Theories have suggested that food-specific inhibition training could lead to food devaluation which, in turn, may help people to regulate their eating behavior. In this review, we have synthesized the current literature on this topic by conducting a meta-analysis of studies investigating the effects of food-specific inhibition training on food evaluation. We identified 24 studies—with 36 independent samples, 77 effect sizes, and 3032 participants—that met our inclusion criteria. Effect sizes were analyzed using the robust variance estimation in random effects meta-regression technique. The results indicate that food-specific inhibition training can lead to statistically significant reductions in food evaluation. More specifically, it was observed that the effects of training on participants’ food evaluation differed according to the type of evaluation; food-specific inhibition training significantly decreased participants’ explicit food evaluation, but not their implicit food evaluation. However, because most of the included studies focused on trained food items and short-term outcomes in normal-weight samples, more research is needed on the continuance of the training effects, as well as on the extent to which effects can be generalized to untrained food items or different populations . Eating behaviors can be defined as the internal driving force for the approach and ingestion of food . DysfuncOne way of devaluing appetitive foods is via food-specific inhibition training ,8. Inhibp-curve analyses, please see ) (see τ2 = 0.03), indicating that the effects of the training on food evaluation were relatively consistent across various conditions. Nevertheless, we explored the effects of moderators that were expected a priori to play an important role in the effects of food-specific inhibition training  see , indicattraining .t(5.55) = 3.23, p = 0.020); food-specific inhibition training significantly decreased participants’ explicit food evaluation , but not their implicit food evaluation .We first examined whether the type of evaluation moderated the effects of the training on food evaluation, given that the evidence for the effect of food-specific inhibition training on implicit food evaluation is relatively weak. Results showed that the type of evaluation moderated the effects of training on food evaluation  = 0.16, p = 0.728).We next examined whether the training model moderated the effects of the training on food evaluation, given prior work suggesting that the go/no-go task has a larger effect on changing unhealthy behaviors  than the stop-signal task . Results showed that the training paradigm did not moderate the effects of food-specific inhibition training on food evaluation , but not the evaluation of generalized food  or mixed food ; however, these differences were not found to be statistically significant  = 4.33, p = 0.071).We then examined whether food novelty moderated the effects of the training on food evaluation. Results showed that food-specific inhibition training significantly lowered the evaluation of trained food  for overweight or obese individuals were not significantly different from the effects  for normal-weight individuals (t(8.35) = 1.24, p = 0.316).Furthermore, we examined whether weight status moderated the effects of food-specific inhibition training on food evaluation. Results showed that the effects (p > 0.626).Finally, exploratory analysis showed that none of age, sex, or length of follow-up moderated the effects of food-specific inhibition training on food evaluation . Among seven moderators examined, we found that the effect of food-specific inhibition training was significantly moderated by the type of evaluation (t(5.55) = 3.23, p = 0.020). In particular, food-specific inhibition training significantly decreased explicit  but not implicit food evaluation . These results are discussed below, with a focus on the theoretical and practical applications.Many people today live in an obesogenic food environment, and are constantly exposed to low-nutritive-value yet appetitive foods—for example, foods containing large amounts of sugar and fat . These eThere are various theories to explain the food devaluation effect of food-specific inhibition training. For example, the BSI theory ,28 propor =0.44). Although such results are still nascent, these functional magnetic resonance imaging (fMRI) studies and future neuroscience studies similar to them can directly measure value signals in the brain, and may provide further conclusive evidence for the food devaluation effect of food-specific inhibition training [The food devaluation effect observed in the meta-analysis is supported by recent neuroimaging studies ,67. Moretraining .The magnitude of the food devaluation effect observed in the meta-analysis was small-to-medium. Future studies should examine whether this small-to-medium effect size has any practical or real-world significance. For example, it was proposed that the decrease in food evaluation may play a critical role in promoting healthier eating behaviors. Supporting this notion are the findings of Veling et al. , who shoTo further highlight contextual factors that may influence the training effects, this meta-analysis examined the effects of potential moderators, including the type of evaluation, training paradigm, food novelty, weight status, age, sex, and length of follow-up.Our results indicated that the effect of food-specific inhibition training on food evaluation was moderated by the type of evaluation. In particular, food-specific inhibition training was found to have a statistically significant effect on explicit food evaluation, but not on implicit food evaluation. Similarly, a previous meta-analysis showed that repeated inhibition of behaviors in response to appetitive stimuli  does not change implicit evaluation of these stimuli . Taken tWe found no other statistically significant moderators. Researchers have speculated that, compared to the stop-signal task, higher food-inhibition contingency in the go/no-go task might result in a greater degree of effectiveness . In thisg+ = 0.291) were numerically larger than on the evaluation of generalized food (g+ = 0.130) or mixed food (g+ = 0.150), the differences were not statistically significant. It should be noted that there were only eight and nine studies that investigated the effects of training on generalized food and mixed food, respectively, which might result in an insufficiency of statistical power when conducting the moderator analysis. Therefore, future studies or meta-analyses should continue to test the generalization effects of food-specific inhibition training.Food novelty was also found not to moderate the effects of food-specific inhibition training on food evaluation. A series of experiments conducted by Chen et al. ,40 showeWeight status also did not emerge as a statistically significant moderator, suggesting that food-specific inhibition training causes similar decreases in food evaluation in normal-weight and overweight/obese participants. However, it should be noted that only seven food-specific inhibition training studies were conducted with people with excessive weight or obesity. Therefore, more studies focusing on overweight or obese individuals are needed before food-specific inhibition training can be translated into clinical interventions.Despite its strengths, this meta-analysis has limitations. Firstly, relative to the main analysis, statistical power might be low for some of the moderator analyses. For example, as previously mentioned, not many studies recruited people with excessive weight or obesity, or focused on whether there were generalization effects of food-specific inhibition training. Similarly, only four studies investigated the persistence of the training effects on food evaluation, and only three studies used the stop-signal task. As such, when additional related studies have been conducted, updated moderator analyses will be warranted. Secondly, there may be additional moderators  of the effects of food-specific training on food evaluation that were unaccounted for in our analysis. For example, Chen et al.  only obsIn conclusion, our meta-analysis supports the idea that food-specific inhibition training can produce beneficial changes in food evaluation. In particular, the type of evaluation moderated the effects of training on food evaluation, with food-specific inhibition training significantly decreasing participants’ explicit, but not their implicit, food evaluation. However, since most of the included studies focused on trained food items and short-term outcomes in normal-weight samples, more research is needed on the persistence of the training effects, and on the extent to which the effects can be generalized to untrained food items or different populations .


