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The prevalence, onset, pathophysiology, and clinical course of many neuromuscular disorders (NMDs) may significantly differ between males and females. Some NMDs are more frequently observed in females, and characterized to show a higher grade of severity during or after the pregnancy. Meanwhile, others tend to have an earlier onset in males and exhibit a more variable progression. Prevalently, sex differences in NMDs have a familiar character given from genetic inheritance. However, they may also influence clinical presentation and disease severity of acquired NMD forms, and are represented by both hormonal and genetic factors. Consequently, to shed light on the distinctive role of biological factors in the different clinical phenotypes, we summarize in this review the sex related differences and their distinctive biological roles emerging from the current literature in both acquired and inherited NMDs.

We describe a case of intrathecal methotrexate toxicity and perform a literature review of existing cases.A 23-year-old man who received diagnosis of acute lymphoblastic leukemia and started chemotherapy according to the LAL1913 protocol underwent CNS prophylaxis with intrathecal methotrexate. About 1 month after, he developed a flaccid paraparesis. CSF analysis showed albumin/cytological dissociation. Spinal MRI showed thickening of the ventral roots of the cauda equina with contrast enhancement. Nerve conduction studies showed severe lower limb motor axonal neuropathy. Needle examination showed acute denervation involving L3-S1 roots. Methotrexate was stopped, and the patient was treated with intravenous immunoglobulins, followed by high-dose intravenous methylprednisolone, with a gradual improvement. Three months later, the spine MRI was normal. Electrophysiological and imaging findings were indicative of pure motor L3-S1 polyradiculopathy.Literature review of existing cases confirm the relatively selective involvement of lumbosacral ventral roots in intrathecal methotrexate toxicity. Pathophysiologic mechanisms suggest either a direct toxicity with localized folate deficiency or an immune-mediated mechanism, the latter consistent, in our patient, with the albumin/cytological dissociation and response to immunomodulatory treatments. Pure motor polyradiculopathy of the lower limbs is rare but predictable complication of intrathecal methotrexate, which can benefit from early withdrawal and immunomodulatory treatments.

Temporal muscle thickness (TMT) is a new potential MRI biomarker, which has shown prognostic relevance in neuro-oncology. We aim at investigating the potential prognostic value of TMT in patients with Amyotrophic Lateral Sclerosis (ALS). We retrospectively evaluated 30 ALS patients, whose clinical, Magnetic Resonance Imaging (MRI) and Electrodiagnostic testing (EDX) data were available, in comparison to age-matched 30 healthy subjects. TMT calculated on T1-weighted MR images was significantly lower in ALS patients than in healthy subjects (p&nbsp

Gastrointestinal (GI) symptoms have been described in a conspicuous percentage of coronavirus disease 2019 (COVID-19) patients. This clinical evidence is supported by the detection of viral RNA in stool, which also supports the hypothesis of a possible fecal-oral transmission route. The involvement of GI tract in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is corroborated by the theoretical assumption that angiotensin converting enzyme 2, which is a SARS-CoV-2 target receptor, is present along the GI tract. Studies have pointed out that gut dysbiosis may occur in COVID-19 patients, with a possible correlation with disease severity and with complications such as multisystem inflammatory syndrome in children. However, the question to be addressed is whether dysbiosis is a consequence or a contributing cause of SARS-CoV-2 infection. In such a scenario, pharmacological therapies aimed at decreasing GI permeability may be beneficial for COVID-19 patients. Considering the possibility of a fecal-oral transmission route, water and environmental sanitation play a crucial role for COVID-19 containment, especially in developing countries.

Hyposmia is a common finding in Parkinson’s disease (PD) and is usually tested through the University of Pennsylvania Smell Identification Test (UPSIT). The aim of our study is to provide a briefer version of the Italian-adapted UPSIT test, able to discriminate between PD patients and healthy subjects (HS). By means of several univariate and multivariate (machine-learning-based) statistical approaches, we selected 8 items by which we trained a partial-least-square discriminant analysis (PLS-DA) and a decision tree (DT) model: class predictions of both models performed better with the 8-item version when compared to the 40-item version. An area under the receiver operating characteristic (AUC-ROC) curve built with the selected 8 odors showed the best performance (sensitivity 86.8%, specificity 82%) in predicting the PD condition at a cut-off point of ≤ 6. These performances were higher than those previously calculated for the 40-item UPSIT test (sensitivity 82% and specificity 88.2 % with a cut-off point of ≤ 21). Qualitatively, our selection contains one odor (i.e., apple) which is Italian-specific, supporting the need for cultural adaptation of smell testing; on the other hand, some of the selected best discriminating odors are in common with existing brief smell test versions validated on PD patients of other cultures, supporting the view that disease-specific odor patterns may exist and deserve a further evaluation.

