowded transport boxes containing water and ice until transport to the slaughterhouse. No control of temperature, DO or stunning effectiveness was observed. In the first minutes after allocation in the transport container, animals presented agitated swimming and frequent escape behavior, and at the end of the capture procedure the first animals to be submitted to the ice slurry were apparently dead. On farm C, 10% of animals were sold directly to the local market. In this case, the tilapias were placed in raffia bags without any procedure for slaughtering or stunning them, and consequently probably died from asphyxiation on the way to the reseller. The animals considered too small were thrown alive to be consumed by birds, constantly present around the tanks likely conditioned to that practice. Remaining fish, on farm C, were placed in 500 L tanks containing water and arranged in a truck; after arrival at the slaughterhouse complex, the animals were transferred to another tank. This slaughterhouse tank had a recirculation pump; however, there was no filtering system or temperature control device. Animals that were slaughtered lastly showed signs of physical exhaustion, apathy and remained practically outside of the water because the tank was drained before the slaughter procedure was complete. On the processing table, tilapias were decapitated with a knife and fileted. It was noticed that after decapitation, some organs as the heart and pectora fins stayed connected to the fish head. This allowed for the presence of movements which characterize consciousness in most severed heads. From 10 animals that were evaluated, severed heads presented frequent OR, six showed attempts to swim when reconditioned into water and three showed mouth regular opening movements. The average time for loss of OR and VER were 257.36 ± 121.42 and 301.87 ± 120.16 s, respectively. However, no fish showed a reaction to the pain stimulus applied in the lips after decapitation. Due to the prolonged suffering that animals were exposed to in both scenarios, slaughter welfare score was classified as 4. In total, 26 welfare scores were measured on farms A and C, and 25 evaluated on farm B, including health, environment, nutrition and behavior indicators. For the comparison between farms analysis, all indicators scores were included. Despite the high frequency of score 4 on farm A, when considering the total added welfare scores, no significance between farms was found (p = 0.435). Results of analyses showed a weak correlation between gill score and weight  and placed in 1,000 L transport boxes located over a fish transport truck, containing water and ice. On farm C the procedure was faster, as the massive capture, including the cage displacement to the handling deck and lifting, the fish capture by fishing net (~10 fish/catch) and weighing lasted 20 min. However, some critical welfare points in relation to this management were observed. Fish that were not sold to customers immediately remained in same cage until slaughter time by the afternoon. In the meantime, the water column was extremely reduced due to the tank lifting. Fish presented agitated swimming and some parts of body were exposed to air and luminosity. In farm B the capture using fishing net was performed four times, aiming to collect enough animals to conduct biometrics. Fish were placed in 10 L bucket  to the toxic un-ionized gaseous form (NH3)  . In addirm (NH3) . Despite2 by the algae .MQ: funding acquisition. AP, CM, and MQ: study conception and design. AP, FB, and ES: performance of the data collection and tabulation. AP: statistical modeling and data analyses. AP and CM: preparation of the manuscript. MQ and CM: project coordination. All authors reviewed and approved the final version of manuscript.MQ and FB were employed by FAI Farms. The remaining 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.


Multiple Chemical Sensitivity (MCS) is a chronic and/or recurrent condition with somatic, cognitive, and affective symptoms following a contact with chemical agents whose concentrations do not correlate with toxicity in the general population. Its prevalence is not well defined; it mainly affects women between 40 and 50 years, without variations in ethnicity, education and economic status. We aimed to assess the core symptoms of this illness in a sample of Italian patients. Two physicians investigated different symptoms with a checklist compilation in 129 patients with MCS (117 women). We conducted a categorical Principal Component Analysis (CATPCA) with Varimax rotation on the checklist dataset. A typical triad was documented: hyperosmia, asthenia, and dyspnoea were the most common symptoms. Patients also frequently showed cough and headache. The CATPCA showed seven main factors: 1, neurocognitive symptoms; 2, physical (objective) symptoms; 3, gastrointestinal symptoms; 4, dermatological symptoms; 5, anxiety-depressive symptoms; 6, respiratory symptoms; 7, hyperosmia and asthenia. Patients showed higher mean prevalence of factors 7 (89.9%), 6 (71.7%), and 1 (62.13%). In conclusion, MCS patients frequently manifest hyperosmia, asthenia, and dyspnoea, which are often concomitant with other respiratory and neurocognitive symptoms. Considering the clinical association that is often made with anxiety, more studies are necessary on the psychosomatic aspects of this syndrome. Further analytical epidemiological studies are needed to support the formulation of aetiological hypotheses of MCS. Multiple chemical sensitivity (MCS) syndrome is a medically unexplained condition characterized by the appearance of symptoms in various organs and systems when the subject is exposed to odours of chemicals that are usually present in the environment at concentrations that are not related to hypersensitivity in the general population ,2,3,4. MMCS aetiology and pathogenesis have not been clarified, as no variation in clinical trials has been reported. MCS prevalence is not well defined, probably since it is not a universally recognized diagnostic typology ; 40–50-yCurrently, the World Health Organization has not recognized MCS as an organic, chemical-caused illness . This coDifferent studies have suggested correlations of MCS symptoms with various possible triggers, including psychological and physical trauma, environmental exposure to chemical compounds, oxidative stress, and changes in the metabolism of xenobiotics related to genetic factors, including some variants of the CYP2D6, NAT2, PON1, MTHFR, and CCK2R genes ,18,19,20The major hypothesis regarding the etiopathogenesis of MCS provides a multifactorial model, including genetic, environmental , and anamnestic (previous surgery) variables .MCS is acquired in relation to some documentable environmental exposure that may initially have produced a demonstrable toxic effect.Symptoms involve more than one organ system and recur and abate in response to predictable environmental stimuli.Symptoms are elicited by exposures to chemicals even at very low concentrations.No widely available test of organ system function can explain symptoms.Cullen introduced the term “Multiple Chemical Sensitivity” in 1987, and drew up the following diagnostic criteria:MCS is defined as a chronic condition .CNS symptoms are reproducible and associated with self-reported hypersensitivity.Symptoms involve multiple systems .Symptoms occur with exposure to low levels of multiple (unrelated) chemicals and improve or remit when these triggers are avoided.In 1996, the International Program of Chemical Safety (IPCS) of the World Health Organization proposed the Nethercott criteria :MCS is dIn 2005, an extension of the Nethercott criteria was proposed by Lacour & Zunder  and undeA new concept is the so-called “Central Sensitivity Syndromes”, which include the MCS, Chronic Fatigue Syndrome and Fibromyalgia, i.e., syndromes with considerable overlap of symptoms and mutual comorbidities ,28. ThesFor some authors, MCS is an unrecognised psychiatric disorder  or a psyOn these bases, our main hypothesis