A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%. In recent years there has been an apparent increase in interest in brain tumour diseases, which affect humans severely and are life threatening. Brain cancer is the tenth most-common primary reason for death in men and women. As per the International Agency for Research on Cancer, approximately 126,000 people worldwide are diagnosed with brain tumours each year, with a death rate of over 97,000 [While abnormal tissues such as tumours, necrosis, and oedema. Necrosis is the death of cells inside an active tumour, whereas oedema occurs near the active tumour boundaries ,3,4. MalMedical experts have claimed for many years that detecting brain tumours in clinics with human interpretation is difficult. Due to this, there is an urgent need for more reliable early tumour detection techniques such as computer-aided diagnosis (CAD) ,11,12. CMedical imaging techniques are critical in detecting tumours early and improving treatment options. Brain tumours are studied using various non-invasive imaging techniques, including CT, MRI, SPECT, PET, and X-ray ,17. The Image classification is a machine learning (ML) method used to train a computer system to be an “expert system”. It uses classified medical images of the medicine domain for diagnostic and teaching purposes. The classification process of medical images is based on pre-processing by extracting features; for example, brain images are used to determine and classify the type of tumour ,21. NumeDeep learning (DL) is the current state of the art, having gained popularity and widespread interest in every field, particularly medical image analysis . Deep leIt introduces a hybrid deep learning model that detects brain tumours in the early stages in order to accelerate the treatment process and control the spread of the malignant tissues.It proves that using hybrid deep learning classification, which combines Google-Net with SVM, gives higher accuracy and better results than traditional methods.It helps radiologists avoid errors from manual diagnosis of tumours through magnetic resonance images (MRI) images without invasive measures.However, this study uses a new brain tumour classification method based on the hybrid CNN-SVM method through MRI brain images. The contribution of this study could be summarized in the following points: The remainder of this paper is structured as follows. ML and DL methods have recently been widely used for the detecting and classification brain tumours using various imaging modalities, particularly those obtained using MRI brain images. This section presents the most relevant and recent research work related to the study in this paper. Bahadur et al., 2017  proposedThis paper’s primary goal and motivation is to present a new hybrid deep-learning model method. The proposed approach investigated two different hybridizations. The first combines Google-Net (GN) with SVM and is named GN-SVM, and the second combines Google-Net with fine-tuning and is named GN-FT. Fine-tuning is a process that takes a model that has already been trained (pre-trained) for one task and returns it or tweaks the same model to perform a classification task ,44. AssuThe fine-tuning technique is utilized in this study by using a pre-trained neural network to transfer and classify MRI brain images. This method uses the Google-Net model combined with the fine-tuning technique (GN-FT) to generate an output. After feature extraction from MRI brain images using the convolution layers of the Google-Net model, the fine-tuning technique applies a full connection layer (FCL) to classify MRI brain images into four classes—glioma, meningioma, and pituitary tumours as abnormal tissue or non-tumours as normal tissue—based on extracted features.Algorithm 1: Proposed Fine-Tuning TechniqueInputs: Training and testing images.Outputs: Calculated accuracy.Select the optimal value of features for determining Fine-Tuning output.Fine-Tuning Steps (condition of output limitation).Step 1: Load pre-trained Google-Net model of CNN Step 2: Truncate the pre-trained network’s last layer (softmax layer) and replace it with our new output layer that is relevant to our problem.Step 3: Add an output layer to the target model, whose number of outputs is the number of categories in the target dataset.Step 4: Freeze the weights of the pre-trained network’s first few layers. The first few layers capture universal features such as curves and edges, which are also relevant to our new problem.Step 5: Start training the new model structure while keeping those weights intact, with the network focusing on learning dataset-specific features in the subsequent layers.Step 6: The output Layer yields one of four classes: Not-Tumour.Glioma-Tumour.Meningioma-Tumour.Pituitary-Tumour.Return accuracyThe main steps to apply the fine-tuning technique on features extracted from the inputting images via a convolutional layer (CL) of the Google-Net model, as explained in Algorithm 1, are as follows. First, freeze the weights of the first few layers that have already from the already trained (pre-trained) network. This is followed by adding a new output layer (soft-max) into the target model, whose number of outputs is the number of categories in the target dataset. Finally, the Google-Net model will be ready to re-train the new dataset by setting the initial layers to zero. The freezing process of the layers increases the network’s training speed. We used the finely tuned Google-Net from The architecture of Google-Net is shown in The depth of the Google-Net architecture is 22 layers with 27 pooling layers. There are nine inception modules in total, and they are stacked linearly. The endpoints of the inception modules are linked to the global average pooling layer. In this section, the finely tuned Google-Net was used to extract the MRI medical features from the last layer, which generated a feature vector of length 1024D, as shown in The hybrid deep learning algorithm uses the finely tuned Google-Net model of the CNN technique based on two classification methods. First, it combines Google-Net with the support vector machine (SVM) classifier technique (GN-SVM). Second, it combines Google-Net with fine-tuning techniques (GN-FT), as shown in Support vector machines (SVM) are one of the common traditional machine learning classifiers and are related to supervised learning methods. The SVM technique is a commonly used classifier in various fields, including handwriting analysis, face analysis, medical image analysis, and so on. It is particularly useful for pattern detection and regression-based applications ,52,53,54Algorithm 2: The proposed SVM classifier techniqueInputs: Training and testing images.Outputs: Calculated accuracy.Select the optimal value of cost and gamma for SVM.While (stopping condition is not met) DoStep 1: Implement the SVM train step for each data point.Step 2: Implement SVM classification for testing data points.Step 3: Define SVM-based kernel as Where x, y belong to the samples of feature space in the training set parameter.Step 4: The objective is to have four classes:       1- Normal (Not Tumour)      2- Tumour, with three subclasses:1.Glioma.2.Meningioma.3.Pituitary.End WhileReturn accuracyThe SVM classifier technique is used in this proposed method to classify MRI brain images into four classes: glioma, meningioma, and pituitary tumours as abnormal tissue or non-tumours as normal tissue. After feature extraction from MRI brain images using convolution layers of the Google-Net model, the SVM algorithm will apply the full connection layer (FCL) as explained in Algorithm 2 to classify brain tumours based on the extracted features. In this paper, we have applied data augmentation techniques to a large dataset of 3460 different types of brain MRI images . The datTo evaluate the proposed use of hybrid deep-learning classification based on CNN architecture in the grading of brain tumours as glioma, meningioma, and pituitary tumours or as normal brain (No_Tumour), in this study, standard evaluation measures were used; the measures used in this study were precision, recall, and accuracy.TP), false positive (FP), false negative (FN), and true negative (TN), were calculated and used to evaluate the performance of the proposed classification system, as shown below [From the proposed method obtained by using the dataset, three statistical indices, namely true positive AAll experiments were carried out and evaluations were conducted using MATLAB R2021b programming on a laptop computer equipped with an Intel Core-I5 processor, 20 GB of RAM, and a Hard SSD type. Full specification details are given in As explained earlier, the dataset used in this study contains two types of MRI brain images, some having normal tissue and some having abnormal tissue. The normal images are considered as “not a tumour”, and the abnormal images as having different types of tumours, namely meningioma, glioma, and pituitary tumours. This part of the research compares the execution time of two methods, GN-SVM and GN-FT. The execution time of the GN-SVM method is 0.097 s, and the GN-FT method is 0.098 s per single image, as www.kaggle.com website: (https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection (accessed on 1 May 2022)). It contains 253 images of the normal brains of persons and 155 images of the brains of persons who have tumours, and 98 images of persons without tumour. Some sample images from the MRI brain dataset are shown in Further analysis was performed by validating the performance of the proposed approach with an additional benchmark dataset. We used the MRI brain images dataset, which was obtained from different patients gathered from several hospitals, WHO , and the Whole Brain Atlas site, which was published by In this experiment, the dataset has been divided into 80% to 20% for training and testing, respectively. The outcomes of the proposed approach and other methods are given in In this paper, we have presented a new hybrid deep-learning classification method based on CNN-SVM. This method combines a finely tuned Google-Net model with SVM to identify and classify the tumour types through MRI brain images and developed a new hybrid CNN architecture that efficiently automated the classification of MRI brain image datasets into four classes: meningioma, glioma, pituitary tumour, and not a tumour. The suggested technique outperforms existing deep-learning methods in recall, 98.1%, precision, 98.2%, and accuracy, 98.1%. The hybrid CNN method performed more rapidly among deep-learning methods, with higher classification accuracy. The reported results showed that the proposed approach could be used as a diagnostic tool to help the radiologist with highlighting suspicious brain regions. However, further manual inspection is needed to confirm these cases.This method is suitable for locating and detecting tumours easily. In the future, we are planning to use other CNN models such as Squeeze-Net combined with the SVM technique and fine-tuning technique to classify tumour brain type from MRI brain images.