Background Congenital malformations of the central nervous system (CNS) consist of a wide range of birth defects of multifactorial origin. Methods Concentrations of 44 metals were determined by Inductively Coupled Plasma Mass Spectrometry in serum of 111 mothers in the second trimester of pregnancy who carried a malformed fetus and compared them with serum concentrations of the same metals in 90 mothers with a normally developed fetus at the same week of pregnancy. Data are reported as means ± standard deviations. Results We found a direct relationship between congenital defects of the CNS and maternal serum concentration of aluminum: it was statistically higher in women carrying a fetus with this class of malformation, compared both to mothers carrying a fetus with another class of malformation (6.45 ± 15.15 μg/L Vs 1.44 ± 4.21 μg/L, p Conclusion CAluminum may play a role in the onset of central nervous system malformations, although the exact Aluminum species and related specific type of malformation needs further elucidation.

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder in which environmental (lifestyle, dietary, infectious disease) factors as well as genetic make-up play a role. Metabolomics, an evolving research field combining biomarker discovery and pathogenetics, is particularly useful in studying complex pathophysiology in general and Parkinson's disease (PD) specifically. PD, the second most frequent neurodegenerative disorder, is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of intraneural inclusions of α-synuclein aggregates. Although considered a predominantly movement disorder, PD is also associated with number of non-motor features. Metabolomics has provided useful information regarding this neurodegenerative process with the aim of identifying a disease-specific fingerprint. Unfortunately, many disease variables such as clinical presentation, motor system involvement, disease stage and duration substantially affect biomarker relevance. As such, metabolomics provides a unique approach to studying this multifactorial neurodegenerative disorder.

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder in which environmental (lifestyle, dietary, infectious disease) factors as well as genetic make-up play a role. Metabolomics, an evolving research field combining biomarker discovery and pathogenetics, is particularly useful in studying complex pathophysiology in general and Parkinson's disease (PD) specifically. PD, the second most frequent neurodegenerative disorder, is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of intraneural inclusions of ?-synuclein aggregates. Although considered a predominantly movement disorder, PD is also associated with number of non-motor features. Metabolomics has provided useful information regarding this neurodegenerative process with the aim of identifying a disease-specific fingerprint. Unfortunately, many disease variables such as clinical presentation, motor system involvement, disease stage and duration substantially affect biomarker relevance. As such, metabolomics provides a unique approach to studying this multifactorial neurodegenerative disorder.

Objective Heart anomalies represent nearly one-third of all congenital anomalies. They are currently diagnosed using ultrasound. However, there is a strong need for a more accurate and less operator-dependent screening method. Here we report a metabolomics characterization of maternal serum in order to describe a metabolomic fingerprint representative of heart congenital anomalies. Methods Metabolomic profiles were obtained from serum of 350 mothers (280 controls and 70 cases). Nine classification models were built and optimized. An ensemble model was built based on the results from the individual models. Results The ensemble machine learning model correctly classified all cases and controls. Malonic, 3-hydroxybutyric and methyl glutaric acid, urea, androstenedione, fructose, tocopherol, leucine and putrescine were determined as the most relevant metabolites in class separation. Conclusion The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal heart anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the revelation of the associated metabolites and their respective biochemical pathways allows a better understanding of the overall pathophysiology of affected pregnancies. This article is protected by copyright. All rights reserved.

Background The pediatric obesity epidemic calls for the noninvasive detection of individuals at higher risk of complications. Aims To investigate the diagnostic role of combined salivary uric acid (UA), glucose and insulin levels to screen noninvasively for metabolic syndrome (MetS) and nonalcoholic fatty liver disease. Methods Medical history, clinical, anthropometric, and laboratory data including serum triglyceride, glucose, insulin, HOMA, HDL-cholesterol, and UA levels of 23 obese children (15 with [St+] and 8 without [St−] ultrasonographic hepatic steatosis) and 18 normal weight controls were considered. Results Serum and salivary UA (p Conclusions Salivary testing together with selected anthropometric parameters helps to identify noninvasively obese children with hepatic steatosis and/or having MetS components.