was that MCS might show specific categories of symptoms, based on the organs/systems involved. The aim of this study was to identify the core MCS clusters of symptoms in a group of Italian patients attending a regional referral centre. The objectives consist in deepening knowledge of the main clinical aspects of the syndrome, dividing this into defined and identifiable dimensions, and opening the way to personalized treatments.This was a single-centre, descriptive, observational, epidemiological study conducted at the Centre of Personalized Medicine at “Sant’Andrea” University Hospital, Rome, Italy.Descriptive observational studies can reveal the frequency and distribution of health events in populations or groups of individuals. These are useful in bringing attention to characteristics of individuals suffering from a disease, or who have a greater risk of contracting a disease. They can also provide essential information for planning health care facilities. They represent a fundamental step towards proceeding with the formulation of etiological hypotheses that can be further verified through analytical epidemiological studies.We analysed medical reports of patients who have had access to the centre for a medical consultation. We included data from patients who met the following criteria: MCS syndrome according to the Cullen criteria  with LacPatients were assessed for MCS symptoms with a simple checklist compiled by two physicians . The stuChi-square (2χ) test for categorical variables. We analysed the homogeneity of variance with Levene tests. We conducted a categorical Principal Component Analysis (CATPCA) with Varimax rotation. Cut-off for statistical significance was set at p < 0.05. All p values were two-tailed. We used SPSS Statistics 25.0 software  for all analyses.Statistical Analyses. We analysed the clinical characteristics of the samples with one-way analysis of variance (ANOVA1way) for the continuous variables, and the Study participants were 129 patients , including 112 women and 17 men affected by MCS. We summarize the main sociodemographic and clinical features of the sample in The CATPCA showed seven components with Eigenvalue >1, the first of which explained 42.23% of the variance, the second 8.5%, the third 6.86%, the fourth 6.42%, the fifth 4.63%, the sixth 4.1%, and the seventh 2.74%. The seven factors accounted for 75.48% of the total variance.The scree-test did not indicate the need to reduce the number of factors. The elements constituting each factor showed a high linear correlation, and Cronbach’s alpha was compatible with an excellent internal consistency of the checklist (0.99). Cronbach’s alphas for each Factor were 0.96 for Factor 1 (F1), 0.7 for Factor 2 (F2), 0.62 for Factor 3 (F3), 0.6 for Factor 4 (F4), 0.43 for Factor 5 (F5), 0.35 for Factor 6 (F6), and 0.01 for Factor 7 (F7).From the analysis of the items included in each factor we proceeded to the following denominations: F1, neurocognitive symptoms; F2: physical (objective) symptoms; F3: gastrointestinal symptoms; F4: dermatological symptoms; F5: anxiety-depressive symptoms; F6: respiratory symptoms; F7: hyperosmia and asthenia. Analysing the expression of these factors, our sample showed a high mean prevalence of F7 (89.9%), F6 (71.7%), and F1 (62.13%) .One-way ANOVA did not show gender differences in age and factor scores, i.e., the sum of symptoms composing each factor .One of the main problems in defining MCS based on its clinical presentation is the variation in individual responses to chemicals, which is mutable to the point that a characteristic pattern of symptoms has not been yet established . The mosIn line with existing evidence ,8, we foHyperosmia and asthenia. Different studies showed that olfactory hypersensitivity is one of the most frequent clinical complaints referred by MCS patients ,39,40, wAsthenia/fatigue is the other main symptom of MCS in our sample. Its prevalence has been involved in theorizing a common aetiology of multiple chemical sensitivity, chronic fatigue syndrome, and posttraumatic stress disorder, to the point that changes in nitric oxide/peroxy-nitrite metabolism has been viewed as a common etiological correlate . In thisRespiratory symptoms. Our results support the fact that MCS sufferers display frequent manifestation of respiratory symptoms. In this regard, patients often refer to respiratory and allergy specialists and their MCS symptoms can be often misdiagnosed as asthma or allergic conditions, which can lead to vain medical investigations . MCS patGastro-enteric symptoms. We showed that gastrointestinal symptoms are statistically grouped in MCS, in line with existing research that reported irritable bowel, gastroesophageal reflux , and gasNeurocognitive symptoms. Our study showed that MCS includes a major component of neurocognitive symptoms, in line with previous evidence regarding concentration difficulties, headache, and other cognitive symptoms. Other frequently reported symptoms are paraesthesia, light-headedness, and mental confusion ,55.Most patients relate these symptoms to the toxic effects of environmental “chemicals”, although direct evidence of this is lacking. Other hypotheses considered hyperventilation responses after stimuli exposure and suggested that the resulting hypocarbia might account for symptoms, comparable with panic disorder. This has been partially supported by response to treatment with sodium lactate compared to placebo .Neuropsychological assessments of cognitive functioning may be an indirect method for determining the state of health of the central nervous system and could be important in MCS patients diagnosed with certainty .Other somatic symptoms. Our study showed that MCS sufferers might present with some physical symptoms, including arthro-myalgia/fibromyalgia, cystitis, recurrent fever, motor incoordination, trembling, pressure peaks, and dizziness. This cluster of symptoms is the least represented in our sample (38.88% of patients), although arthro-myalgia, fibromyalgia, and cystitis, if considered separately, were displayed by more than 45% of patients.Some patients experience significant discomfort from one or more of these symptoms, as reported in the literature regarding myalgia ,59, hemoFurthermore, there is a recognized overlap among unexplained clinical conditions, including MCS, fibromyalgia, interstitial cystitis, and others . Fever iDermatological symptoms. Atopic dermatitis significantly increased in MCS sufferers between 2012 and 2015 in Japan , and wasAnxiety-depressive symptoms. Some authors have argued that MCS diagnosis might be a misdiagnosis of an unrecognized psychiatric disorder ,69 or a Limitations. The results of this study should be viewed with caution due to the following limitations. The study lacks a control sample  and the statistical analyses are incorrect for any organic comorbidities, which may have precluded the observation of important outcomes. Since this is not an experimental study, it did not allow the verification of whether the association of symptoms with the diagnosis is causal or not. However, the study is observational and descriptive in nature, aiming to illustrate the main symptoms of patients who have received a diagnosis of MCS. In addition, reports of patients with severe chronic organic illnesses, acute pathologies, and acute psychiatric disorders were excluded.MCS is a widespread disease or medical condition associated with exposure to common chemical pollutants, more frequently affecting adult women, and with unknown aetiology. It is mainly characterized by hyperosmia and asthenia, which are often associated with respiratory and neurocognitive symptoms. Gastro-enteric, dermatological and psychiatric symptoms are also often present, while other somatic symptoms are less frequent. MCS is a complex syndrome, involving many organs/systems, often displaying psychosomatic and psychiatric aspects. Further analytical epidemiological studies are needed to support the formulation of aetiological hypotheses of MCS.