Automated analysis, modeling, and interpretation of the high-dimensional multimodal data is an active research direction in the areas of immunology and immuno-oncology. In general, there is a growing need for the systems biology perspective and data analyses, as well as the corresponding computational methodology development, in these interrelated fields.For this Research Topic, we sought both applied and computational/methodological research papers at the interface of immuno-oncology and computational systems biology, with an emphasis on immune scores and prognostic signatures.Zhao et al. presented an analysis strategy involving protein-protein-interaction (PPI) and TF-lncRNA-miRNA-mRNA regulatory networks to identify differentially expressed prognostic genes and scrutinize the potential regulatory mechanisms in cervical cancer tumor immune microenvironments. Four candidate prognostic genes were identified from the TCGA data and validated in the independent GEO cervical cancer cohort, with an eye towards increasing immunotherapy efficiency and exploration of novel treatment avenues. This study highlighted the utility of combining PPI and multifactor regulatory networks in a unified analysis framework; such multi-level network analyses  are a clear and welcome trend in the field.Multi-modal, integrative, network analyses emerged as a common theme in the immuno-oncological computational systems biology domain. Chen et al. concentrated on cervical cancer but used LASSO regression to construct and refine the immune prognostic signature based on the lncRNA data. The resulting six-lncRNA immune prognostic signature (LIPS) was in silico validated by training/testing dataset subdivision, and its prognostic accuracy was estimated by ROC curves, univariate, and multivariate Cox regression analyses, using different datasets. This compact LIPS was shown to be an independent risk factor for cervical cancer, thus adding to both more accurate prognostic assessment and, potentially, further insights into immunotherapy strategies. While no network modeling per se was performed, gene set enrichment analysis suggested LIPS association with the immune response and tumor-related signaling pathways; subsequent scrutiny revealed patients with high and low LIPS being in different immune states.Likewise, Zhu et al. also adopted an integrative network approach to identifying and validating immune signatures. Here, TCGA and GEO databases were screened for differentially expressed genes, and a PPI network was constructed for enriched immune-related genes. The prognostic role of “hub” genes in the network was assessed via the Kaplan-Meyer plotter and OncoLnc databases; qualified “hub” immune-related genes subsequently led to the lncRNA–miRNA–mRNA ceRNA (competing endogenous RNA) network construction. Tumor-infiltrating immune cells’ landscape was also explored, and linked to the “hub” genes. Ultimately, two immune-related ceRNA networks were found to be involved in lung adenocarcinoma progression, presenting two novel directions for targeted immunotherapy.Moving on from cervical cancer to lung adenocarcinoma, Gibbs et al. study, wherein high-level network features, representing signaling “axes”, were linked with the clinical outcomes from the large-scale TCGA patient samples’ datasets. Cell-cell edges associated with predominant  cell subtypes were found to be prognostic and predictive with respect to clinical phenotypes; in addition, genes from the differential networks were used for gene ontology term enrichment, leading to, among others, five immune-related themes. As a part of the study, novel methodologies for cell-cell communication network construction, weighting, and interpretation were developed and tested.Network modeling on the cell level, namely patient-specific cell-cell communication, was the subject of in silico, thus partially sidestepping the “dimensionality curse”. The research papers in this Research Topic are exemplary in these regards, and clearly outline the attractive methodological template for ongoing and future investigations in the field.The above studies exemplify the emerging and prospective trends in immuno-oncological computational systems biology research: first, integrated and multi-level network analyses; second, emphasis on the wide variety of methods, ranging from “conventional” statistical and machine learning approaches to novel and original network-centered algorithms and tools; third, combining different available modalities into the single analysis framework/pipeline. The two major challenges facing the field are the robustness of the network modeling, and the sheer variety of the methods and their assumptions. These two issues are interrelated. For example, there is only a limited concordance between the different network modeling methods in the scRNA-seq gene regulatory network space, and many of the network modeling methods are unsuccessful in recovering the whole “ground truth” . LikewisIt remains to note that while most of the so-called multi-level or integrative network analyses are in fact sequential , it would be beneficial to incorporate many different immuno-oncology–related datatypes into the same multiscale network, similar to the recent methodological work in other domains, such as Alzheimer’s Disease -omics . There a


Stroke alters muscle co-activation and notably leads to exaggerated antagonist co-contraction responsible for impaired motor function. However, the mechanisms underlying this exaggerated antagonist co-contraction remain unclear. To fill this gap, the analysis of oscillatory synchronicity in electromyographic signals from synergistic muscles, also called intermuscular coherence, was a relevant tool.This study compares functional intermuscular connectivity between muscle pairs of the paretic and non-paretic upper limbs of stroke subjects and the dominant limb of control subjects, concomitantly between two muscle pairs with a different functional role, through an intermuscular coherence analysis.Twenty-four chronic stroke subjects and twenty-four healthy control subjects were included. Subjects performed twenty elbow extensions while kinematic data and electromyographic activity of both flexor and extensor elbow muscles were recorded. Intermuscular coherence was analyzed in the beta frequency band compared to the assessment of antagonist co-contraction.Intermuscular coherence was higher in the stroke subjects’ paretic limbs compared to control subjects. For stroke subjects, the intermuscular coherence of the antagonist-antagonist muscle pair  was higher than that of the agonist-antagonist muscle pair . For the paretic limb, intermuscular coherence of the antagonist-antagonist muscle pair presented a negative relationship with antagonist co-contraction.Differences in intermuscular coherence between the paretic limbs of stroke subjects and control subjects suggest a higher common central drive during movement. Furthermore, results highlight the association between stroke-related alteration of intermuscular functional connectivity and the alteration of motor function. Stroke leads to chronic motor deficits and involves impairment of voluntary motor skills consecutive to the central and peripheral alteration of neuromuscular function . These c15. In control subjects, authors have demonstrated a link between IMC and changes in muscular synergies induced by different tasks, suggesting that IMC reflects a neural control strategy of functional coordination . For all subjects, the exclusion criteria were the presence of comprehension disorder, neurodegenerative disease, and pain in the upper limbs during active elbow extension. Patients had to be free of botulinum toxin A injections in the elbow flexors for at least four months. They also had to be able to actively extend the elbow by at least 20°.Twenty-four unilateral chronic stroke subjects (mean ± standard deviation of time after stroke: 37.56 ± 49.44 months) and twenty-four healthy control subjects were age-matched . Eighteen stroke subjects were included in a routine care protocol (No. ID-RCB: 2017-A01616-47), and one stroke subject and fifteen healthy control subjects in an interventional protocol (No. ID-RCB: 2017-A01616-47). All these studies took place at the University Hospital of Toulouse and were conducted in accordance with the Declaration of Helsinki. All subjects provided written informed consent before study entry.As presented in Kinematic continuous data were low-pass filtered at 6 Hz . The onsSpastic co-contraction, here more appropriately called antagonist co-contraction, was calculated as the average of the elbow’s flexors (BB & BR) electromyographic root mean square value during elbow extension, normalized by their average root mean square value during maximal isometric voluntary elbow flexion contraction . This maFor IMC analysis, the continuous EMG data were segmented from 3 s before and 3 s after each movement to limit the alteration of the ends of the signals during the pre-processing. Then the signals were normalized to account for inter-movement time variability . During EMG1/EMG2  is the wavelet cross-spectrum between EMG time series at frequency ω and time u; SEMG1  and SEMG2  are wavelet auto-spectra of EMG time series at frequency ω and time u. Then, IMC was quantified in the β frequency band and in a time window of 200 ms before maximal peak velocity as the volume under the IMC values, which were previously detected as significant  by controlling potential covariables: angular velocity, elbow angle , and agep < 0.017. For the partial Spearman correlation, significance was set at p < 0.05.For the ANCOVAs, significance was corrected at p = 0.02, d = 0.69, 95% CI = [0.08:1.29])  = 2.38, 8:1.29]) .p = 0.015) and a muscle pair effect only for the paretic and non-paretic limbs of stroke subjects (p = 0.016) (The ANCOVAs showed a significant limb effect between the paretic limb of stroke subjects and the dominant limb of control subjects = 0.016) , 6. , 6.p = BB–BR   .This study aimed to assess the differences in IMC between stroke and control subjects in both a muscle pair composed of antagonist muscles (BB-BR) and a muscle pair composed of an agonist muscle and an antagonist muscle (TB-BR). This approach allowed us to consider the functional role  of the synergistic muscles constituting the pairs implicated in changes in intermuscular functional connectivity after stroke.First, our results showed higher antagonist co-contraction for the paretic limb of stroke than for control subjects. This result fully agrees with the literature  and highConcomitantly, our key results showed a significantly higher IMC when calculated from muscle pairs of the paretic limb of stroke subjects compared with the muscle pairs of the dominant limb of control subjects. In addition, our results showed significantly higher IMC for the BB-BR muscle pair compared to the TB-BR, only for stroke patients. Finally, a negative linear link between the IMC of the BB-BR muscle pair and antagonist co-contraction of the paretic limb of stroke subjects were highlighted.While controlling for possible methodological biases and differences in motor function between subjects, we found that IMC calculated from the muscles of stroke subjects’ paretic limb was higher when compared to the dominant limb of control subjects. This result agrees with our hypotheses, as we expected to observe a difference in motor control between the paretic limb of stroke subjects and control subjects. These results are also in agreement with most of the literature studying the differences in IMC between stroke and control subjects for muscle pairs composed of an agonist and an antagonist’s muscles, where IMC appears higher for muscles of stroke subjects . HoweverDifferent hypotheses could explain the effect of stroke on the IMC of the paretic limb. First, it is interesting to discuss the observed IMC differences concerning De Luca and Erim’s common drive theory . These aSecondly, these findings can also be discussed in relation to the alteration of spinal mechanisms after stroke. Indeed, spinal regulatory mechanisms can contribute to modifying IMC because they regulate efferences and differences since, in stroke subjects, reciprocal  and recuThe changes in IMC observed in stroke patients could reflect the mechanisms underlying the regulation of muscular synergistic co-activation and their alteration, resulting in a modification of antagonist-agonist co-contraction. IMC appears to be higher for the antagonist-antagonist muscle pair (BB-BR) compared to the agonist-antagonist muscle pair (TB-BR) only for stroke subjects. A more common drive directed to muscles with the same functional role could be explained by a “simple, functional control scheme to compensate for lower efficiency in motor execution” , which cOur results indicate for the first time a negative relationship between antagonist co-contraction and IMC of the antagonist-antagonist muscle pair (BB-BR) in the paretic limb of stroke subjects during active elbow extension. This finding agrees with that of The IMC differences between the stroke subjects’ paretic limb and the dominant limb of control subjects reinforced the results of the literature about the effects of stroke on intermuscular connectivity and completed it with an analysis of pairs of muscles with the same and opposite functional roles. Moreover, the negative correlation in stroke subjects’ paretic limbs between antagonist co-contractions of these muscles and their IMC could highlight a functional simplification of motor control in this population. Finally, the IMC appeared to be higher for muscles with the same functional role (BB-BR) for stroke patients, corroborating this hypothesis of simplifying motor control.Conclusions on IMC should be treated with caution given the possible influence of velocity and muscle length on IMC values , althougAll the data that support the findings of this study are available from the principal investigator DG and the scripts are available from the corresponding author, DA, upon request. Requests to access these datasets should be directed to DA.The studies involving human participants were reviewed and approved by Protection of Persons Committee : No. ID-RCB: 2017-A01616-47; Research Ethics Committee of Toulouse University Hospitals: No. 07-0716; Protection of Persons Committee (Sud-Est 1): No. ID-RCB: 2017-A01616-47. The patients/participants provided their written informed consent to participate in this study.CD: conceptualization, methodology, software, formal analysis, investigation, data curation, and writing . CC: resources, writing (review and editing). AC: investigation, software, and writing (review and editing). DA: methodology, software, and writing (review and editing). DG: software, resources, writing (review and editing), and project administration. All authors contributed to the article and approved the submitted version.


