Anterior lumbosacral polyradiculoneuropathy following intrathecal methotrexate administration: a case report and literature update.

INTRODUCTION: We describe a case of intrathecal methotrexate toxicity and perform a literature review of existing cases. CASE PRESENTATION: 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. DISCUSSION: 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.

Screening performances of an 8-item UPSIT Italian version in the diagnosis of Parkinson's disease.

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.

Noninvasive screening for congenital heart defects using a serum metabolomics approach.

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.

A metabolomic signature of treated and drug-naïve patients with Parkinson's disease: a pilot study.

INTRODUCTION: 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. OBJECTIVE: 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. METHODS: We collected blood samples from 16 drug naïve 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. RESULTS: 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. CONCLUSION: Metabolomics can be useful in pathogenetic studies and biomarker discovery. The latter needs large-scale validation and comparison with other neurodegenerative conditions.

Serum metallome in pregnant women and the relationship with congenital malformations of the central nervous system: a case-control study.

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 < 0.0006) and to Controls (i.e. mothers carrying a normally-developed fetus) (6.45 ± 15.15 µg/L Vs 0.11 ± 0.51 µg/L, p < 0.0006). Moreover, Aluminum abundances were below the limit of detection in the majority of control samples. 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.

Metabolomic Signature of Endometrial Cancer.

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).

A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies.

BACKGROUND: 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. OBJECTIVES: To perform a characterization of maternal serum in order to build a metabolomic fingerprint resulting from congenital anomalies of the central nervous system. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Sex differences in neuromuscular disorders.

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.

Microbiota dysbiosis influences immune system and muscle pathophysiology of dystrophin-deficient mice.

Duchenne muscular dystrophy (DMD) is a progressive severe muscle-wasting disease caused by mutations in DMD, encoding dystrophin, that leads to loss of muscle function with cardiac/respiratory failure and premature death. Since dystrophic muscles are sensed by infiltrating inflammatory cells and gut microbial communities can cause immune dysregulation and metabolic syndrome, we sought to investigate whether intestinal bacteria support the muscle immune response in mdx dystrophic murine model. We highlighted a strong correlation between DMD disease features and the relative abundance of Prevotella. Furthermore, the absence of gut microbes through the generation of mdx germ-free animal model, as well as modulation of the microbial community structure by antibiotic treatment, influenced muscle immunity and fibrosis. Intestinal colonization of mdx mice with eubiotic microbiota was sufficient to reduce inflammation and improve muscle pathology and function. This work identifies a potential role for the gut microbiota in the pathogenesis of DMD.

Temporal muscle thickness and survival in patients with amyotrophic lateral sclerosis.

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 < 0.001), correlating with the ALS Functional Rating Scale (FRS) (p:0.018) and compound motor action potential (CMAP) (p:0.012) in the patients group. Multivariate analysis of overall survival (OS) showed that the only parameters that remained significant were TMT (p:0.002, OR 0.45, 95%vCI: 0.28-0.75) and ALS FRS-R (p:0.023, OR: 0.80, 95%CI: 0.67-0.92). TMT seems to be a promising surrogate biomarker of survival and functional status in ALS. Our data deserve further investigations in multicenter and prospective trials.

The Spectrum of PRRT2-Associated Disorders: Update on Clinical Features and Pathophysiology.

Mutations in the PRRT2 (proline-rich transmembrane protein 2) gene have been identified as the main cause of an expanding spectrum of disorders, including paroxysmal kinesigenic dyskinesia and benign familial infantile epilepsy, which places this gene at the border between epilepsy and movement disorders. The clinical spectrum has largely expanded to include episodic ataxia, hemiplegic migraine, and complex neurodevelopmental disorders in cases with biallelic mutations. Prior to the discovery of PRRT2 as the causative gene for this spectrum of disorders, the sensitivity of paroxysmal kinesigenic dyskinesia to anticonvulsant drugs regulating ion channel function as well as the co-occurrence of epilepsy in some patients or families fostered the hypothesis this could represent a channelopathy. However, recent evidence implicates PRRT2 in synapse functioning, which disproves the "channel hypothesis" (although PRRT2 modulates ion channels at the presynaptic level), and justifies the classification of these conditions as synaptopathies, an emerging rubric of brain disorders. This review aims to provide an update of the clinical and pathophysiologic features of PRRT2-associated disorders.

COVID-19 and the gastrointestinal tract: Source of infection or merely a target of the inflammatory process following SARS-CoV-2 infection?

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.

Metabolomics in Parkinson's disease.

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.

Machine Learning Approaches in Parkinson's Disease.