A total of 21 serum amino acids levels were measured using ultra high performance liquid chromatography and mass spectrometry. The normalized data were analyzed by multivariate approaches, and the selected potential biomarkers were combined in different combinations for prediction of stroke functional recovery. The results demonstrated that there were significant differences in leucine-isoleucine, proline, threonine, glutamic acid, and arginine levels between good and poor recovery groups. In the training (0.952) and test (0.835) sets, metabolite biomarker panels composed of proline, glutamic acid, and arginine had the highest sensitivity and specificity in distinguishing good recovery from poor. In particular, arginine was present in the top 10 combinations of the average area under the receiver operating characteristic curve (AUC) test set. Our findings suggest that amino acids related to energy metabolism and excitotoxicity may play an important role in functional recovery after stroke. Therefore, the level of serum arginine has predictive value for the recovery rate after stroke.Amino acids have been shown to be among the most important metabolites to be altered following stroke; however, they are a double-edged sword with regard to regulating hemostasis. In this study, we conducted a targeted metabolomic study to examine the association between serum levels of amino acids and functional recovery after stroke. Three hundred and fifty-one patients with stroke admitted to an acute rehabilitation hospital were screened, and 106 patients were selected based on inclusion and exclusion criteria. Recruited patients were stratified using Montebello Rehabilitation Factor Score (MRFS) efficiency. We selected the top ( Increasing evidence suggests that amino acids, including homocysteine and branched-chain amino acids (BCAA), are one of the most important disturbed metabolites after stroke , 2. ImpoMetabolomics is a new strategy that can detect changes in amino acids, vitamins, organic acids, and other small-molecule metabolites in biofluids  of patients in real time , 10. As Currently, most studies of metabolomic biomarkers in patients with stroke compare metabolomic profiles of stroke patients with the normal population , 13. FewThis is a retrospective study approved by the ethics committee of Spaulding Rehabilitation Hospital. Three hundred and fifty-one patients with stroke admitted to an acute rehabilitation hospital in Boston between January 2015 and December 2016 were screened with the following inclusion criteria: first ischemic stroke, confirmed by CT or MRI, no tPA (tissue plasminogen activator) treatment, age between 50 and 85 years, length of stay >6 days, admission total Functional Independence Measure (FIM) score between 36 and 71 , 15, 16,FIM scale is widely used to evaluate the functional abilities of stroke patients undergoing rehabilitation , 17. TheBecause length of stay and total FIM scores at admission varied considerably, Montebello Rehabilitation Factor Score (MRFS) efficiency was used to evaluate functional recovery after stroke , 18. TheDemographic data and clinical characteristics including age, gender, education, body mass index (BMI), diagnosis, comobilities, and discharge destination were collected from electronic medical records. Stroke features including site  and side were collected from MRI or CT reports. Lesion size was not measured due to limited imaging available for the measurement.Non-fasting venous blood was obtained from all stroke patients within 1 week after hospitalization. Studies showed that there were no significant difference of amino acids between fasting serum samples and non-fasting serum samples , 20. TheBlood samples were centrifuged at 13,000 g for 30 min at 4°C, after which time serum was removed and aliquotted before storage at −80°C (not more than 2 years) until ultra high performance liquid chromatography—mass spectrometry (UHPLC-MS) analysis. Prior to analysis, serum samples were thawed and centrifuged at 14,000 g for 10 min in a cold room (4°C). Then 200 μl supernatant was transferred into a new 1.5 ml microcentrifuge tube, and 800 μl of cool methanol (−80°C)  was added to make a final 80% (vol/vol) methanol solution. This mixture was incubated for 8 h at −80°C and then centrifuged at 14,000 g for 10 min (4°C). Subsequently, the supernatant was transferred into a new 1.5 ml microcentrifuge tube, dried in a SpeedVac , and stored at −80°C until analysis , 22.Each sample was resuspended in 20 μl of UHPLC-MS grade water , and 10 μl per sample was analyzed with UHPLC-MS using the selected reaction monitoring (SRM) method with positive/negative ion polarity switching on the hybrid triple quadrupole/linear ion trap mass spectrometer (AB/SCIEX). A total of 21 amino acids were monitored and detected for each sample. Peak areas from the total ion current for each metabolite SRM transition were integrated using MultiQuant v2.0 software (AB/SCIEX) .T-test was used to analyze the difference in the continuous variables between the two groups, and the chi-square test was used to evaluate clinical categorical measures  for multivariate analysis, including principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) after mean-centering and unit variance (UV) scaling. The potential biomarkers were filtered and confirmed when their Variable Importance of Projection (VIP) scores are >1 (VIP > 1) , 12.n = 200). The parameters of the models, such as the R2 and Q2 intercepts, were investigated to ensure the quality of the models and to avoid over-fitting  and a goodness of prediction parameter (Q2Y). In addition, the PLS-DA model and the reliability models were further validated using a rigorous permutation test  scores. The potential biomarkers were filtered and confirmed when their VIP scores are >1 (VIP > 1) , 11, 25.Matlab R2014a  was used to perform variable selection of potential biomarkers. The selected potential biomarkers were combined in different combinations for prediction of stroke functional recovery. All prediction combinations were examined separately, using 10-fold cross-validation , 27.In 10-fold cross-validation, nine-tenths of the serum samples from all 40 samples were randomly assigned to the training set. The metabolite profile of this training set was used to diagnose for this prediction task. The remaining one-tenth of the serum samples from all 40 samples formed the test set. This test set was used to validate the metabolite profile diagnostic for the feature of interest. This was repeated 10 times, so that each one-tenth split of the data set acts as the testing set once. Areas under the curve (AUCs) with 95% confidence intervals (CIs) were calculated for sensitivity and specificity values. Mean AUC in the training set, mean AUC in the test set, standard deviation (SD) of AUC in the test set, and 95% CI of mean AUC in the test set were analyzed , 27.Predictive performance results for each prediction combination were compared using area under the receiver operating characteristic curve (AUC of ROC). We note that this predictive performance is for the stratified data set, so our primary interest is in the predictive factors. The values of mean AUC in the test set are regarded as the criteria for selecting the best combination of predictive biomarkers , 27. Sinp < 0.001). The time of hospitalization, destination of discharge, total score of FIM at admission and discharge, and effective rate of MRFS and MRFS in the GR group were significantly better than those in the PR group (p < 0.001). There was no significant difference in other clinical indexes  between the two groups (P > 0.05) . Through analysis, five serum metabolites  were screened out, which were closely related to the recovery level of stroke (VIP > 1) . In contModels were fit where five metabolites were considered in all possible combinations, including one single metabolite combination, two different metabolites in combination, and up to the combination of all five metabolites . There wCross-validation is a well-known technique to choose tuning parameters of a model, while limiting the risk of overfitting , 27. As As shown in There were significant differences in age between the GR and PR groups. So, we investigated the correlation between age and identified metabolites in the two groups. As shown in Based on the above results, we recombined the age and the panel of proline, glutamate, and arginine. We analyzed the 4-variable panel, respectively, in the training and test sets. The values of mean AUC of the 4-variable panel was 0.962  in the training set, and 0.871  in the test set, which had higher sensitivity and specificity