We demonstrate a significant improvement in performance on all the datasets and experiments in comparison to other relevant analytical techniques.•Pairwise association analysis reveals several significant functional connectivity patterns, in line with previous PTSD neuroimaging literature. Early intervention following exposure to a traumatic life event could change the clinical path from the development of post traumatic stress disorder (PTSD) to recovery, hence the interest in early detection and underlying biological mechanisms involved in the development of post traumatic sequelae. We introduce a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD symptoms at one-, six- and fourteen-months after the exposure. FMRI data, as well as PTSD status and symptoms, were collected from adults at risk for PTSD development, after admission to emergency room following a traumatic event. Our computational method utilized a per-region encoder to extract brain regions embedding, which were subsequently updated by applying the algorithmic technique of pairwise attention. The affinities obtained between each pair of regions were combined to create a pairwise co-activation map used to perform multi-label classification. The results demonstrate that the novel method’s performance in predicting PTSD symptoms, in a prospective manner, outperforms previous analytical techniques reported in the fMRI literature, all trained on the same dataset. We further show a high predictive ability for predicting PTSD symptom clusters and PTSD persistence. To the best of our knowledge, this is the first deep learning method applied on fMRI data with respect to prospective clinical outcomes, to predict PTSD status, severity and symptom clusters. Future work could further delineate the mechanisms that underlie such a prediction, and potentially improve single patient characterization. While mnic PTSD . The devnic PTSD . Longitunic PTSD .Converging neuroimaging studies have suggested abnormalities in brain regions involved in emotional processing in individuals with PTSD . These iNevertheless, these findings come mainly from cross-sectional studies comparing a group of PTSD patients to a group of trauma-exposed individuals who did not develop PTSD. Moreover, the dynamics in clinical symptoms observed through the first year after trauma points to associated changes over time in underlying neural processing. To dateIn addition, to date, work on PTSD classification has largely overlooked the dynamics of evolving PTSD symptom trajectories and considered single time-point outcomes. Some longitudinal studies  yielded Another challenge in examining the trajectory of PTSD following trauma is the heterogeneity of clinical symptoms. According to DSM-5, PTSD consists of four symptom clusters: Intrusion (Criteria B), Avoidance (Criteria C), Negative alterations in mood and cognition (Criteria D), and Hyperaousal (Criteria E); raising the possibility that different symptom clusters may reflect different mechanisms/processes involved. It is therefore of the utmost importance to further consider, not only PTSD diagnosis but also the specific symptom cluster or constellation. However, the majority of studies finding qualitatively distinct profiles among diagnosed individuals have not been utilized to predict the four symptom clusters of PTSD. In this work, we fill this gap by training our deep learning model to identify distinct, symptom-based clusters of PTSD according to DSM-5 criteria.Machine learning (ML) approaches are increasingly utilized to overcome the problem of characterization, prediction, and treatment selection for individuals suffering from a variety of psychiatric disorders . For exaWith the advent of deep learning (DL) methods, a variety of neural network approaches have been applied for fMRI classification, including autoencoders , RecurreIn this work, we used clinical data and neural indices, collected shortly after trauma to predict the course of PTSD severity, including distinct clusters of PTSD-related symptom/variables, during the first year after trauma exposure . We addiOur novel analytical methodology utilized connectivity (correlation) maps extracted from pairs of brain regions, in a whole-brain analysis. These connectivities were obtained using a novel end-to-end neural network by first computing embedding for every brain region and then further applying an attention mechanism , which aTo benchmark our predictive model, we compared the model with previously used ML and DL approaches by training all the techniques from scratch on the same dataset, showing a significant improvement on all the tasks, both in classification accuracy, AUC (Area Under the Curve) and average precision.To our knowledge, this is the first report of deep learning method used to predict PTSD diagnosis and symptom clusters (based on DSM-5), in a prospective design, using neural data obtained one-month following trauma. While we considered rs-fMRI to be the most common modality used in this type of analysis , we repr22.1n=160 individuals with valid fMRI and clinical data, obtained one-month after trauma (T1)  who were admitted to a general hospitals emergency department after a traumatic event underwent clinical assessments and fMRI scans, at one-, six- and fourteen-months following trauma . Eleven participants were excluded from the analysis at T1, due to partial or low-quality data, resulting in a final dataset of n=160 enrolled at T1, n=135 and n=130 completed clinical follow-up assessments at T2 and T3 (see pOut of the d T3 see . Twenty-2.2(i) Resting-state fMRI (rs-fMRI) scan. A 10 min rs-fMRI scan in which participants were instructed to keep their eyes open, focusing on a fixation cross. (ii) Emotional