About 90% of cases of Parkinson’s disease (PD) are idiopathic and attempts to understand pathogenesis typically assume a multifactorial origin. Multifactorial diseases can be studied using metabolomics, since the cellular metabolome reflects the interplay between genes and environment. The aim of our case–control study is to compare metabolomic profiles of whole blood obtained from treated PD patients, de-novo PD patients and controls, and to study the perturbations correlated with disease duration, disease stage and motor impairment. We collected blood samples from 16 drug naive parkinsonian patients, 84 treated parkinsonian patients, and 42 age matched healthy controls. Metabolomic profiles have been obtained using gas chromatography coupled to mass spectrometry. Multivariate statistical analysis has been performed using supervised models; partial least square discriminant analysis and partial least square regression. This approach allowed separation between discrete classes and stratification of treated patients according to continuous variables (disease duration, disease stage, motor score). Analysis of single metabolites and their related metabolic pathways revealed unexpected possible perturbations related to PD and underscored existing mechanisms that correlated with disease onset, stage, duration, motor score and pharmacological treatment. Metabolomics can be useful in pathogenetic studies and biomarker discovery. The latter needs large-scale validation and comparison with other neurodegenerative conditions.

Bladder cancer has a high incidence and is marked by high morbidity and mortality. Early diagnosis is still challenging. The objective of this study was to create a metabolomics-based profile of bladder cancer in order to provide a novel approach for disease screening and stratification. Moreover, the study characterized the metabolic changes associated with the disease. Serum metabolomic profiles were obtained from 149 bladder cancer patients and 81 healthy controls. Different ensemble machine learning models were built in order to: (1) differentiate cancer patients from controls, (2) stratify cancer patients according to grading, (3) stratify patients according to cancer muscle invasiveness. Ensemble machine learning models were able to discriminate well between cancer patients and controls, between high grade (G3) and low grade (G1-2) cancers and between different degrees of muscle invasivity, ensemble model accuracies were ≥80%. Relevant metabolites, selected using the partial least square discriminant analysis (PLS-DA) algorithm, were included in a metabolite-set enrichment analysis, showing perturbations primarily associated with cell glucose metabolism. The metabolomic approach may be useful as a non-invasive screening tool for bladder cancer. Furthermore, metabolic pathway analysis can increase understanding of cancer pathophysiology. Studies conducted on larger cohorts, and including blind trials, are needed to validate results.

Endometrial cancer (EC) is the most common cancer of the female reproductive tract in developed countries. At the moment, no effective screening system is available. Here, we evaluate the diagnostic performance of a serum metabolomic signature. Two enrollments were carried out, one consisting of 168 subjects: 88 with EC and 80 healthy women, was used for building the classification models. The second (used to establish the performance of the classification algorithm) was consisted of 120 subjects: 30 with EC, 30 with ovarian cancer, 10 with benign endometrial disease, and 50 healthy controls. Two ensemble models were built, one with all EC versus controls (Model I) and one in which EC patients were aggregated according to their histotype (Model II). Serum metabolomic analysis was conducted via gas chromatography-mass spectrometry, while classification was done by an ensemble learning machine. Accuracy ranged from 62% to 99% for the Model I and from 67% to 100% for the Model II. Ensemble model showed an accuracy of 100% both for Model I and II. The most important metabolites in class separation were lactic acid, progesterone, homocysteine, 3-hydroxybutyrate, linoleic acid, stearic acid, myristic acid, threonine, and valine. The serum metabolomics signature of endometrial cancer patients is peculiar because it differs from that of healthy controls and from that of benign endometrial disease and from other gynecological cancers (such as ovarian cancer).

Central nervous system anomalies represent a wide range of congenital birth defects, with an incidence of approximately 1% of all births. They are currently diagnosed using ultrasound evaluation. However, there is strong need for a more accurate and less operator-dependent screening method. To perform a characterization of maternal serum in order to build a metabolomic fingerprint resulting from congenital anomalies of the central nervous system. This is a case–control pilot study. Metabolomic profiles were obtained from serum of 168 mothers (98 controls and 70 cases), using gas chromatography coupled to mass spectrometry. Nine machine learning and classification models were built and optimized. An ensemble model was built based on results from the individual models. All samples were randomly divided into two groups. One was used as training set, the other one for diagnostic performance assessment. Ensemble machine learning model correctly classified all cases and controls. Propanoic, lactic, gluconic, benzoic, oxalic, 2-hydroxy-3-methylbutyric, acetic, lauric, myristic and stearic acid and myo-inositol and mannose were selected as the most relevant metabolites in class separation. The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal central nervous system anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is therefore a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the details of the most relevant metabolites and their respective biochemical pathways allow better understanding of the overall pathophysiology of affected pregnancies.