distinguished between patients with GR and PR compared with the identified panel from 31 combinations .Amino acids are among the most important disturbed metabolites after stroke, but the association of amino acid levels with level of stroke recovery is not clear. Our main findings were that there was a significant difference in leucine-isoleucine, proline, threonine, glutamic acid, and arginine levels between the GR and PR groups. The panel of combined proline, glutamate, and arginine provided high sensitivity and specificity in prediction of functional recovery.Studies have shown that arginine in plasma can cross the blood-brain barrier (BBB) into the brain (50 1–4 nM)  , 30, NO  1–4 nM) . Several 1–4 nM) . However 1–4 nM) , 28. Thefunction , 28.In this study, in addition to arginine, serum glutamate levels in patients with PR were also significantly higher than those in patients with GR. Glutamate is one of the most abundant free amino acids in the mammalian central nervous system (CNS) and is at the intersection of multiple metabolic pathways . In its 2+-dependent and will be activated to generate NO when calmodulin forms Ca2+/calmodulin complex with calcium (−), which is of high neurotoxicity  differed significantly between GR and PR groups. The panel grouped by proline, glutamate, and arginine had the highest sensitivity and specificity in predicting recovery and functional outcomes of stroke. This finding suggests that the metabolomic process combined high level of neuroexcitoxicity with low level of cell membrane stability may worsen stroke recovery.p < 0.05). The age difference may contribute to the difference in metabolimic profiling. In addition, the age difference could also cause bias of the results. Further study with age-matched groups, multi-center and large sample size is warranted to confirm the findings.The stratification method used in this study enhanced the power of detection by targeting the extremes; however, this study is still limited by relatively small sample size. Second, subjects from this study were from one single, urban rehabilitation hospital; therefore, the findings cannot be generalized to the general population. Third, in this study, the average age of the GR group was significantly younger than the average age of the PR group (High levels of glutamate and arginine are associated with PR after stroke, which is related to oxidative stress and excitotoxicity.Leucine-isoleucine, threonine, and proline involved in energy metabolism are positively related to functional recovery after stroke.Reduced leucine-isoleucine, threonine, and proline, as well as all or only one or two of them, were combined with elevated arginine into new panels, and these new panels illustrate a high predictive value for stroke functional recovery. The panel grouped by proline, glutamate, and arginine has the highest sensitivity and specificity on predicting recovery and functional outcomes of stroke of all 31 combinations. Age can increase sensitivity and specificity of this identified panel for stroke functional recovery.The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.The study was approved by the ethics committee of Spaulding Rehabilitation Hospital. Written informed consent was not required as per local legislation and national guidelines.XW, TL, and QW contributed to the inception and design of the paper and writing of the manuscript. HS, SC, GL, AC, and PF contributed to patient recruitment and data collection. XW, QW, XL, and LW contributed to the statistical analysis and research design. All authors approved the final version of the 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.


The metabolome, as downstream of the transcriptome and proteome, has been considered as the most representative approach to monitor the relationships between animal physiological features and environment. Thirty-nine molecules were unambiguously quantified, able to give information about diet, proteins digestion, energy generation, and gut-microbial co-metabolism. The samples collected allowed study of the effects of age and sex on the giraffe urinary metabolome. In addition, preliminary information about how sampling procedure and pregnancy could affect a giraffe’s urinary metabolome was obtained. Such work could trigger the setting up of methods to non-invasively study the health status of giraffes, which is utterly needed, considering that anesthetic-related complications make their immobilization a very risky practice.The urine from 35 giraffes was studied by untargeted  Giraffa camelopardalis) is declared a vulnerable species [https://www.giraffidsg.org/). In addition, from November 26, 2019, giraffes are included in Appendix II of the CITES  to improve its protection, subjecting it to strict regulation (https://www.cites.org/).According to the International Union for Conservation of Nature (IUCN), giraffe  on humans and other animals suggest that the giraffe’s urinary metabolome may be particularly informative about the general health of the animal. In horse urine, molecules revealing the action of the intestinal microbiota were found in micromolar concentrations [Among the completely unexplored characteristics of giraffe urine is its metabolome, the ensemble of low weight molecules produced by the cellular metabolism. Studies carried out by liquid chromatography–mass spectrometry (LC/MS) or by proton nuclear magnetic resonance spectroscopy  has been widely used for the investigation of urine metabolomes, taking advantage of its high reproducibility and minimal sample preparation.Among the analytical platforms capable of fulfilling the requirements, proton nuclear magnetic resonance spectroscopy . Among 39 quantified compounds, four molecules showed a variation of concentration higher than 50%, namely To obtain hints about the potential effects of location on the metabolome of giraffe urine, we selected the samples from the locations BG (Parco Faunistico Le Cornelle) and FA (Zoosafari Fasanolandia), where most of the samples had been collected, and we set up a three-way ANOVA analysis aiming at excluding any effect related to gender or age. None of the molecules quantified appeared as significantly different in relation to zoo, so this variable was not considered in the subsequent analyses.p < 0.05) affected by sex, as shown in To obtain preliminary data on the effect of sex on the urinary metabolome, we focused on samples collected from adult, non-pregnant individuals. Six molecules were found to be significantly  model was calculated on their concentration, as shown in p < 0.05), with female and male individuals appearing respectively at low and high PC scores. Among these molecules, hippurate, phenylacetylglycine, and thymine were more abundant in the urine of male individuals, while lactate, acetate, and succinate were more concentrated in the females’ urine. Three principal components (PCs) were accepted by the algorithm to depict the overall data features. PC 1, accounting for 59% of the variance thus represented, indeed significantly summarized the peculiarities connected to sex (p < 0.05) the concentration of three urinary metabolites, namely formate, alanine, and valerate, , with Young, Adult, and Old individuals appearing respectively at low, intermediate, and high PC scores. Among these molecules, formate and alanine were more abundant in young individuals, while valerate showed an opposite trend.Three PCs were accepted by the algorithm to depict the overall data features. PC 1, accounting for 44.1% of the variance thus represented, summarized effectively the peculiarities connected to age  [Comparisons with the urinary metabolome of other animals are also possible, giving indirect information about the differences in metabolism. An example of this possibility is offered by allantoin. This molecule is the fourth most concentrated in giraffe urine , identicunniens)  and horsunniens) . Differeunniens) , even ifunniens) . This apunniens) .In the current study acetate, succinate, and lactate concentrations appeared to be significantly higher in female giraffe urine, while hippurate, phenylacetylglycine, and thymine were more concentrated in male urine. For acetate, two of the authors of the present paper identified a similar situation in horse urine . For theBacteroides and Acinetobacter genera, responsible for the degradation of starch and cellulose to formate [Treponema [By removing the gender effect by two-way ANOVA, it was possible to focus on the effect of age. In parallel with previous studies in rats and humans ,28, form formate , while ireponema .The concentration of amino acids in urine has been consistently linked to the