Machine Learning Detects Pattern of Differences in Functional Magnetic Resonance Imaging (fMRI) Data between Chronic Fatigue Syndrome (CFS) and Gulf War Illness (GWI)

Role of infrastructure and operation in disease prevalence in dairy farms: groundwork for disease prevention-based antibiotic stewardship

Attempts at regulating misuse of antibiotics in the dairy industry have been ineffective, especially in low- and middle-income countries, who also typically have high burden of preventable infectious disease, we propose a disease prevention-based approach to minimize the need and in turn consumption of antibiotics in dairy farms. Since the immediate environment of the animals is key to disease prevalence, we targeted the infrastructure- and operation-related factors in dairy farms and their link with prevalence of most common diseases and symptoms. We conducted four focused group discussions and a cross-sectional survey in 378 dairy farms to investigate disease prevalence and associated infrastructural (housing system, and manger shape), and operational (waste management, feed management, and type of cleaning agent) parameters. The most common diseases (Mastitis and secondary infections related to Foot-and-mouth disease) and symptoms (fever and diarrhoea) in the focus area were linked with the infrastructural and operational factors on the dairy farm with higher disease prevalence reported in dairy farms, where the animals were exposed to variations in diurnal temperatures or were hard to clean. We further used ML classifiers - Neural Network (NN), k-Nearest Neighbour (kNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) - to corroborate the relationship between infrastructure and operations of the dairy farms and disease prevalence- The DT classifier on randomly sampled data could predict the prevalence of the two most common diseases (accuracy = 92%, F1-score = 0.919) Our results open new avenues for cost-effective interventions such as use of curve-edged mangers, use of rubber mats on floors, not reusing leftover feed etc. in dairy farms to prevent the most common diseases and symptoms in dairy farms and reduce the need and consumption of antibiotics.

Tai Chi increases functional connectivity and decreases chronic fatigue syndrome: A pilot intervention study with machine learning and fMRI analysis

BACKGROUND

The latest guidance on chronic fatigue syndrome (CFS) recommends exercise therapy. Tai Chi, an exercise method in traditional Chinese medicine, is reportedly helpful for CFS. However, the mechanism remains unclear. The present longitudinal study aimed to detect the influence of Tai Chi on functional brain connectivity in CFS.

METHODS

The study recruited 20 CFS patients and 20 healthy controls to receive eight sessions of Tai Chi exercise over a period of one month. Before the Tai Chi exercise, an abnormal functional brain connectivity for recognizing CFS was generated by a linear support vector model. The prediction ability of the structure was validated with a random forest classification under a permutation test. Then, the functional connections (FCs) of the structure were analyzed in the large-scale brain network after Tai Chi exercise while taking the changes in the Fatigue Scale-14, Pittsburgh Sleep Quality Index (PSQI), and the 36-item short-form health survey (SF-36) as clinical effectiveness evaluation. The registration number is ChiCTR2000032577 in the Chinese Clinical Trial Registry.

RESULTS

1) The score of the Fatigue Scale-14 decreased significantly in the CFS patients, and the scores of the PSQI and SF-36 changed significantly both in CFS patients and healthy controls. 2) Sixty FCs were considered significant to discriminate CFS (P = 0.000, best accuracy 90%), with 80.5% ± 9% average accuracy. 3) The FCs that were majorly related to the left frontoparietal network (FPN) and default mode network (DMN) significantly increased (P = 0.0032 and P = 0.001) in CFS patients after Tai Chi exercise. 4) The change of FCs in the left FPN and DMN were positively correlated (r = 0.40, P = 0.012).

CONCLUSION

These results demonstrated that the 60 FCs we found using machine learning could be neural biomarkers to discriminate between CFS patients and healthy controls. Tai Chi exercise may improve CFS patients' fatigue syndrome, sleep quality, and body health statement by strengthening the functional connectivity of the left FPN and DMN under these FCs. The findings promote our understanding of Tai Chi exercise's value in treating CFS.