Reactivity Task ((iii) Safe or Risky Domino Choice task (SRDC)  . It invo2.3For all participants, whole-brain functional and anatomical images were acquired using a 3.0 Tesla Siemens MRI system , using a T2*-weighted gradient-echo planar imaging pulse sequence  that discriminate between PTSD patients and controls.All computational experiments employed a cross-validation scheme, in which five random splits of the dataset to train and test were created , which treats each of the sifier C , and oncons of x , σ is si2.5.3We used the Adam optimizer with 2.5.4Our primary analysis concerned the prediction of PTSD diagnosis at three different time-points. This problem is treated as a three-label classification and the network architecture described in 2.5.5According to DSM-5, PTSD includes four symptom clusters: Intrusion (criteria B) consisting of five symptoms; Avoidance (criteria C)- two symptoms; Negative alternations in mood and cognition (criteria D)- seven symptoms; and Hyperaousal (criteria E)- six symptoms. The classification is based on PTSD diagnosis (by DSM-5): meeting criteria B if there is at least one of the five symptoms; criteria C- if at least one of the two symptoms, criteria D- if at least two of the seven symptoms; and criteria E- if at least two of the six symptoms.To identify distinct, symptom-based clusters of PTSD, we trainrd our model, each time separately, on rs-fMRI dataset. The network architecture described in 2.5.6In clinical use, it is beneficial to predict remission from PTSD. Starting with a trauma-exposed group with high rates of PTSD at T1 (about 70%), we observe recovery over time  resulting in a final group with mostly recovered healthy individuals and only a subset of PTSD patients (about 23%). We therefore aim to explore the chronicity of the PTSD symptoms from T1 to T3.To this end, we trained our model to predict whether patients of each symptom cluster would still meet the same diagnostic criteria at T3. In this case, the network architecture described in 2.5.7To identify discriminative connectivities, we compared the pairwise connectivities . We performed an ablation analysis that demonstrates the contribution of the various components of our method, by employing multiple method variants of it. First, a model in which the network eep FMRI , however network  (GCN) in33.1We report the mean and the standard deviation of the accuracy for each of the three time-points and prediction tasks as well as the AUC and average precision values from the prediction scores. The same experiments are also repeated when minimizing the mean squared error (MSE) for the CAPS score, instead of the binary PTSD classification. Since there is a high correlation between CAPS-IV and CAPS-5 total scores across all time-points , normalizing the scores by considering the participant’s percentile among all participants and then choosing, if relevant, the most significant cluster for the participant. In other words, if there is no dominant cluster for the participant, we do not visualize the point and skip to the next participant. For full pseudo-code see Algo. E method , and the3.3We used the survival analysis in our prospective design to predict persistence of PTSD. Our model was trained for predicting whether participants still meet the same diagnostic criteria at T3. The fraction of non-remitted participants at T3 was The input to the subsequently trained predictive model was rs-fMRI data of participants, obtained one-month after trauma (T1), and the same cross-validation splits were employed as described in 3.4t-test identifying the discriminative power of the pairwise similarities  right superior temporal gyrus, (ii) left middle temporal gyrus, and (iii) left frontal pole. Nevertheless, as this is a visual processing task involving faces and shapes, we observed various connectivities at T1 known to be involved in visual processing  right amygdala with right lateral occipital cortex (ii) left amygdala with right lateral occipital cortex. The rest of the connectivities are related to the nature of this dynamic task, as was shown in previous work   Four rs-fMRI significant connectivities were found. None of them is consistent with the neuroimaging literature of PTSD. Emotion reactivity taskSRDC Task  The majority of the 135 significant connectivities that were found are highly relevant to the task   Seven significant connectivities were found, including one which is specifically relevant to PTSD: right amygdala with right parahippocampal gyrus. Emotion reactivity task  Out of 84 significant connectivities were found, only one involves the amygdala, namely amygdala-brain stem connectivity. Similar to T1 and T2, we observe the dominance of neural connectivities related to visual processing: Almost 50SRDC task  Out of the 115 significant connectivities that were found, seven connectivities involve the amygdala. Interestingly, ten connectivities involve the insula and nine connectivities invlove the anterior cingulate cortex, both are part of the Salience Network . Nevertheless, we also trained the proposed architecture on different fMRI tasks, which provided further valid information on brain processing related to emotional reactivity, risky behavior, and sensitivity to punishments and rewards, as well as additional analyses pointing to the performance of our method in comparison to other approaches. Indeed, training the model on two different fMRI tasks, in addition to rs-fMRI data, may provide a more comprehensive understanding of neural activity in individuals with PTSD.Here, we utilized the within-subject prospective design to predict a dynamic PTSD diagnosis for each patient, using neural measures obtained one-month after trauma and clinical status