turnover of muscle amino acids ,31, withEarly identification of pregnant giraffes with maximum accuracy is an important issue for optimizing their management. Although some diagnostic methods  have been described in domestic animals , their aTaurine is an important amino acid during pregnancy and lactation, because it satisfies the needs of both the fetus and suckling infant. In our research, taurine excretion through urine increased during early pregnancy, consistent with human studies . Taurinep-cresol sulfate and phenylacetylglycine, mainly described as gut microorganism co-metabolites [A further contribution to urine metabolome profile modifications may be due to changes in the gut microbiota. In fact, among the molecules showing the greatest changes we found abolites ,25, absoabolites ,40, in wabolites . DespiteAll the procedures related to animals respected the Directive 2010/63/EU of the European Parliament and of the Council of September 22, 2010 on the protection of animals used for scientific purposes  and the Italian legislation .Giraffa camelopardalis) were involved in the current study. Based on physical examinations, giraffes did not show symptoms of diseases both before and during the urine sampling period. The giraffes were housed in five Italian zoos: Zoosafari Fasanolandia (FA) (N = 11), Safari Ravenna (RA) (N = 4), Giardino Zoologico di Pistoia (PT) (N = 1), Parco Natura Viva (VR) (N = 4), and Parco Faunistico Le Cornelle (BG) (N = 15).A total of 35 captive giraffes  to bi-distilled water (350 μL) and to a D2O solution (200 μL) of TSP (3-(trimethylsilyl)-propionic-2,2,3,3-d4 acid) 10 mM and of NaN3 2 mM. A 1M phosphate buffer had been used to set the D2O solution to pH of 7.00 ± 0.02. After a further centrifugation, we recorded 1H-NMR spectra at 298 K with an AVANCE III spectrometer , at a frequency of 600.13 MHz, equipped with Topspin software (Ver. 3.5).We prepared urine samples for NMR by thawing and centrifuging them for 15 min at 18,630× According to Zhu et al. , we suppWe aligned the spectra by using the TSP signal as a reference (−0.017 ppm). We adjusted the baseline of each spectrum by distinguishing irregularities of the baseline from genuine signals, according to the “rolling ball” idea , implemeAccording to Dieterle et al. , water iwww.gimp.org). Prior to univariate analysis, we transformed the data to normality by BoxCox transformation [p-value of 0.05. We conducted the statistical analysis in R computational language  and we rormation . To inveormation . For theIn agreement with Bazzano et al. , we high1H-NMR. Foraging behaviors and daily activity could be considered as one of the main reasons for the differences we highlighted that are linked to sex and age. A preliminary observation conducted on two female giraffes suggests that 1H-NMR based metabolomics could be conveniently applied to monitor modifications occurring during pregnancy, some of which are potentially related to inflammatory status triggered by modification of the microbiota profile. This work represents a primer in giving quantitative information about the urinary metabolome of captive giraffes, as detected by untargeted


The prevalence of food allergy is increasing over the last decades. The role of vitamin D in the prevention of food allergy has been largely investigated. Its role on the physiology of calcium and bone is known, but calcitriol (active form of the vitamin D) also influences the epithelial cells, T cells, B cells, macrophages, and dendritic cells. Almost all cells of the adaptive immune system express the vitamin D receptor, making them also capable of being vitamin responsive. Specifically considering the potential role of vitamins in food allergy, vitamin D has been shown to affect several mechanisms that promote immunologic tolerance, including the T regulatory cell function and the induction of tolerogenic dendritic cells. The target of our review is to evaluate the role of vitamin D in the prevention of food allergy in children. There are contradictory data on the relationship among the vitamin D deficiency and the developing of food allergy. Some studies associate lower exposure to sunlight to food allergy; on the other hand, further research has found that higher vitamin D levels could increase the likelihood of allergic sensitization and food allergy. Therefore, there is an urgent need for well-planned randomized controlled trials on vitamin D supplementation, with particular regard to the prevention of food allergy. The role of vitamin D beyond bone and calcium metabolism is not fully understood. Vitamin D is a hormone with pleiotropic effects, essential not only for calcium homeostasis and bone mineralization but also for the proper functioning of the immune system.However, some patients do not benefit from vitamin D supplementation owing to genetic alterations in metabolism rather than absorption.The association between vitamin D and development of food allergy is contradictory.There is a potential association between lower sunlight exposure and food allergy, but on the other side, it appears that higher levels of vitamin D might raise the probability of allergic sensitization and food allergy.Vitamin D must be considered as a further chance in comprehension and treatment of atopic diseases.There is an urgent need for well-planned randomized controlled trials on vitamin D supplementation in food allergy.In recent decades, the occurrence of food allergies has recorded a significant increase in many developed countries worldwide, probably as a result of environmental and lifestyle changes , 2. Vita+CD25+ Foxp3+ T cells, avoid dendritic cell maturation, and abolish allergen-specific IgE synthesis 2D, their conversion into IL-10 secreting Treg cells has been proven; at the same time, there is a suppression of IgE making by B cells, with production of antiphlogistic and tolerogenic cytokines (Vitamin D receptors (VDRs) are expressed in almost all the tissues of the human body. There is a significant association between vitamin D levels and the risk of immunologic, metabolic, or neoplastic disorders . Moreoveynthesis , 11. In ytokines , 13.In our study, we reviewed the current literature to evaluate the role of vitamin D in food allergy in children.3) and finally carried to the kidneys, where it is transformed into the active form   is the exposure to solar UVB radiation (290–315 nm wavelengths), which determines the formation of cholecalciferol in the skin, which is then metabolized in the liver to 25-hydroxyvitamin D (25-OH-D-(OH)2D] . Only 103 levels, which reflect the whole intake of vitamin D, comprehensive sun exposure, integrations, and food intake. In recent decades, it has been observed that there is an increasing evidence of a global vitamin D deficiency (VDD) for all ages (3 were identified (The best indicator of vitamin D status is serum 25(OH)Dall ages , 16 owinall ages . Howeverentified .The cellular and molecular mechanisms involved in the pathogenesis of food allergy are very complex and encompass genetic, epigenetic, and environmental factors –21. SeveThe current studies in the literature on the possible role of vitamin D in the development of food allergy have been reported in The first reports regarding a possible association between food allergy and VDD came from the observation that there was a direct relationship among increasing latitude and cases of anaphylaxis, prescription of epinephrine autoinjector, or food allergy–related admissions , 40–44. 