Although inflammation is a known predictor for poor prognosis in patients with diabetics, few data report the synergistic association between inflammation, malnutrition, and mortality in patients with diabetics. We aim to explore whether malnutrition modifies the predictor of inflammation on prognosis.Nutritional status and inflammation were measured in 6,682 patients with diabetics undergoing coronary angiography or percutaneous coronary intervention between January 2007 to December 2018 from Cardiorenal Improvement Registry. Malnutrition was defined as Controlling Nutritional Status (CONUT) score, which was more than 1. High-sensitivity C-reactive protein (hs-CRP) exceeding the median was assessed as a high-risk inflammation. Cox regression models were used to estimate hazard ratios (HR) for mortality across combined hs-CRP and CONUT score categories.p < 0.001). The p-value for interaction between nutritional status and hs-CRP level on all-cause mortality was 0.03.During a median follow-up of 5.0 years (interquartile range: 3.0–7.6 years), 759 (11.36%) patients died. The mortality of the four groups  were 7.29, 7.12, 10.71, and 17.31%, respectively. Compared with normal nutrition and low hs-CRP level, an isolated condition of either malnutrition or high hs-CRP level was not associated with any significant risk for all-cause mortality. However, concomitant presence of both high hs-CRP level and malnutrition condition was associated with a significantly increased risk of all-cause mortality (HR: 1.51; 95% CI: 1.20–1.89; The interplay of inflammation and malnutrition in patients with diabetics significantly amplifies the deleterious effects of each as distinct disease entities. A prospective randomized clinical trial is needed in the future to verify the results. Diabetes mellitus (DM) is a highly prevalent disease and associated with a large cardiovascular disease (CVD) burden , 2. The As a metabolic disease, malnutrition is also common in DM. The recent evidence has proved that malnutrition is the independent risk factor for poor prognosis in patients with DM , 7. The Therefore, this study aimed to explore whether the presence of malnutrition can modify the association between inflammation and all-cause mortality.n = 20,896). Exclusive criteria included the following: a) Patients were diagnosed with cancer (n = 340); b) patients without data of CONUT score  ; c) patients without data of baseline hs-CRP ; d) patients without follow-up data ; e) patients with in-hospital death (n = 129). These data were missing randomly. Eventually, 6,682 patients were included  study is a single-center, retrospective, and observational study, enrolling 88,938 consecutive patients undergoing coronary angiography (CAG) or percutaneous coronary intervention (PCI) in Guangdong Provincial People's Hospital, Guangdong, China, hospitalized in between January 2007 to December 2018 . This study identified the individuals diagnosed with DM ].From January 2007 to December 2018, the data were extracted from the electronic clinical management records system of the Guangdong Provincial People's Hospital. We had access to all primary and secondary care records. The baseline information included demographic characteristics, coexisting conditions, laboratory examinations, and medications at discharge. The laboratory examinations were measured by an overnight fasting venous blood sample. The death of patients after discharge were recorded by the attending physician or a trained research assistant at the follow-up.vs. normal nutrition and high hs-CRP level vs. malnutrition and low hs-CRP level vs. malnutrition and high hs-CRP level). The estimated glomerular filtration rate (eGFR) was calculated by the Modification of Diet in Renal Disease (MDRD) formula .Continuous variables with the baseline characteristics conforming to the normal distribution are presented as mean ± SDs, and the others that are not conforming to the normal distribution are presented as quartiles [median ] by Kolmogorov–Smirnov test. In addition, the baseline characteristics are presented as proportions for categorical variables. Since the distribution of hs-CRP levels was skewed to the right, natural log-transformation for CRP (ln hs-CRP) was used to normalize the data for the statistical analysis. The differences of four groups in the baseline characteristics were compared through the use of Chi-square tests for categoric variables and a generalized paired There were 6,682 patients included in total in this study . Among the patients with both high hs-CRP level and malnutrition, the prevalence of anemia, chronic kidney disease (CKD) and the level of pro-BNP increased in a stepwise manner, while the level of eGFR and left ventricular ejection fraction (LVEF) decreased . The patp > 0.05) (p > 0.05). However, in the setting of high hs-CRP level, a significant risk of all-cause mortality was observed with malnutrition [HR (95% CI):1.66 (1.29–2.14)] (During a median follow-up of 5.0 years (interquartile range: 3.0 to 7.6 years), 759 (11.36%) patients died. The high hs-CRP level or malnutrition were associated with increased risk of all-cause mortality in patients with DM . In the  > 0.05) . Again, 9–2.14)] .p < 0.001). The p-value for interaction between nutritional status  and hs-CRP (low hs-CRP level vs. high hs-CRP level) on all-cause mortality was 0.03  was independently associated with an increased risk of death in 302 elderly patients with diabetes . AlejandThe most important finding in our study was the patients with diabetics with high hs-CRP level and malnutrition had a significantly increased risk of all-cause mortality. A significant interaction was observed between hs-CRP and nutritional status on all-cause mortality in patients with DM. On the one hand, elevated hs-CRP was strongly associated with increased risk of all-cause mortality, either by continuous or dichotomy variables in patients with malnutrition, while such association was not significant in those with normal nutrition. On the other hand, malnutrition is only associated with increased risk for poor prognosis in patients with diabetics with high hs-CRP level. Compared with those patients with normal nutrition and low hs-CRP level, concomitant condition of both high hs-CRP level and malnutrition was associated with a significantly higher risk of all-cause mortality while the presence of either high hs-CRP level or malnutrition was not significantly associated with an increased risk of all-cause mortality. It should be noted that the patients with high hs-CRP level and malnutrition were more likely to have a higher proportion of major comorbidities , which indicates that the comorbidities may together constitute risk factors for poor prognosis to a certain extent in the patients with high hs-CRP level and malnutrition. Jagadeswaran et al. demonstrated that malnutrition–inflammation complex syndrome is an important factor that determines mortality in patients with pre-dialysis CKD . Rambod Our findings strongly support the need for physicians to practice early assessment, clinical interventions by inflammatory and nutritional status in the patients with diabetics. Malnutrition is often associated with poor prognosis but overlooked in clinical practice. Thus, shifting diet therapy strategies from the treatment of obesity syndrome to prevent malnutrition may be necessary in patients with diabetes. Clinicians should stay abreast of the current scientific evidence to provide meaningful and effective nutritional guidance. Nutritional support knowledge in clinicians is essential to administer DM effectively and to optimize clinical response. Currently, a study reveals physical activity reduces total mortality in the patients with type 2 diabetics with elevated hs-CRP levels. This suggests that the anti-inflammatory effect of physical activity may counteract increased mortality associated with high inflammation . The issLimitations of this study should be considered. Because it was a single-center, retrospective study, our inferences did not reflect direct causality. Unfortunately, information on hematological affection, educational attainment, marital status, socioeconomic characteristics, height, and weight were not available that might help us understand the contributing causes of malnutrition. Due to the lack of other endpoint events, this will limit to generalize our results. In addition, we only included Chinese individuals who undergoing CAG that might be restrictions regarding generalizing to general population; We did not re-evaluate the time-dependent changes in nutritional and inflammatory status.The interplay of inflammation and malnutrition in patients with diabetics significantly amplifies the deleterious effects of each as distinct disease entities, and thus may merit closer surveillance and more aggressive management regarding the inflammatory and nutritional status in patients with diabetics. Although we established that hs-CRP might help classify risk in different nutritional status, due to the observational nature of our study, further large-scale clinical studies may be required to verify our results.The original contributions presented in the study are included in the article/The authors' responsibilities were as follows—JW, LC, and ZH: research idea and study design. JL, YY, JT, YP, KB, WC, YL, and JX: data acquisition. JL, YY, and XZ: data analysis/interpretation. LC, JL, and ZH: statistical analysis. LC and SC: supervision and mentorship. KC: writing guidance. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions on the accuracy or integrity of any portion of the work are appropriately investigated and resolved. All authors read and approved the final version.This research was funded and supported by Fujian Province Natural Science Foundation (Grant number: 2019J01617), Beijing Lisheng Cardiovascular Health Foundation (No. LHJJ20141751), Study on the function and mechanism of the potential target for early warning of cardiorenal syndrome after acute myocardial infarction based on transformism (DFJH201919), Natural Science Foundation of Guangdong Province General Project (2020A1515010940), and Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention (2017B030314041). The funders had no role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript; the work was not funded by any industry sponsors.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In the European Union, 6.1 million people are being treated in hospital annually for a sports injury. Of this, 31% of these injuries affect young people (15-24 years). Injury incidence, medical and lost productivity costs can be reduced by promoting a healthy and active lifestyle, where attention is paid to primary injury prevention. However, after two decades of initiatives, traditional injury prevention prevention programs seem effective in the short-term and controlled study settings, but have not decreased long-term injury incidence. One reason for this is the content of the exercises, currently being mainly ?closed and static? exercises. These exercises don't reflect real-world situations where unexpected and automatic movements are required involving complicated motor control adaptations. In addition, physical educators (PE) and trainers/coaches (TC) neither experience current exercises as being context specific and contributing to their training goals. To overcome this implementation gap, the purpose of the MoveHealthy project is to create exercise routines PE and TC can use, where children acquire the ability to sustain optimal automatic motor control while engaging in complex athletic, unpredictable environments , whilst minimising their risk to sustain an injury.Exercise routines  were co-created with the end-users (PE & TC) and their wishes and needs have been incorporated. Furthermore, to train the complex task-person-environmental interaction, real-world aspects such as visual-motor control where quickly processing environmental cues and anticipation and decision is crucial, was included into all conceptual considerations.Twelve prototype exercises have been developed. Merging theoretical foundations of motor learning and wishes and needs of end-users made it possible to create exercises that serve both needs.The development of these prototype exercises guides towards further validation and final development of innovative exercise routines where real-world aspects are incorporated. With this, we will better ensure real-world effects of injury reduction.This project is financially co-funded by the Erasmus+ Sport program from the European Union.