Identification of Resting-State Network Functional Connectivity and Brain Structural Signatures in Fibromyalgia Using a Machine Learning Approach

Abnormal resting-state functional connectivity (rs-FC) and brain structure have emerged as pathological hallmarks of fibromyalgia (FM). This study investigated and compared the accuracy of network rs-FC and brain structural features in identifying FM with a machine learning (ML) approach. Twenty-six FM patients and thirty healthy controls were recruited. Clinical presentation was measured by questionnaires. After MRI acquisitions, network rs-FC z-score and network-based gray matter volume matrices were exacted and preprocessed. The performance of feature selection and classification methods was measured. Correlation analyses between predictive features in final models and clinical data were performed. The combination of the recursive feature elimination (RFE) selection method and support vector machine (rs-FC data) or logistic regression (structural data), after permutation importance feature selection, showed high performance in distinguishing FM patients from pain-free controls, in which the rs-FC ML model outperformed the structural ML model (accuracy: 0.91 vs. 0.86, AUC: 0.93 vs. 0.88). The combined rs-FC and structural ML model showed the best performance (accuracy: 0.95, AUC: 0.95). Additionally, several rs-FC features in the final ML model correlated with FM's clinical data. In conclusion, ML models based on rs-FC and brain structural MRI features could effectively differentiate FM patients from pain-free subjects.

Effects of the Prolong Life With Nine Turn Method (Yan Nian Jiu Zhuan) Qigong on Brain Functional Changes in Patients With Chronic Fatigue Syndrome in Terms of Fatigue and Quality of Life

Background

Chronic fatigue syndrome (CFS) is characterized by persistent fatigue, which often leads to physical and psychological damage. The Prolong Life with Nine Turn method (PLWNT) Qigong is considered as one of the complementary treatments for improving symptoms in patients with CFS. In this study, we used functional magnetic resonance imaging (fMRI) to explore the effects of PLWNT intervention on the subjects with CFS.

Methods

Thirty four CFS patients were randomly divided into PLWNT group and cognitive behavioral therapy (CBT) group. Both groups were taught by a highly qualified professor at the Shanghai University of Traditional Chinese Medicine once a week and were supervised online during the remaining 6 days at home, over 12 consecutive weeks. We calculated the regional rs-fMRI index amplitude of low-frequency fluctuations (ALFF) for all subjects. To study the changes of the brain network, we used the brain regions with significant differences in ALFF as the regions of interest for whole-brain functional connectivity (FC) analysis. The Multi-dimensional Fatigue Inventory 20 (MFI-20) and Short Form 36-item Health Survey (SF-36) were used for clinical symptom assessment to explore the possible correlation between the rs-fMRI indicators and clinical variations.

Results

The ALFF values of the right superior frontal gyrus (SFG), and left median cingulate gyrus (DCG) were increased, whereas those of the left middle occipital gyrus (OG), right middle OG and left middle temporal gyrus (MTG) were decreased in PLWNT group. The FC values between the DCG and middle temporal gyrus (MTG), and those between the left OG and the right OG were enhanced. In addition, the SF-36 were positively with the left OG (r = 0.524), SFG (r = 0.517), and DCG (r = 0.533), MFI-20 were negatively with the SFG (r = −0.542) and DCG (r = −0.578). These results were all corrected by FWE (voxel level p < 0.001, cluster level p < 0.05).

Conclusion

CFS patients have abnormal regional spontaneous neuronal activity and abnormal functional connections between regions after PLWNT intervention. PLWNT can relieve the fatigue symptoms of CFS patients and improve their quality of life. The study was registered in the American Clinical Trial Registry (12/04/2018). Registration Number is NCT03496961.

Ponesimod inhibits astrocyte-mediated neuroinflammation and protects against cingulum demyelination via S1P1 -selective modulation