3 levels in autumn and winter months; in the higher latitudes, there is no sufficient UVB intensity in the cooler months for proper synthesis of 25(OH)D3 to occur, irrespective of sunlight exposure Dexposure . In addiexposure , in theiexposure .3 levels were correlated with an increase in IgE in a non-linear relationship. Furthermore, higher rates of food sensitization have been seen in infants born to mothers with low vitamin D intake in pregnancy. Nwaru et al. (3 <50 nmol/L) at 12 months of age were more likely to be affected by challenge-proven food allergy, in particular to peanuts and egg, and to have multiple food allergies, in comparison with those who had appropriate vitamin D levels. Curiously, this connection was clear only among infants of Australian-born parents, hinting a gene–environment interaction D3 levels and skin prick test induration size, specific IgE to milk and specific IgE to casein. For the study purpose, they considered vitamin D deficiency if 25(OH)D3 values were ≤ 20 ng/ml, insufficiency if they were between 21 and 29 ng/ml and adequate when they were ≥30 ng/ml. No statistically significant relationship was found between the CMPA group and healthy controls in terms of 25(OH)D3 levels, nor even milk antigen induration diameter and vitamin D levels of CMPA infants. Hence, they concluded that, at the starting diagnosis of infants with CMPA, routine workup of vitamin D levels may have no benefit. Sardecka et al. D3 was <75 nmol/L and sufficient otherwise. They did not find evidence supporting the link between low levels of 25(OH)D3 and allergic sensitization to various allergens.In a prospective, observational, case–control study that involved 111 children <2 years of age, Ercan et al.  assesseda et al.  examineda et al.  performe3 concentration in newborns as a marker of risk of future development of food allergies. In the study of Mullins et al. (3 levels. In comparison with the reference group (50–74.9 nmol/L), 25(OH)D3 levels of 75 to 99.9 nmol/L were linked to a reduced risk of peanut allergy. At levels of 100 nmol/L or higher, no additional reduction was found, whereas the probability of peanut allergy at levels lower than 50 nmol/L was substantially equal to that of the reference group. The risk of peanut allergy at levels <50 nmol/L was also not significantly different from the reference group. Jones et al. D3 (3 levels and milk sensitization at the age of 2 years. One hundred eighty-six children aged 0 through 4 years were enrolled and regularly followed up for 4 years. The average cord blood 25(OH)D3 level was 23.8 ± 9.5 ng/ml, with a high occurrence of VDD (<20 ng/ml) at birth (42%). A trend was found between low cord blood 25(OH)D3 levels and higher risk of milk sensitization throughout childhood. At the same time, cord blood 25(OH)D3 levels showed an inverse relationship to the risk of milk sensitization at 2 years old, an age at which a greater occurrence of milk sensitization was markedly associated to the risk of asthma development and allergic rhinitis at the age of 4 years. Nonetheless, low cord blood vitamin D levels do not seem linked to a higher risk of allergic rhinitis, eczema, or asthma in early childhood. Hennessy et al.  and atopic outcomes in 649 children recruited at birth and followed from then on, and found that VDD alone was not related with food sensitization. If examined together with SNPs, a significant interaction was found between VDD and IL-4 gene polymorphism. VDD raised the risk of food sensitization among children carrying CC/CT genotypes; comparable but lower relationships were seen for the SNPs. The conclusion of their study is that VDD may enhance the risk of food sensitization among people with specific genotypes. A recent longitudinal Icelandic study, involving 144 children followed up for 6 years, compared infant feeding with particular regard to vitamin D supplementation and 25(OH)3 levels between IgE-sensitized and non-sensitized children at 6 years. They found that, at 1 year of age, IgE-sensitized children had a reduced intake of vitamin D, partially explained by a reduced, but non-significant, vitamin D supplement use and reduced consumption of vitamin D fortified formula. At 6 years, less IgE-sensitized children used vitamin D supplements regularly. Equally, vitamin D integration at 6 years decreased the ratio of IgE sensitization  or 6 years . In conclusion, their data encouraged, for Nordic infants and children, vitamin D intake from diet and supplements.Another matter investigated by some authors has also been the potential link between 25(OH)Ds et al.  on 115 ps et al.  studied 25(OH)D3 , 37. Cha25(OH)D3 , considey et al. , in theiy et al.  focused y et al.  proved t allergy . Yepes-N allergy , in a re allergy  evaluatetization . NonetheFurther issues are about the definition of optimum, deficiency, and insufficiency vitamin D serum levels, not worldwide recognized and rather specific for bone outcomes, but not for global health effects , 6. Low The current reviews , 56–59 cCurrently, there are more evidence to support vitamin D supplementation in pregnancy and infancy, in light of its positive effects. In fact, VDD may boost the risk of food allergy and sensitization among people with particular genotypes . NeverthThe role of vitamin D beyond bone and calcium metabolism is alluring but not fully understood. The association between vitamin D and development of food allergy is contradictory , 2.Potential relationships come from ecologic studies that associate lower sunlight exposure to food allergies. On the other hand, further research found that higher levels of vitamin D might raise the probability of allergic sensitization and food allergy. However, in light of a large literature linking the vitamin D levels to the onset of eczema and allergic diseases, this hormone must be considered as a further chance in the comprehension and treatment of atopic diseases. For this reason, there is an urgent need for well-planned randomized controlled trials on vitamin D supplementation, with particular regard to food allergy, to demonstrate that vitamin D might actually contribute to the prevention of allergic diseases.GR, AG, and LB: conceptualization. AG, LB, and JC: resources. GR, AG, and RM: methodology and writing—review and editing. AG, LB, JC, MG, RM, and GR: writing—original draft preparation. RM and GR: supervision. 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.


Calculations in this evaluation were carried out using multivariate statistical procedures with preceding pre-treatment procedures of the spectral data. Such transformations could remove noise, highlight features, and extract essential wavelengths for quantitative predictions. This frequently significantly improved the predictions. Since selecting the appropriate transformation was not straightforward due to the large numbers of available methods, more comprehensive insight into choosing appropriate and optimized pre-treatments was required. Therefore, the objectives of this study were (i) to compare various pre-processing transformations of spectral data to determine their suitability for modeling soil C and N using NIR spectra (55 pre-treatment procedures were tested), and (ii) to determine which wavelengths were most important for the prediction of C and N. The investigations were carried out on an arable field in South Germany with a soil type of Calcaric Fluvic Relictigleyic Phaeozem (Epigeoabruptic and Pantoclayic), created in the flooding area of the Isar River. The best fit and highest model accuracy for the C  and N models in the calibration and validation modes were achieved using derivations with Savitzky–Golay (SG). This enabled us to calculate the Ct, Corg, and N with an R Near-infrared reflectance spectroscopy (NIRS) has been used successfully to characterize many soil properties, including C, N, cation exchange capacity, and soil texture. In comparison to conventional laboratory methods, NIRS is inexpensive, fast, non-destructive, produces no chemical substances, and requires minimal sample preparation, making it highly attractive. Several soil properties can also be determined with a single scan. Some researchers have noted that this technique can estimate primary soil properties , as well as secondary soil properties  at the same time ,3,4,5,6.Near-infrared spectroscopy measures the reflection of radiation with wavelengths from 750 to 2500 nm. The NIR spectra contain broad bands that are produced by reflections with overlapping wavelengths. Reflections measured by this type of spectroscopy mainly correspond to combinations of vibrational modes with chemical C–H, O–H, overtones, and N–H bonds .The recorded reflectance produces spectra that are unique for this sample. The collected reflectance lines include data on the molecules’ properties in the sample and provide essential information about the sample composition.However, the raw spectra also contain some information that complicates the evaluation. For example, background interference, overlapping absorption bands from other constituents, noise , and weak deflections can make the mathematical derivations between light and particles more difficult or sometimes even impossible.Therefore, it is common practice to carry out pre-processing and special calibration/validation procedures to reduce such effects and obtain valuable information about the soil samples’ properties.According to Gholizadeh et al. ,5,6, theAlthough many pre-processing transformations have been used in NIRS, the choice between them is somewhat arbitrary, and little is known about how this choice affects the final prediction of soil properties ,15. IndeAccording to Gholizade et al. ,5,6 and The smoothing procedure is used to reduce noise and is also referred to as a measurement error. Commonly used smoothing methods include the