Food-induced anaphylaxis is an immediate adverse reaction, primarily triggered by the cross-linking of allergen-specific immunoglobulin (Ig) E bound to the high-affinity IgE receptor (FcεRI) on mast cells (MCs) after re-exposure to the same food allergen. Patients with an IgE-mediated food allergy often suffer from a variety of symptoms, e.g., gastrointestinal, skin, lungs, and, in the worst case, anaphylaxis. The site where food antigens are firstly taken up, either the skin or the gut, may cause sensitization against this food antigen . SensitiSensitization to food allergens can be measured with the Skin Prick test (SPT) and/or specific serum IgE (sIgE). Unfortunately, standardized commercial food allergen extracts for SPT are less readily available. Furthermore, due to globalization, the number of foods that causes an allergic reaction is increasing. In addition to the SPT, the Prick-to-Prick test (PTP) is also very commonly used to measure sensitization. The PTP test shows high sensitivity and specificity, but it is not very practical, as fresh fruits have to be available at the department. One alternative for commercial extracts and fresh fruits for PTP might be to prepare homemade (HM) extracts through standardized protocols. Recently, S. Terlouw et al. performed a clinical trial in 54 food-allergic patients comparing SPT results with commercial and home-made extracts . ExtractThe evaluation of patients with a possible food allergy starts with an extensive food-specific medical history. The standardized diet history tool published by S. Skypala et al. provides a practical approach to support food allergy diagnosis, ensuring that all relevant information is captured and interpreted in a robust manner . AlthougDoses and servings become more important since the early introduction of foods is advised for the prevention of food allergy. S. Filep et al. published doses of specific allergens in “early introduction foods” (EIF) for the prevention of food allergy for 17 major food allergens . CumulatSince 2011, when J.S. Kim et al.  publisheMany studies have investigated the tolerance-inducing effect of baked milk, but the form of the product  and the precise heating process were found to be highly variable. For the introduction of, e.g., milk and egg, so-called milk and egg ladders can be used, but even the latest literature from Venter et al. in 2022  does notTo further investigate whether food components or processed food components have effects on the adaptive immune system, intervention studies are widely suggested. The debate is still far from consensual, in particular on skewing the immune system towards a more homeostatic situation by means of inducing production of higher numbers of Tregs, which can decrease the number of T-helper 2 (TH2) cells and consequently decrease, e.g., IL-4 and IL-5 production. Lately, the supplementation of brown seaweed is presented in literature as having modulating properties on adaptive immune responses. The article by Kamunde et al. showed a highly significant increased total plasma antioxidant capacity in fish . These r


Psychiatry emergencies in India is major challenge for emergency service providers due to rapid growth of various behavioural, higher morbidity and mortality rate. Despite, psychiatry conditions are neglected area related to stigma, share, lack of awareness, and superstitious beliefs. There is an urgent need for specialist psychiatric emergency services, which can fill the huge gap between policymakers and health service providers joined together.Present feasibility study has been undertaken to evaluate the safety and efficacy of combined emergency and Ayurveda medicine management of psychiatric emergencies in community-based settings.Ayu-Emergency Care project was developed in partnership with policy makers, researchers and health care providers, a collaborative platform of emergency medicine and Ayurveda medicine  for developing whole-system perspective, where providers work in a coordinated and joined-up way. Twenty trained care providers in psychiatry emergency and Ayurveda management worked in partnership with community-based organisation.Patients with major clinical difficulties, in the acute phase were treated and managed by Ayu-Emergencypractitioners. Severe Agitation and violence relating to substance abuse, anxiety disorder and psychosis were the most common admission diagnoses. 2-weeks results indicate that Ayurveda intervention can reduce anxiety(p<0.01), aggression (p< 0.001) and agitation (p<0.01) significantly with no side effects reported. Intervention found to be clinically beneficial and cost-efficient alternative to out-of-home placements .The study’s findings highlight safety, efficacy and feasibility of intervention. Patients both prefer and seem to benefit from community-based ayu-psychiatric care, and early-intervention community program could be a good model for such care.No significant relationships.


The number of patients aged 80 years or older with diffuse large B-cell lymphoma (DLBCL) is increasing, and the incidence rate of the disease in this population group reaches up to 20%. The risk of infection is higher in older patients than in other patients. Although hypnotic drugs are frequently detected as potentially inappropriate medications, it is unclear whether hypnotic drugs affect the occurrence of infection during chemotherapy. Here, we investigated whether the use of hypnotic drugs is associated with infection during first-line chemotherapy in patients with diffuse large B-cell lymphoma (DLBCL) aged 80 years or older.Japanese patients aged 80 years or older with diffuse large B-cell lymphoma who had received first-line chemotherapy at Fujita Health University Hospital from January 2005 to March 2020 were enrolled in this retrospective cohort study. The primary study outcome was the identification of the risk factor for infection during first-line chemotherapy.P = 0.022).This study included 65 patients received first-line chemotherapy. The proportion of patients with National Comprehensive Cancer Network-international prognostic index ≥ 6 was higher in the infection group than in the non-infection group. The relative dose intensity of each anticancer drug  and dose of prednisolone did not significantly differ between the two groups. Multivariate analysis showed that the use of benzodiazepines was a risk factor for infection (odds ratio, 4.131 [95% confidence interval: 1.225–13.94], DLBCL patients using benzodiazepines should be monitored for infection symptoms during chemotherapy. Diffuse large B-cell lymphoma (DLBCL) is the most frequent type in non-Hodgkin lymphoma (NHL) , 2. In JAn infectious episode is a well-known adverse drug reaction and has been reported to be associated with increased mortality , 9. ComoInappropriate medication use and drug interactions are resulting from poor functional impairment in older patients. Several screening tools are available to evaluate the appropriateness of pharmacotherapy for older patients and to avoid inappropriate medication use and drug interactions. Although these tools have shown that hypnotic drugs increase the risk for falls or fractures , 14, it Japanese patients aged 80 years or older with DLBCL who had received first-line chemotherapy at Fujita Health University Hospital from January 2005 to March 2020 were enrolled in this retrospective cohort study. All medical data were collected from the hospital’s medical records. The exclusion criteria were as follows: (a) patients whose baseline data were unavailable and (b) patients without completed first-line chemotherapy. The standard chemotherapy regimens used were R-CHOP , CHOP , COP , R-COP , CHP , R-CHP , and R-HOP . The RDI was calculated by dividing the DI of each chemotherapeutic drug  by the respective target DI and multiplying by 100 . DisordeThe identification of the risk factors for infection during first-line chemotherapy was the primary outcome of this study. Infection severity was defined in terms of grade 2 or higher based on the Common Terminology Criteria for Adverse Events version 5.0, as follows:Grade 2: Oral intervention indicated Grade 3: IV antibiotic, antifungal, or antiviral intervention indicated; invasive intervention indicatedGrade 4: Life-threatening consequences; urgent intervention indicatedGrade 5: DeathPatients received administration of prophylactic antibiotics were not defined as CTCAE grade 2.The NCCN-IPI was determined as described in a previous report . BrieflyP-value of <0.05 was considered significant in all statistical analyses, which were performed using SPSS version 22.0 .All data are shown as medians and ranges. The Mann–Whitney U test was used for comparisons of data without normal distribution. The chi-square test and Fisher’s exact test were used to analyze nominal scales. Logistic regression analysis using a multivariable model was performed to identify the risk factors for the infection events. To determine the risk factors for infection, logistic regression analysis with a multivariable model was applied for the following covariates: NCCN-IPI and benzodiazepine use. To avoid multicollinearity with the NCCN-IPI, PS was not included in the multivariate analysis. The fitness of the logistic regression model was evaluated using the Hosmer-Lemeshow test. The predictive ability was evaluated by plotting the receiver operating characteristic curve. A two-sided This study was approved by the ethics board of Fujita Health University Hospital  and conducted according to the appropriate guidelines. Because this was a retrospective cohort study, an opt-out approach for informed consent was used according to the approval of the ethics board.P = 0.049). The proportion of patients with NCCN-IPI of ≥6 was higher in the infection group than in the non-infection group (P = 0.053). The RDI of each drug  and PSL dose were not significantly different between the two groups. Furthermore, the number of concomitant medications was not significantly different between the groups . Meanwhile, extranodal disease, bone marrow infiltration, central nervous system infiltration, advanced cancer, CCI, polypharmacy, and the use of antiulcer drug were not associated with infection events. NCCN-IPI affects clinical outcomes more than PS, and these are confounding factors. Therefore, we performed multivariate analysis using NCCN-IPI and use of benzodiazepines. The use of benzodiazepines was identified as a risk factor for infection in multivariate analysis . To determine whether the use of benzodiazepines is a predictor of the occurrence of infection, we evaluated the predictive ability by using receiver operating characteristic curve analysis. The use of benzodiazepines showed a trend toward good prediction of the occurrence of infection   .Types of infections and pathogens are shown in This study was aimed at investigating whether the use of hypnotic drugs is associated with infection during first-line chemotherapy in patients with DLBCL aged 80 years or older. Our results suggested that compared to a high NCCN-IPI, the use of benzodiazepines was potentially associated with infection events during chemotherapy. Although dose intensity is related to mortality in patients with DLBCL , it was Older patients are at a higher risk of infection-related mortality than are younger patients –20. To dThe occurrence rate of treatment-related adverse drug events in chemotherapy is higher than that associated with other treatments. In patients with metastatic cancer, high PS and polypharmacy are risk factors for hospitalization or an emergency room visit . TherefoTwo major screening tools recommended that older adults should avoid to use benzodiazepines , 27. TheOur study has several limitations. First, this was a single-center and retrospective study. The number of subjects and clinical parameters evaluated in our study were limited. To verify our results, the multicenter research and a large spontaneous reporting system  might be useful in the further study. Second, the patients received different chemotherapy regimens . Although the proportions of patients who received each of the chemotherapy regimens were not significantly different between the infection and non-infection groups, further studies should enroll patients who received the same chemotherapy regimen. Third, we could not come to a conclusion about the mechanism of benzodiazepine-induced infection. Oral secretions, reflux events, and skeletal muscle mass should be evaluated in further studies.In conclusion, patients with DLBCL using benzodiazepines should be monitored for infection symptoms during chemotherapy.S1 TableaFisher’s exact test, bchi-square test.CCI: Charlson Comorbidity Index, LDH: lactate dehydrogenase, NCCN-IPI: National Comprehensive Cancer Network-International Prognostic Index, R: rituximab, C: cyclophosphamide, H: adriamycin, O: vincristine, P: prednisolone, RDI: relative dose intensity, (DOCX)Click here for additional data file.S1 Raw data(XLSX)Click here for additional data file.