Bisphenol A (BPA) is a widely distributed estrogen-mimetic molecule, with well-established effects on the dopaminergic system. It can be found in canned food, dental sealants, thermal paper, etc. BPA undergoes liver conjugation with glucuronic acid and is subsequently excreted in the urine.In the present study we quantified the concentration of free and conjugated Bisphenol A in blood of patients affected by Parkinson Disease, using their spouses as controls.An interview was performed to determine possible confounders in BPA exposure. Free and conjugated BPA were quantified by gas chromatography coupled with mass spectrometry.Parkinson's Disease patients carried a statistically significant lower amount of conjugated Bisphenol A compared to controls. The two populations were mostly homogeneous in terms of exposure to possible Bisphenol A sources. The only exceptions were exposure to canned tuna and canned tomatoes PD patients consumed significantly more of both (p0.05). Moreover, no difference in Bisphenol A glucuronidation was found after stratification by typology of anti-Parkinson's drug taken and after conversion to the Levodopa Equivalent Daily Dose.BPA glucuronidation was decreased in patients with Parkinson disease. The possible unique mechanisms underlying Bisphenol A metabolism in PD patients deserve further elucidation. Moreover, further study is needed to assess a possible BPA role in Parkinson's Disease pathogenesis, due to its documented dopaminergic toxicity.

Chromosomal anomalies (CA) are the most frequent fetal anomalies. To evaluate the diagnostic performance of a machine learning ensemble model based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester . This is a case-control pilot study. Metabolomic profiles have been obtained on serum of 328 mothers (220 controls and 108 cases), using gas chromatography coupled to mass spectrometry. Eight machines learning and classification models were built and optimized. An ensemble model was built using a voting scheme. All samples were randomly divided into two sets. One was used as training set, the other one for diagnostic performance assessment. Ensemble machine learning model correctly classified all cases and controls. The accuracy was the same for trisomy 21 and 18; also, the other CA were correctly detected. Elaidic, stearic, linolenic, myristic, benzoic, citric and glyceric acid, mannose, 2-hydroxy butyrate, phenylalanine, proline, alanine and 3-methyl histidine were selected as the most relevant metabolites in class separation. The proposed model, based on the maternal serum metabolomic fingerprint of fetal aneuploidies during the second trimester, correctly identifies all the cases of chromosomal abnormalities. Overall, this preliminary analysis appeared suggestive of a metabolic environment conductive to increased oxidative stress and a disturbance in the fetal central nervous system development. Maternal serum metabolomics can be a promising tool in the screening of chromosomal defects. Moreover, metabolomics allows to extend our knowledge about biochemical alterations caused by aneuploidies and responsible for the observed phenotypes.

To get insight into still elusive pathomechanisms of pediatric obesity and non-alcoholic fatty liver disease (NAFLD) we explored the interplay among GC-MS studied urinary metabolomic signature, gut liver axis (GLA) abnormalities, and food preferences (Kid-Med). Intestinal permeability (IP), small intestinal bacterial overgrowth (SIBO), and homeostatic model assessment-insulin resistance were investigated in forty children (mean age 9.8 years) categorized as normal weight (NW) or obese (body mass index 95th percentile, respectively) ± ultrasonographic bright liver and hypertransaminasemia (NAFLD). SIBO was increased in all obese children (p = 0.0022), IP preferentially in those with NAFLD (p = 0.0002). The partial least-square discriminant analysis of urinary metabolome correctly allocated children based on their obesity, NAFLD, visceral fat, pathological IP and SIBO. Compared to NW, obese children had (1) higher levels of glucose/1-methylhistidine, the latter more markedly in NAFLD patients; and (2) lower levels of xylitol, phenyl acetic acid and hydroquinone, the latter especially in children without NAFLD. The metabolic pathways of BCAA and/or their metabolites correlated with excess of visceral fat centimeters (leucine/oxo-valerate), and more deranged IP and SIBO (valine metabolites). Urinary metabolome analysis contributes to define a metabolic fingerprint of pediatric obesity and related NAFLD, by identifying metabolic pathways/metabolites reflecting typical obesity dietary habits and GLA perturbations.