BACKGROUND: Parkinson's disease is the second most frequent neurodegenerative disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At present, no biomarker is available to obtain a diagnosis of certainty in vivo. OBJECTIVE: The present review aims at describing machine learning algorithms as they have been variably applied to different aspects of Parkinson's disease diagnosis and characterization. METHODS: A systematic search was conducted on PubMed in December 2019, resulting in 230 publications obtained with the following search query: "Machine Learning" "AND" "Parkinson Disease". RESULTS: The obtained publications were divided into 6 categories, based on different application fields: "Gait Analysis - Motor Evaluation", "Upper Limb Motor and Tremor Evaluation", "Handwriting and typing evaluation", "Speech and Phonation evaluation", "Neuroimaging and Nuclear Medicine evaluation", "Metabolomics application", after excluding the papers of general topic. As a result, a total of 166 articles were analyzed after elimination of papers written in languages other than English or not directly related to the selected topics. CONCLUSION: Machine learning algorithms are computer-based statistical approaches that can be trained and are able to find common patterns from big amounts of data. The machine learning approaches can help clinicians in classifying patients according to several variables at the same time.

The Therapeutic use of the Zonulin Inhibitor AT-1001 (Larazotide) for a Variety of Acute and Chronic Inflammatory Diseases.

BACKGROUND: The involvement of intercellular tight junctions and, in particular, the modulation of their competency by the zonulin pathway with a subsequent increase in epithelial and endothelial permeability, has been described in several chronic and acute inflammatory diseases. In this scenario, Larazotide, a zonulin antagonist, could be employed as a viable therapeutic strategy. OBJECTIVE: The present review aims to describe recent research and current observations about zonulin involvement in several diseases and the use of its inhibitor Larazotide for their treatment. METHODS: A systematic search was conducted on PubMed and Google Scholar, resulting in 209 publications obtained with the following search query: "Larazotide," "Larazotide acetate," "AT-1001," "FZI/0" and "INN-202." After careful examination, some publications were removed from consideration because they were either not in English or were not directly related to Larazotide. RESULTS: The obtained publications were subdivided according to Larazotide's mechanism of action and different diseases: celiac disease, type 1 diabetes, other autoimmune diseases, inflammatory bowel disease, Kawasaki disease, respiratory (infective and/or non-infective) diseases, and other. CONCLUSION: A substantial role of zonulin in many chronic and acute inflammatory diseases has been demonstrated in both in vivo and in vitro, indicating the possible efficacy of a Larazotide treatment. Moreover, new possible molecular targets for this molecule have also been demonstrated.

Bipolar Disorder and Parkinson's Disease: A 123I-Ioflupane Dopamine Transporter SPECT Study.

Objectives: Bipolar disorder (BD) has been suggested to be a risk factor for the development of Parkinson's disease (PD). Standard treatment of BD includes drugs that are known to induce drug-induced parkinsonism (DIP). Clinical differentiation between PD and DIP is crucial and might be aided by functional neuroimaging of the dopaminergic nigrostriatal pathway. Methods: Twenty consecutive BD patients with parkinsonism were clinically assessed and underwent 123I-ioflupane dopamine transporter single-photon emission computer tomography (SPECT). Imaging data of BD patients with pathological scans were further compared to a population of 40 de novo PD patients. Results: Four BD patients had abnormal scans, but their clinical features and cumulative exposure to both antipsychotic drugs and lithium were similar to those of BD patients with normal dopamine transporter imaging. BD patients with pathological scans had putaminal binding ratio and putamen-to-caudate ratios higher than those of PD patients despite a similar motor symptom burden. Conclusions: Up to 20% of BD patients with parkinsonism might have an underlying dopaminergic deficit, which would not be due to cumulative exposure to offending drugs and is ostensibly higher than expected in the general population. This supports the evidence that BD represents a risk factor for subsequent development of neurodegenerative parkinsonism, the nature of which needs to be elucidated.

Salivary markers of hepato-metabolic comorbidities in pediatric obesity.

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 < 0.05; R2 = 0.51), insulin (p < 0.0001; R2 = 0.79), and HOMA (p < 0.0001; R2 = 0.79) levels were significantly correlated; however their values tended to be only slightly higher in the obese patients, predominately in [St+], than in the controls. Notably, UA and insulin levels in both fluids increased in parallel to the number of MetS components. After conversion of the z-logit function including salivary/anthropometric parameters in a stepwise logistic regression analysis, a factor of 0.5 allowed for predicting hepatic steatosis with high sensitivity, specificity, and total accuracy. CONCLUSIONS: Salivary testing together with selected anthropometric parameters helps to identify noninvasively obese children with hepatic steatosis and/or having MetS components.

Bisphenol A glucuronidation in patients with Parkinson's disease.

BACKGROUND: 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. OBJECTIVES: 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. METHODS: An interview was performed to determine possible confounders in BPA exposure. Free and conjugated BPA were quantified by gas chromatography coupled with mass spectrometry. RESULTS: 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 (p<0.05). 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. CONCLUSION: 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.

Urinary Metabolomics in Pediatric Obesity and NAFLD Identifies Metabolic Pathways/Metabolites Related to Dietary Habits and Gut-Liver Axis Perturbations.

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 <85th or >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.