Intestinal mycobiome dysbiosis plays an important role in the advancement of HIV- and HCV-infected patients. Co-infection with HCV is an important risk factor for exacerbating immune activation in HIV-infected patients, and gut fungal microbial dysbiosis plays an important role. However, no systematic study has been conducted on the intestinal fungal microbiome of HIV/HCV co-infected patients to date. Patients infected with HIV and HCV, either alone or in combination, and healthy volunteers were included. Stool samples were collected for fungal ITS sequencing and for further mycobiome statistical analysis. We found that the abundance of fungal species significantly decreased in the HIV/HCV co-infection group compared to in the healthy control group, while no significant differences were found in the mono-infection groups. Low-CD4 + T-cell patients in the HIV group and high-ALT-level patients in the HCV group were discovered to have a more chaotic fungal community. Furthermore, the opportunistic pathogenic fungal profiles and fungal inter-correlations in the co-infection group became less characteristic but more complicated than those in the mono-infection groups. Intestinal fungal dysregulation occurs in HIV- and HCV-infected patients, and this dysregulation is further complicated in HIV/HCV co-infected patients. Human immunodeficiency virus type 1 (HIV-1) has become a worldwide threat to human health. In addition to the continuous replication of HIV itself, intestinal microecological changes and co-infection with other chronic viral infections, such as HCV, are considered to be the key reasons for the persistence of immune activation in HIV-infected patients. Since 2012, several researchers have successively proposed that fungal translocation is associated with immune activation and systemic inflammation in HIV-infected patients undergoing ART treatment ,4,5,6. FHowever, there are only a few studies regarding intestinal fungi in HIV-infected patients that have been conducted thus far. Some researchers have found that compared to healthy controls, fungi show a higher prevalence in HIV-diarrhea patients, of which C. parvum, C. difficile, and C. albicans were the most representative species ,17,18,19As there have been limited studies on intestinal fungal dysbiosis in HIV mono-infected patients, HCV mono-infected patients, and HIV/HCV co-infected patients, in this study, we characterized the intestinal mycobiome communities in patients from these groups, aiming to identify specific mycobiome lineages that may play important roles in the development of disease and trying to identify important intestinal fungi in HIV/HCV co-infected patients to better understand the disease status of co-infected patients for treatment.In this study, 87 patients infected with HIV and/or HCV and 22 healthy controls matched by age and BMI were recruited. Both the HIV and HIV/HCV co-infected patients had been infected for an average of 22 years and had received cART for an average of 13 years. All of the HIV patients had been infected through blood transmission. The diagnosis of HIV-infected individuals was verified using PCR and HIV-1 antibody tests, and the diagnosis of HCV infection was based on serologic markers, virological markers, liver function tests, and biochemical assays. HIV mono-infected patients and HIV/HCV co-infected individuals had all been treated with two nucleoside reverse transcriptase inhibitors (NRTIs) and nonnucleoside reverse transcriptase inhibitors (NNRTIs) for more than 10 years, while no antiviral treatment had been taken in the HCV mono-infection group.The exclusion criteria were age >72 years or <40 years, the use of antibiotics or immunosuppressive drugs or antifungal drugs within the past 1 month, a history of gastrointestinal disease or gastrointestinal surgery, and evidence of hepatitis A or B virus infection or other chronic diseases. Clinical and demographic data were obtained by performing standardized subject interviews and medical record reviews.Fecal samples were collected from the subjects, with each individual sampled once. Each sample was immediately placed in a sterile plastic container and stored at −80 °C before being processed. DNA was extracted using the QIA amp DNA Stool Mini Kit  according to the instructions. DNA quantification and purity were assessed using a NanoDrop ND-1000 spectrophotometer .The primers 5′-CTTGGTCATTTAGAGGAAGTAA-3′ and 5′-GCTGCGTTCTTCATCGATGC-3′ were used for ITS rDNA gene amplification. Oligonucleotide primers were synthesized by the Shanghai Sangon Biotechnology Limited Company . The PCR products were extracted from a 2% agarose gel and purified using the AxyPrep DNA Gel Extraction Kit  according to the manufacturer’s instructions and were quantified using a Quantus™ Fluorometer .https://github.com/OpenGene/fastp, version 0.20.0), and merged using FLASH [http://www.cbcb.umd.edu/software/flash, version 1.2.7). Operational taxonomic units (OTUs) with a 97% similarity cutoff were clustered using UPARSE [http://drive5.com/uparse/, version 7.1), and chimera identification sequences were identified and removed. The RDP classifier [http://rdp.cme.msu.edu/, version 2.2) was used in combination with the Unite [http://unite.ut.ee/index.php) database for taxonomic analysis at a 70% cutoff confidence level for each taxonomic level: domain, kingdom, phylum, class, order, family, genus, and species, to calculate the community species composition of each sample.Purified amplicons were pooled in equimolar amounts and were paired-end sequenced on an Illumina MiSeq PE300 platform  according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. . The raw ITS gene sequencing reads were demultiplexed, quality-filtered by fastp  version ng FLASH  version g UPARSE  version assifier  known ashe Unite   and GraphPad Prism 7.00 . Alpha diversity and beta diversity were assessed using the R statistical software package . The effect size of the linear discriminatory analysis (LDA) was an LDA score > 2.5 and a p value < 0.05. Spearman correlations between fungi were calculated using Origin software (version 2021b) with a cutoff of 0.5 and p value < 0.05 and were plotted as a heatmap. A Venn diagram was used to display the numbers of common and unique OTUs and the shared OTUs among different samples.Categorical variables were analyzed using the chi-square test. Comparisons between three or more groups were analyzed by one-way ANOVA or the Kruskal–Wallis test. Comparisons between two groups were performed with an unpaired p = 0.0424), the CD4 + T-cell counts showed no differences between the two groups. Liver enzyme indicators in serum were significantly higher in the HCV mono-infected patients than in the healthy controls, and co-infection with HIV may further exacerbate abnormal liver function, as reflected by a further increase in the glutamate transaminase, glutamic oxaloacetic transaminase, and gamma-glutamyl transpeptidase levels in HIV/HCV co-infected patients compared to in HCV mono-infected patients.In total, 18 HIV mono-infected patients, 40 HCV mono-infected patients, 29 HIV/HCV co-infected patients, and 22 healthy individuals were enrolled. Their basic information can be found in The basic information obtained from the sequencing reads and operational taxonomic units (OTUs) for each sample can be found in p = 0.01531, p = 0.04281), indicating that the abundance of the intestinal fungal community in HIV/HCV co-infected patients was reduced  (p < 0.05) was the class Leotiomycetes  A. The co= 0.001) B. A deta= 0.001)  showed t= 0.001) C,D. Of n bold in B, which  bold in B, rankin bold in B, signif bold in C.composition at the genus level in depth, what is worth noticing is a sharp increase in Candida in the low-CD4 + T-cell group, indicating a high risk of opportunistic infection. Through further analysis, there were several genera worth noting .Finally, we conducted correlation analysis using R statistical computing software to determine the correlation between different members of the mycobiome, and the correlation coefficients of each fungal pair are listed in p < 0.0001). A negative correlation was also observed between Aspergillus and Candida .Seven significantly correlated fungal pairs were detected in the HCV mono-infected group B, and thp < 0.0001).There were 25 significantly correlated fungal pairs in the HIV mono-infected group D, which p = 0.0003). Apart from the combined features of the mono-infection groups, there were also some novel pairs that were absent in the mono-infection groups, such as the negative correlation between Alternania and Candida, Rhodotorula and Olpidium, and Acremonium and Fusariella, and some positive reaction groups, such as the positive correlation square in the right lower corner of For the HIV/HCV co-infected group, we found 11 significantly correlated fungal pairs, which was more than the number of correlated fungal pairs in the HCV group but less than in the HIV group C. The moIn addition, we could see that there were many more negative correlations in the infection groups than in the healthy controls. The most typical pair was the strong negative correlation between Candida and Aspergillus in all three infection groups, which was absent in the HC group.Candida, Saccharomyces, and Malassezia and filamentous fungi such as Aspergillus, Cladosporium, and Penicillium. Collectively, these fungal constituents represent core mycobiota species [Neocosmospora rubeola, were clearly enriched in low-CD4 + T-cell patients with HIV infection compared to in patients with normal CD4 + T cell levels. Members of the genus Neocosmospora have been reported to cause lung infections in liver transplant patients [Stachybotryacea, which has not been previously reported in humans, was significantly enriched in the HCV mono-infection group. The mechanism leading to this remains unclear. Further analysis of the correlation between ALT and the fungal profile revealed that the fungal profile of the high-ALT group was more