moving average, median filters, and Savitzky–Golay transformation . In currA scaling procedure is applicable when the intensity of different spectra needs to be scaled so that the specifications can be compared. Scaling produces a spectrum with a mean of zero and a standard deviation of 1. Here, the procedure corrects light scattering by centering and scaling each spectrum.Using baseline removal, background signals that are far from the zero lines are removed ,6,19,20.Normalization generates new spectral data via the creation of shifted and scaled data. With these normalized values, scattering effects  are eliminated.Some soil parameters are frequently derived with vis-NIR spectroscopy. The most apparent properties are soil organic carbon (SOC), total soil carbon, soil organic matter (humus content), and clay. However, achievements of the quality of carbon are rare. The applied methods for deriving these and other soil properties are listed in Vasques et al. ,15. MainThis table provides the results from a query of the ResearchGate database to select publications featuring the application of pre-processing methods to predict soil properties over the last ten years.Within this catalogue of transformations, some methods were more frequently used than others, including smoothing with different search windows, normalization, standardization, and derivation with Savitzky–Golay ,32,35,36The selection of multivariate techniques was similarly difficult. Upon the first examination, regressions seemed to be an appropriate statistical method for building reliable calibration models. However, the multicollinearity of highly correlated wavelengths required additional methods. These methods were mainly principal component regression (PCR) and partial least squares regression (PLSR). Instead of using the original data, these methods develop orthogonal (uncorrelated) linear combinations from the spectral variables (components or factors). Other methods have also been established for analyzing spectrometer readings, including support vector machine (SVM), soft independent modeling of a class anthology (SIMCA), cluster analysis (CA), discriminant partial least square (DPLS), K-nearest neighbors (KNN), and linear discriminant analysis (LDA). A comprehensive review of the relevant qualitative methods and their applications can be found in Cen and He .The derivation of a calibration model also involves validations. A commonly used validation method is internal or cross-validation, where the validation dataset is a part of the calibration dataset. In contrast, external validations use an independent data sample set (test set) and provide more reliable and relevant estimates. In general, in both procedures, it is necessary to select a representative sample set that provides the most considerable amount of information for the calculation, including as many variations as possible that may occur in future samples .Among all multivariate statistics, PLSR has been applied the most frequently. Dotto et al.  searchedThe soil investigated in this work occupies a particular position among the various soils investigated, and its high clay content in combination with high C-content has not yet been described in the literature. The most frequently examined soil types are Ultisol, Spodosol, Entisol, Cambisol, Luvisol, Oxisol, and Alfisol, as shown in Therefore, the specific objectives were the following: (i) To compare various pre-processing transformations of spectral data to determine their suitability for modeling C  and N using NIR spectra with PLSR, and (ii) to evaluate which wavelengths are most important for the prediction of the selected parameters.The study area is approximately 1.0 ha in size and is located in Freising, 30 km north of Munich, Germany , in a formerly flooded area of the Isar River.The average annual temperature in the area is approximately 7.8 °C, and the average annual precipitation is 800 mm. Holocene sediments are the predominant soil material. The composition of this flat area is a consequence of repeated floods with periods of soil-forming processes. According to the German Soil Survey  and the A total of 120 soil samples were taken from 60 geo-referenced positions down to a depth of 1 m in April 2011 . Soil saSamples were air-dried at room temperature, cleaned of their visible plant residues, and then sieved using a 2-mm mesh sieve.All samples were subjected to determination of their soil texture , siltcoarse (20–63 µm), soil pH, C , and N). Soil carbon was analyzed by the dry combustion method using a Costech ECS 4010 Carbon–Nitrogen analyzer .The soil texture was analyzed according to VDLUFA .The soil samples’ spectral reflectance was determined using an FT NIRS Bruker Vector 22/N  with a spectral range of 833–2703 nm. The soil samples were distributed homogeneously in glass Petri dishes (9 cm diameter). The constant rotations captured large areas of the sample. The PbS detector scanned an area approximately 2 cm in diameter. A metal stamp (822 g) was used to compress the soil in the dishes to make the sample density uniform and avoid the influence of external light.During the measurements, a gold standard plate was scanned every 40 min during calibration. Three replications were recorded from each sample. In this way, obviously wrong measurements were identified and eliminated during the measurements. Correct readings were averaged and used for the calculations.Pre-processing techniques and multivariate statistical methods were applied using the Unscrambler 10.5 . All comTo determine the transformation that delivers the most accurate fit, 55 combinations were tested. All selected transformations are frequently cited in the literature ,35,36,41The compilation of pre-processing procedures in For smoothing, the readings here were smoothed with different levels. In the first case, the search window was 3, and in the other case, the window was 11 or 25. The derivation techniques of the Savitzky–Golay first derivative, second-order polynomial, Savitzky–Golay second derivative, and second-order polynomial was chosen. The same additional procedure was always selected within each smoothing, search window, and derivation variation . The first was applied as a control treatment, where the raw reflectance values were not pre-treated.Such pre-treatment procedures have already been described in the literature .Smoothing was used to reduce background or instrumental noise. The expression of 3 smoothing points indicated 1 point on the right and 1 point on the left of the smoothed point. Eleven smoothing points described 5 points on the left and 5 points on the right of the smoothed reading.After applying the pre-processing techniques, the next step was to build prediction/calibration models with the soil’s physical and chemical properties as dependent target variables.This PLSR extracted a set of components from the spectra relevant to the target variables . The indCalibration and validation models were assessed to determine their predictive qualities using different indices. Model calibration was performed based on the differences between observed and predicted values after fitting the model using the entire data set. Model validation and the performance of the pre-processing procedures and multivariate statistical methods were evaluated using randomly selected data subsets. For this purpose, the data set was randomly divided into 20 groups, with 15 observations in each group. Ninety percent of the groups were used for model training, and one group was the validation set (prediction set) for model testing. This division in cross-validation and prediction sets was replicated two times.2), the root mean square error (RMSE), the mean of the difference between the measured and calculated values (Bias), the standard error of prediction (SEP), the ratio of the performance deviation for predicted data (RPDpre), and the ratio of performance deviation for the measured data (RPDlab) [2 (the coefficient of determination) is a commonly used measure for the goodness of fit. The RMSE is also commonly used to measure differences between the calculated and observed values from the measured model. The advantage of RMSE is its use of the same data units:The applied indices were the coefficient of determination (R(RPDlab) ,43. R2  and predicted (pred).2 and RPD values are dimensionless so that they can be compared similarly between models for different components/properties. Higher values for the RPD indicate more accurate models. The RMSE should be lower than the SD, and the RPD should ideally be four or higher  is a less-often-used index. According to Bellon–Maurel et al. (2010), the