obtained one-, six- and fourteen months following the trauma. As demonstrated in Beyond the clinical dichotomous PTSD diagnosis (yes or no) and general symptom severity , we assessed different PTSD symptom clusters in order to account for the known heterogeneity in symptom manifestation. This characterization could guide a more personalized clinical approach, for example by targeting the most dominant symptom cluster for each individual. The majority of studies found only qualitatively distinct profiles among diagnosed participants, and were conducted using the DSM-IV-TR symptom criteria or combined PTSD symptoms with additional items  . To dateIn the second axis of our work, we filled this gap by training our model to identify distinct, symptom-based clusters of PTSD according to the most updated DSM-5 criteria. We obtained high predictive ability with an average AUC of around 0.8 for the different PTSD symptom clusters (across the three time-points) see , Table 8The third axis of our work was the survival analysis. In clinical use, it is beneficial to predict remission or persistence of initial PTSD symptoms. Survival analysis studies the time to a dichotomous event , enabling group comparisons, accounting for censoring and inclusion of time-dependent covariates. Previous survival methods were conducted in PTSD populations mostly using single sampling and only demographic and clinical variables. For example, Finally, for the fourth axis of our analysis, we applied statistical tests to identify pairwise correlations in the PTSD prediction model. The reported resting state discriminative connectivities are consistent with commonly found abnormal resting state functional connectivity patterns in PTSD involving amygdala functional connections see  Sripada. InteresAlthough our findings are promising and the cohort is relatively large for an fMRI study of clinical population, this work has several limitations that arise from the challenges of recruiting participants on a larger scale. First, there is class imbalance as 115/160, 39/135, 29/130 participants met PTSD diagnostic criteria at T1, T2 and T3, respectively. That is, most individuals recovered from initial symptoms after six- or fourteen-months from the traumatic incident. To overcome this challenge, we used weight-balancing during the training of the model. For each time-point, the weight was proportional to the ratio between the number of subjects with PTSD and the total number of subjects in this time-point. We also reported balanced error measures, such as the AUC. Second, the number of participants with PTSD decreased between the time-points and at T3 there were only 29 participants with PTSD. Splitting the data to 80% training and 20% test resulted in 23 participants with PTSD in the training set, and 6 in the test set. While this might have reduced the statistical power of the results, we repeated the experiment five times  in order to somewhat mitigate this issue. Lastly, in this work we used fMRI scans at T1 in order to predict PTSD symptoms at T2 and T3, thus precluding mechanistic insights regarding the clinical outcome. Future work should consider longitudinal neuroimaging and neuropsychological measures from all three time-points to enhance explainability of the clinical prediction. This could possibly guide therapeutic approaches that are more mechanistic in nature.5Our work demonstrates a computational approach for identifying objective variables linked to clinical PTSD diagnosis across different time-points during the first critical year following trauma exposure, based on single-session examination of functional connectivity shortly after exposure to trauma. To this end, we applied a novel deep learning model that employs fMRI scans, obtained shortly after trauma, to predict PTSD symptoms at one-, six- and fourteen-months following trauma. Our method demonstrates a significant improvement in performance in comparison to other analytical techniques reported in fMRI literature. The prediction is highly accurate compared to the existing methods for all three time-points and benefits from learning these all at once, using a single model. We further show a high predictive ability for predicting PTSD symptom clusters and persistence of the disorder. If validated, the objective features derived from our computational model may further guide mechanism-driven interventions for PTSD . Our code will be promptly shared as an open-source code.The research study meets all ethical regulations as required by NYU Langone Health Institutional Review Board (IRB) and ethics committee in Tel-Aviv Sourasky Medical Center (Reference number 0207/14). All subjects gave written informed consent in accordance with the Declaration of Helsinki. This study is registered at ClinicalTrials.gov (registration number: NCT03756545)Shelly Sheynin: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing - original draft, Writing - review & editing. Lior Wolf: Conceptualization, Methodology, Writing - original draft, Writing - review & editing, Supervision. Ziv Ben-Zion: Methodology, Resources, Data curation, Writing - original draft, Writing - review & editing. Jony Sheynin: Methodology, Resources, Data curation, Writing - review & editing. Shira Reznik: Resources, Software. Jackob Nimrod Keynan: Resources, Data curation. Roee Admon: Resources. Arieh Shalev: Conceptualization, Methodology, Funding acquisition, Writing - original draft, Writing - review & editing. Talma Hendler: Conceptualization, Methodology, Resources, Funding acquisition, Writing - original draft, Writing - review & editing. Israel Liberzon: Conceptualization, Methodology, Resources, Funding acquisition, Writing - original draft, Writing - review & editing.