diversified, and some of those enriched fungi (such as Mortierella and Sagenomella) have been reported to cause fungal infections in humans [In the healthy human gastrointestinal tract, the most common fungal taxa are yeasts such as  species . The funpatients  and to epatients , indicatpatients . Howevern humans ,48. We aLasiosphaeriaceae and Metschnikowiaceae, the human-infecting fungus Kodamaea ohmeri, and the opportunistic fungus Curvularia hominis. Notably, the co-infection group lost many unique fungal profiles of the HIV group, such as the class Leotiomycetes and genus Preussia, both of which were enriched in the HIV group when compared to the HC group, yet diminished in the co-infection group for some reason, which indicates that as the co-infection of the two viruses takes place, the fungal profile becomes less characterized and more complicated. What comes along with the presence of some novel fungal genera is many unique fungal inter-correlations, which may be positive or negative.Compared to the HIV/HCV mono-infection groups, some of the fungi enriched in the co-infection group were not discovered through mono-infection vs. HC comparison, such as the plant-originated fungi Penicillium citrinum and Aspergillus minisclerotigenes was present in all of the groups except the co-infection group, and the correlation coefficients showed no significant difference, indicating that alternation of the gut environment does not have any impact on the correlation of the two fungi. However, the positive correlation between Aspergillus penicillioides and Aspergillus cibarius became stronger in the co-infection group than in the HCV mono-infection group. We can infer that this might be the result of the decrease in immunity caused by HIV infection. Additionally, taking an overall glance at the inter-correlation heatmap, we found that there were more negative correlations in the mono/co-infection groups than in the healthy group, which may indicate that infection causes fierce competition in the fungal microbiome in the patient’s gut, speeding up the process of natural selection. The most representative pair was Candida and Aspergillus, whose correlation coefficient reached −0.9360 in the infection groups but showed no significant relationship in the HC group. Taking into consideration the fact that these two fungi are the top two predominant fungi in all four groups but that their relative abundance is different in the HC group and the infected group, we can infer that mutual inhibition results in the alternation of the predominant fungus, participating in the advancement of the disease.Fungal inter-correlation analysis also presents some interesting outcomes. There are some similarities between the four groups. For example, the positive correlation between Candida has shown extremely high clinical application value. However, this method is not necessarily a one-time-for-all approach. Antifungal treatment administered by artificially adding fungi to the mycobiome (such as through probiotic supplements) can avoid the tolerance caused by the abuse of antifungal drugs, but it also destroys the original intestinal microenvironment. The long-term use of this method in intervening in vivo fungal infection is worthy of further exploration.Exploring the correlation between intestinal fungi has good clinical application prospects. For example, the antagonism between the harmless fungus and the opportunistic pathogenic This study preliminarily explored the characteristics of the intestinal fungal community in HIV- and HCV-alone and co-infected conditions. The fungal abundance indexes of the infection groups were lower than those of the healthy controls. The fungal microbiomes of the HIV and HCV mono-infection groups were significantly different from those of the HC group, and some opportunistic fungi were clearly enriched in low-CD4 + T-cell patients and in high-ALT-level patients. The fungal profile of the HIV/HCV co-infection group was less characteristic but more complicated than that of the mono-infection groups. As for the inter-correlation of fungi, apart from some similarities, there were more negative correlations in the mono/co-infection groups than in the healthy group. Infection also led to some significant changes in the inter-correlation of certain fungal pairs.The HIV patients in our research cohort were all infected through blood transmission. They had no history of drug abuse or other unhealthy lifestyles and were similar to healthy people in terms of most of their living habits. From this perspective, this population provides a consistent background for studying the effects of HIV and/or HCV on differences in gut fungal profiles. At present, there are few studies on the characteristics of the intestinal fungal community in HIV-infected patients, and the changes in the intestinal fungal composition of patients with disease progression are still unclear. Therefore, although our research presented a large number of results, as a cross-sectional study, our study still lacks the support of previous research and follow-up data.


Aim: To provide a comparison of genotyping for HLA risk alleles versus patch testing to determine which of these two tests is a better diagnostic tool for cutaneous hypersensitivity reactions caused by anti-seizure medication.Methods: A literature study was performed in PubMed to assess the sensitivity and specificity of HLA genotyping and patch tests for identifying anti-seizure medication induced cutaneous hypersensitivity reactions.Results: This study shows that HLA-B*15:02 genotyping shows high sensitivity for carbamazepine-induced SJS/TEN, especially in Han Chinese and Southeast Asian patients (66.7–100.0%) whereas the sensitivity of patch tests , HLA-A*31:01 (0–50%) and HLA-B*15:11 (18.2–42.9%) are lower. On the contrary, for carbamazepine and phenytoin induced DRESS, patch tests (respectively 70.0–88.9% and 14.3–70.0%) show higher sensitivity than HLA tests (0–66.7% and 0–12.7%). Also for lamotrigine-induced DRESS patch tests perform better than HLA-B*15:02 (33.3–40.0 versus 0%). For anti-seizure medication induced MPE and for oxcarbazepine-induced SCARs more studies are needed.Conclusion: Use of HLA-B genotyping may aid clinicians in the diagnosis of carbamazepine, phenytoin, lamotrigine and oxcarbazepine induced SJS/TEN, particularly in Han Chinese and Southeast Asian patients. On the other hand, patch tests seem to perform better in the diagnosis of carbamazepine and phenytoin induced DRESS. Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN) and drug reactions with eosinophilia and systemic symptoms (DRESS) are cutaneous adverse drug reactions (cADRs) characterized by epidermal necrosis and mucous membrane damage . These sCurrently, patch tests are used to identify culprit drugs after rash development. These tests can reproduce drug-induced delayed hypersensitivity reactions and have a low risk of systemic reactions due to moderate drug re-exposure of patients . HoweverAlthough early diagnosis of SCARs is still not optimal, advances in the pharmacogenomics of SCARs have led to the discovery of human leukocyte antigen (HLA) associations with drug-induced SCARs . TestingWhereas in most Asian populations ASM-induced SCARs are associated with HLA-B*15:02, in Caucasian and Korean populations, ASM-induced SCARs, particularly DRESS, are associated with HLA-A*31:01 . In JapaHLA testing in certain Southeast Asian populations is recommended according to the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines . BesidesPrevious studies have only focused on assessing the diagnostic test criteria of either patch tests or HLA tests. Besides, studies on HLA genotyping and drug hypersensitivity reactions focused mainly on the use of HLA genotyping in a preventive setting instead of using it as a tool for diagnosis. HLA testing is not implemented in the clinics in a diagnostic setting, after a cADR has already occurred, possibly due to a lack of knowledge on their diagnostic criteria compared to patch tests . Thus, cA literature search using the PubMed database was conducted on 30 April 2021. The search was performed using the following MeSH and/or search terms: HLA, drug hypersensitivity, anticonvulsant, carbamazepine, oxcarbazepine, lamotrigine, phenytoin, patch test and synonyms. The title and abstract of all records were screened. Furthermore, reference lists from the included papers were manually checked to identify additional relevant studies.Case-control and cohort studies were included with controls and cases using ASMs with the outcomes SJS/TEN, DRESS and MPE. Furthermore, only studies concerning patch tests or HLA-A*31:01, HLA-B*15:02 or HLA-B*15:11 genotyping in relation to carbamazepine, lamotrigine, oxcarbazepine or phenytoin-induced cADRs were included. Duplicates, case reports, reviews and non-English articles were excluded.The papers were then categorized and presented in tables per drug and cADR. In addition, the numbers of true positive, true negative, false positive and false negative participants were used to calculate the sensitivities and specificities of each test. Sensitivity is the percentage of subjects with an cADR who test positive for the HLA risk allele while specificity is the percentage of subjects without an cADR who test negative for the HLA risk allele. The sensitivity and specificity were calculated as explained in the article by Tonk et al. . In the The literature search yielded 324 records which were screened on title and abstract, followed by assessing the full-text articles for eligibility. This resulted in the inclusion of 55 relevant case-control and cohort studies in total. These studies contained data to calculate the sensitivity and specificity of patch tests, HLA-B*15:02 tests, HLA-B*15:11 tests and/or HLA-A*31:01 tests. The study selection procedure is visualized in The sensitivity and specificity of patch tests and HLA-B*15:02, HLA-B*15:11 and HLA-A*31:01 for confirming carbamazepine induced SJS/TEN are