Ponesimod is a sphingosine 1‐phosphate (S1P) receptor (S1PR) modulator that was recently approved for treating relapsing forms of multiple sclerosis (MS). Three other FDA‐approved S1PR modulators for MS—fingolimod, siponimod, and ozanimod—share peripheral immunological effects via common S1P₁ interactions, yet ponesimod may access distinct central nervous system (CNS) mechanisms through its selectivity for the S1P₁ receptor. Here, ponesimod was examined for S1PR internalization and binding, human astrocyte signaling and single‐cell RNA‐seq (scRNA‐seq) gene expression, and in vivo using murine cuprizone‐mediated demyelination. Studies confirmed ponesimod’s selectivity for S1P₁ without comparable engagement to the other S1PR subtypes (S1P_2,3,4,5). Ponesimod showed pharmacological properties of acute agonism followed by chronic functional antagonism of S1P₁. A major locus of S1P₁ expression in the CNS is on astrocytes, and scRNA‐seq of primary human astrocytes exposed to ponesimod identified a gene ontology relationship of reduced neuroinflammation and reduction in known astrocyte disease‐related genes including those of immediate early astrocytes that have been strongly associated with disease progression in MS animal models. Remarkably, ponesimod prevented cuprizone‐induced demyelination selectively in the cingulum, but not in the corpus callosum. These data support the CNS activities of ponesimod through S1P₁, including protective, and likely selective, effects against demyelination in a major connection pathway of the brain, the limbic fibers of the cingulum, lesions of which have been associated with several neurologic impairments including MS fatigue.

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Exercise modifies glutamate and other metabolic biomarkers in cerebrospinal fluid from Gulf War Illness and Myalgic encephalomyelitis / Chronic Fatigue Syndrome

Myalgic encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) and Gulf War Illness (GWI) share many symptoms of fatigue, pain, and cognitive dysfunction that are not relieved by rest. Patterns of serum metabolites in ME/CFS and GWI are different from control groups and suggest potential dysfunction of energy and lipid metabolism. The metabolomics of cerebrospinal fluid was contrasted between ME/CFS, GWI and sedentary controls in 2 sets of subjects who had lumbar punctures after either (a) rest or (b) submaximal exercise stress tests. Postexercise GWI and control subjects were subdivided according to acquired transient postexertional postural tachycardia. Banked cerebrospinal fluid specimens were assayed using Biocrates AbsoluteIDQ® p180 kits for quantitative targeted metabolomics studies of amino acids, amines, acylcarnitines, sphingolipids, lysophospholipids, alkyl and ether phosphocholines. Glutamate was significantly higher in the subgroup of postexercise GWI subjects who did not develop postural tachycardia after exercise compared to nonexercise and other postexercise groups. The only difference between nonexercise groups was higher lysoPC a C28:0 in GWI than ME/CFS suggesting this biochemical or phospholipase activities may have potential as a biomarker to distinguish between the 2 diseases. Exercise effects were suggested by elevation of short chain acylcarnitine C5-OH (C3-DC-M) in postexercise controls compared to nonexercise ME/CFS. Limitations include small subgroup sample sizes and absence of postexercise ME/CFS specimens. Mechanisms of glutamate neuroexcitotoxicity may contribute to neuropathology and “neuroinflammation” in the GWI subset who did not develop postural tachycardia after exercise. Dysfunctional lipid metabolism may distinguish the predominantly female ME/CFS group from predominantly male GWI subjects.

Neuroimaging Markers for Studying Gulf-War Illness: Single-Subject Level Analytical Method Based on Machine Learning

Gulf War illness (GWI) refers to the multitude of chronic health symptoms, spanning from fatigue, musculoskeletal pain, and neurological complaints to respiratory, gastrointestinal, and dermatologic symptoms experienced by about 250,000 GW veterans who served in the 1991 Gulf War (GW). Longitudinal studies showed that the severity of these symptoms often remain unchanged even years after the GW, and these veterans with GWI continue to have poorer general health and increased chronic medical conditions than their non-deployed counterparts. For better management and treatment of this condition, there is an urgent need for developing objective biomarkers that can help with simple and accurate diagnosis of GWI. In this study, we applied multiple neuroimaging techniques, including T1-weighted magnetic resonance imaging (T1W-MRI), diffusion tensor imaging (DTI), and novel neurite density imaging (NDI) to perform both a group-level statistical comparison and a single-subject level machine learning (ML) analysis to identify diagnostic imaging features of GWI. Our results supported NDI as the most sensitive in defining GWI characteristics. In particular, our classifier trained with white matter NDI features achieved an accuracy of 90% and F-score of 0.941 for classifying GWI cases from controls after the cross-validation. These results are consistent with our previous study which suggests that NDI measures are sensitive to the microstructural and macrostructural changes in the brain of veterans with GWI, which can be valuable for designing better diagnosis method and treatment efficacy studies.