soil samples often show skewed distributions instead of normal distributions. The quartiles in the RPIQ better represent the spread of the population.RPIQ is the difference between the first and third quartiles (Q75–Q25), where Q25 is the value in 25% of the samples, and Q75 is the value in 75% of the samples.Variables that have a significant effect are considered essential variables. In addition, the variables involved in important interactions are also important, even if their main effects are negligible.In such cases, Martens’ automatic uncertainty test indicates the significant variables in the multicomponent model .The reflection curves are represented by the different soil properties, which differ in their position and composition. The shapes of the curves vary between 0.2 and 0.6. Changes of a darker and more whitish color with distinctly different absorbance characteristics  are obseThe same figure shows that the spectral reflectance increased gradually with wavelengths from 900–1400 nm. The reflectance reached its first reflection peak between 1390 and 1400 nm. Three more significant reflection peaks appeared at 1450, 1860, and 2120 nm. The two reflection peaks at 1420 and 1930 nm were also characteristic of soil curves. The reflection curves were affected by color, particle size, moisture, and mineral composition . The soil reflectance curves in the NIR region for different soil types, along with the most critical parts for water and carbon and their responsible chemical groups, are shown in Vasques et al. .-Variation of the Corg content between the surface and subsurface parts caused a distinct differentiation in spectral shapes. Higher Corg content yielded reduced reflectance intensity along with the spectra, recognizable until horizon 4. A similar relationship was detectable at horizon 9 with an Ah-horizon and Corg content of nearly 1.7%;-At horizons 10 and 11, higher values of Ccal produced increased spectral readings over the whole reflection shape. Horizons 5 and 8 indicated a more median position;-The influence of Fe-oxides and -hydroxides was not detectable. Higher Fe content would produce lower reflection intensities. The strong influence of Corg  were the main factors that hindered recognition of the interference between the spectrum and the possible occurrence of these minerals ;-The same conclusions can be drawn for the case of sand. The effect of quartz particles on spectral behavior was restricted by humus and carbonatic coatings;-To summarise, Corg and Ccal dominated the spectral shapes. A sandier horizon, which can reflect more energy, was not recognizable. Increasing clay values with increasing depth was also not clearly detectable.According to Demattê et al. , StenberMultivariate calibrations were carried out with PLSR and leave-one-out cross-validation procedures for the 55 pre-processed spectra.Pre-processing techniques were used to improve the spectral characteristics and optimize the relationships with the soil properties of interest. As described before, these techniques were divided into the data treatments of smoothing, the degree of the search window, derivation, and additional techniques.An evaluation of the relevant literature indicates that there is no single or combined pre-processing technique(s) that is optimal for all datasets  and RMSE values of 0.09%/0.25% . The high RPD and RPIQ values of 7.16/10.92 and 2.58/4.08, respectively, for calibration/validation, were another indication of the model’s quality. The results using the procedure of the Savitzky–Golay second derivative, second-order polynomial, and 11 smoothing segments without an additional technique (SG11-SG2-none) were slightly worse. These results compared well to previous work by other researchers. The studies by Vasques et al. [2 and RPD values (R2 0.86–0.95). A similar R2 value (0.97) was also found in work by Reeves et al. [Ct: The pre-treatment trials showed that weak SG-smoothing combined with the Savitzky-Golay first derivative, first-order polynomial, and no additional technique (SG3-SG1-none) provided the best performance for Ct. With this pre-processing transformation, the PLSR models accurately predicted Ct, with Rs et al. ,15, Sarks et al. ,58, McDos et al. ,25, Knoxs et al.  and Pinhs et al.  describes et al. .2 between 0.98 and 0.91  and an RMSE of 0.11 and 0.22. Both RPD and RPIQ presented levels with 6.3/7.83 and 3.23/4.11. Good model performance was also obtained using the treatments SG11-SG1, SG11-SG2, SG25-SG1, and SG25-SG2 with the other additional technique.Ccalc: For Ccalc, the calculation SG25-Sg2-SNV+det provided excellent calibration and validation accuracy, with an R2 values of 0.99/0.94  and RMSE values of 0.09%/0.24%  in combination with high RPD and RPIQ values of 11.59/22.41 and 4.19/8.42, respectively, for calibration and validation, indicated high quality. The results of the same procedure with the additional technique were slightly worse. The pre-treatment groups SG3-SG2 and SG11-SG2 also provided good predictions.Corg: Here, the best performance was produced with the pre-treatment of SG3-SG1- none. The RThe models developed for organic C values in the literature provided slightly worse results ,28,32,60Several studies reported that vis–NIR relates better to Corg than NIR alone  and RMSE values of 0.01%/0.02% . The high RPD and RPIQ values of 15.83/30.67 and 6.36/12.39, respectively, for calibration/validation also indicated the model’s quality.N: Of all the parameters, N was the best for calculations. The R2 of the calibration was 0.95 combined with an RMSE of 0.3–0.5 and an RPD/RPIQ of 4.5–8.0 and 2.3–4.2, the results of the validations were weaker. A similar level between calibration and validation was not observed. Therefore, the calculations were not useable.C/N: All results here were insufficient. The best results were calculated with the procedures of SG3-SG1 and SG3-SG2 followed by SG11-SG2. While the RThe prediction of Ccarb also indicted relevant wavelengths mainly above 1490 nm . The number of selected wavelengths for the C/N ratio was lower than that for the other parameters. The ranges of 1660–1730, 1900–2050, and above 2170 corresponded to the important values of N and Corg.Several authors have identified the most important single wavelengths and ranges of wavelengths for the prediction of Corg and N. Reflection values around 1100, 1600, 1700–1800, 2000, and 2200–2400 nm have been often described as important for Corg and N calibrations ,67,68,69These values from the literature partly differed from those in the present evaluation. However, it was not easy to make specific assignments using the NIR spectrum because other different organic and inorganic molecules could be absorbed in these areas. This was especially true for wavelengths above 2000 nm. Kuang and Mouazen  provided2 values are over 0.75, the models are sufficient. An additional option here was the development of low-cost wavelength-based sensors to measure organic C.In this context, Bellon–Maurel and McBratney  studied The statistics obtained in this study for calibration, cross-validation, and validation showed that NIRS techniques could effectively provide fast and accurate predictions of Ct, Corg, Ccarb, and N.The number of publications on the application of NIR spectroscopy in soil science has increased in recent years, as this technique has the advantage of a rapid application without intensive sample preparation. Altogether, 55 procedures of pre-treatments were calculated to determine the best soil sample model from a humus–clay-rich soil. In all calculations, the derivatives with Savitzky–Golay produced the lowest errors and enhanced the pre-treatments, such as detrending; MSC and SNV were not required. Derivatives using Savitzky–Golay calculated by differences were very popular to perform baseline corrections and enhance weak signals. However, in the current study, up to a wavelength of about 1400 nm, this procedure yielded severe fluctuations due to scattering in the original data. Thus, the condition of the measurement device, the sample presentation, the replications, and the accuracy during measurement have an enormous impact on the final results. Only an iterative process can help develop the best models. Smoothing can reduce these fluctuations, but up to a search window of 25, the effect was very low in the case of Ct, Corg, and N, while only Ccarb and C/N indicated an improvement. Important wavelengths for the predictions were mainly in a range of about 1400–2500 nm.Further work will be undertaken to improve prediction accuracy by applying non-linear calibration techniques such as support vector machine, random forest.