displayed in For the diagnosis of carbamazepine-induced DRESS, HLA-B*15:02 has a sensitivity of 0.0% in most populations except for South-East Asian populations (20.0–66.7%) . Also HLFor carbamazepine-induced MPE, results can be seen in For oxcarbazepine-induced hypersensitivity, studies on patch tests are scarce. Results are shown in As can be seen in For phenytoin-induced DRESS , the HLAFor phenytoin-induced MPE, the studies were limited . Only onFor lamotrigine, only patch tests on lamotrigine-induced DRESS  and MPE vice versa. In our study, the predictor is binary (present or not) leading to fixed values of sensitivity and specificity. Specificity is higher than 90% for both patch- and HLA-testing, although specificity seems to be somewhat higher for patch testing. This might be of importance, when avoidance of false positives is of importance.The aim of this literature study was to determine whether patch tests or HLA risk allele genotyping perform better in diagnosing ASM-induced cutaneous hypersensitivity reactions. Such testing occurs in a retrospective setting: After the hypersensitivity occurred, drug-associations needs to be established to prevent future exposure. As such, the nature of testing is not predictive. Rather, the question is whether the presence of a known risk factor can be established. This naturally leads to assessing the performance of the HLA and patch tests by using the sensitivity and specificity, as sensitivity measures the percentage of patients for which the risk factor can be detected and specificity how often that factor can be excluded. Both measures can be evaluated on case-control data. Sensitivity and specificity cannot be evaluated independently, as for continuous thresholds, sensitivity increases when specificity decreases and In this study, we have examined the cutaneous hypersensitivity reactions SJS/TEN, DRESS and MPE with regard to the drugs carbamazepine, lamotrigine, oxcarbazepine and phenytoin as described in the CPIC and DWPG guidelines. A main finding of this study is that HLA-B*15:02 tests are more sensitive than HLA-B*15:11, HLA-A*31:01 and patch tests for detecting carbamazepine-induced SJS/TEN in Han Chinese and Southeast Asian populations. However, patch testing is most sensitive and specific for the diagnosis of carbamazepine, phenytoin and lamotrigine induced DRESS. For diagnosis of ASM-induced MPE, sensitivities of both HLA and patch tests are low.To our knowledge, this is the first study that determined whether HLA genotyping or patch testing is better in diagnosing ASM-induced SCARs. Although previous studies have already examined the diagnostic utility of patch tests or HLA risk allele tests for identifying SCARS, most of these studies evaluated the diagnostic test criteria of HLA genotyping for pre-emptive use to predict ASM-induced SCARs, whereas this study focuses on the use of HLA genotyping as a diagnostic tool for identifying ASM-related SCARs. Because the number of studies on the diagnostic utility of patch tests and HLA tests for identifying SCARs are limited, more insight into the diagnostic test criteria is needed before HLA testing could be implemented for patients presenting with cutaneous hypersensitivity reactions. Our study provides a clear overview of the available data on the sensitivity and specificity of patch tests and HLA tests in order make a comparison of these tests for identifying SCARs. This may contribute to improvement of the diagnosis of SCARs.The endpoints used in this study were based on the endpoints mentioned in the DPWG and CPIC guidelines concerning the ASMs . We chosA limitation of this study is that publication bias may be present as most papers at least show one statistically significant association. This fact could potentially lead to an overestimation of sensitivity and specificity for the diagnostic tests which may not fully translate to patients in real-world practice, although the strong associations reported here are not likely to exhibit strong bias. For example with oxcarbazepine-induced SJS/TEN, only one study found a strong association with HLA-B*15:02 and high sensitivities and specificities for identifying SJS/TEN . MoreoveDue to the limited articles available on patch tests and HLA tests, all available studies were taken into account, regardless the sample size, differences in patient selection and definition and diagnostic confirmation of cADRs. Most of the studies did use ASM-tolerant subjects as controls. But there were differences in patient selection and definition of the cADRs, making a direct comparison between patch tests and HLA tests difficult and not very precise. However, because of the limited data available, using all available studies was the best that could be done.Patch test studies for identifying ASM-induced hypersensitivity reactions, and studies of HLA genotyping to identify lamotrigine and oxcarbazepine-related SCARs are limited. Even though the evidence is limited, the use of patch tests and HLA genotyping seems promising for identifying ASM-induced SCARs, specifically DRESS. However, more and larger studies on the diagnostic test criteria are needed to determine the sensitivity and specificity more accurately, and confirm the effectiveness of both patch testing and HLA genotyping. Overall, our study provides an overview of the diagnostic criteria of patch tests and HLA risk allele tests for identifying ASM-induced SCARs and it contributes to a better understanding of the utility of these tests as a diagnostic tool. However, we believe that the limitations do not affect the outcome of this study and the results contribute to a better understanding of the diagnostic test criteria of HLA genotyping and patch testing.To conclude, this study compared the diagnostic test criteria of patch tests and HLA genotyping to assess whether patch testing or HLA genotyping performs better in diagnosing ASM-induced cutaneous hypersensitivity reactions. The sensitivity and specificity of HLA-B*15:02 are high for SJS/TEN caused by carbamazepine in Southeast Asian and Han Chinese patients. HLA-B*15:02 genotyping for identifying SJS/TEN shows a higher sensitivity and specificity, especially in Southeast Asians and Han Chinese than patch testing, and is also safer than patch tests. Thus, implementation of HLA-B*15:02 genotyping as a diagnostic tool to identify SJS/TEN should be considered. For the diagnosis of carbamazepine, lamotrigine and phenytoin induced DRESS, patch testing shows a higher sensitivity than HLA genotyping and a specificity of nearly 100%, suggesting that patch tests could be used to rule out if the hypersensitivity reaction is ASM-related. Implementing HLA-B*15:02 testing may aid clinicians in the diagnosis of ASM-induced SJS/TEN. This may result in correctly, safer and faster identification of SCARs.


Sapindus mukorossi Gaertn (SM) seed oil has proven anti-bacterial and anti-inflammation properties. However, the possibility of using this plant to prevent or treat periodontitis has not been reported previously. The aim of this study was to evaluate the effects of SM oil on experimental periodontitis in rats by using micro-CT and microbiota analysis. The distance between cementoenamel junction (CEJ) and alveolar bone crest (ABC) on the sagittal micro-CT slide showed that total bone loss (TBL) was significantly lower in CEJ-ABC distances between SM oil and SM oil-free groups on Day 14. Histology data also showed less alveolar bone resorption, a result consistent result with micro-CT imaging. The microbiota analyzed at phylum and class levels were compared between the SM oil and SM oil-free groups on Day 7 and Day 14. At the phylum level, Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria were the dominant bacterium. Firmicutes in box plot analysis was significantly less in the SM oil group than in the SM oil-free group on Day 7. At the class level, Bacteroidia, Gammaproteobacteria, Bacilli, Clostridia, and Erysipelotrichia were the dominant bacteria. The bacteria composition proportion of Bacilli, Clostridiay, and Erysipelotrichia could be seen in the SM oil group significantly less than in t SM oil-free group on Day 7. Overall, the present results show that topical application of SM oil can reduce bone resorption and change bacteria composition in the ligature-induced periodontitis model. According to these results, it is reasonable to suggest SM oil as a potential material for preventing oral disease.Periodontitis is a common oral disease mainly caused by bacterial infection and inflammation of the gingiva. In the prevention or treatment of periodontitis, anti-bacterial agents are used to inhibit pathogen growth, despite increasing levels of bacterial resistance.  Periodontitis is a common chronic oral inflammatory disease that leads to the destruction of tooth-supporting tissues, which may be caused by genetic variations , imbalanSapindus mukorossi (SM) Gaertn is one of the more commonly used major species. SM can be easily found in tropical and sub-tropical regions of the Pacific Rim . All polymerase chain reactions (PCR) were performed in 25 μL reagents with 0.5 μL KAPAGermany) ,68,69,70ing data . Sequencp < 0.05 were considered significant. Statistical analyses were performed using commercial software . The groups’ differences in bacteria composition were evaluated using box plot analysis with commercial software .Differences in bone loss between groups were evaluated using t-test analysis. Differences at This study demonstrates that SM oil can significantly decrease total bone loss (TBL) and bone resorption in a ligatured animal model. Our results also show that SM oil inhibits pathogenic bacteria associated with oral disease and suppress the F/B ratio. Therefore, SM oil can potentially protect periodontal tissue by altering the microbiota composition in the initial phase of periodontitis. The data shown in this study can be an important reference for future studies that examine larger sample sizes and in clinical studies in humans.