Epigenetic regulations of the tumor microenvironment (TME) and immunotherapy have been investigated in recent years. Nevertheless, the potential value of mitochondrial ribosomal RNA (mt-rRNA) modification in regulation of the TME and immunotherapy remains unknown.We comprehensively investigated the mt-rRNA-modification patterns in glioma patients based on nine regulators of mt-rRNA. Subsequently, these modification patterns were correlated systematically with immunologic characteristics and immunotherapy. An “mt-rRNA predictor” was constructed and validated in multiple publicly available cohorts to provide guidance for prognosis prediction and immunotherapy of glioma patients.Two distinct patterns of mt-rRNA modification were determined based on the evidence that nine regulators of mt-rRNA correlated significantly with most clinicopathologic characteristics, immunomodulators, TME, immune-checkpoint blockers (ICBs), and prognosis. Patients with mt-rRNA subtype II presented significantly poorer overall survival/progression-free survival (OS/PFS), but higher tumor mutational burden (TMB), more somatic mutations, and copy number variation (CNV). These two mt-rRNA subtypes had distinct TME patterns and responses to ICB therapy. An mt-rRNA predictor was constructed and validated in four glioma cohorts. The subtype with high mt-rRNA score, characterized by increased TMB, infiltration of immune cells, and activation of immunity, suggested an immune-activated phenotype, and was also linked to greater sensitivity to immunotherapy using anti-programmed cell death protein 1 (PD-1) but resistance to temozolomide.Regulators of mt-rRNA modification have indispensable roles in the complexity and diversity of the TME and prognosis. This novel classification based on patterns of mt-rRNA modification could provide an effective prognostic predictor and guide more appropriate immunotherapy/chemotherapy strategies for glioma patients. Gliomas are the most common primary intracranial tumors of the central nervous system. Of the subtypes of gliomas, glioblastomas are the most malignant and deadliest –3. The p5U429), METTL15 (modifies the nucleotide m4C839), NSUN4 (modifies the nucleotide m5C841), TFB1M (modifies the nucleotide 1A947), MRM1 (modifies the nucleotide Gm1145), MRM2/FTSJ2 (modifies the nucleotide Um1369), MRM3/RNMTL1 (modifies the nucleotide Gm1370) and RPUSD4 (modifies the nucleotide Ψ1397)  , 14–16. e Ψ1397) .Immunotherapy is represented by immune-checkpoint blockade. It has been studied extensively in recent years as novel and satisfactory therapy for malignant tumors. Unfortunately, immunotherapy cannot extend the survival of patients , 21. NumIn this study, we analyzed the expression patterns and immunological value of the regulators of mt-rRNA modification. Based on the features of nine mt-rRNA regulators, we identified two patterns of mt-rRNA modification in glioma patients which had distinct functional annotations, clinicopathologic characteristics, and survival outcomes. Subsequently, integrated analyses revealed significant differences in genomic variation, the TME, and ICB expression levels between the two subtypes of patients, and determined the different responses to immunotherapy. Therefore, we constructed an “mt-rRNA predictor” that could distinguish the two mt-rRNA subtypes of glioma patients and verified it in four independent cohorts of glioma patients. This mt-rRNA predictor could provide a selection strategy for screening glioma patients who elicit a positive response to immunotherapy or temozolomide therapy. In this way, we aimed to provide novel ideas for survival prediction and better targeted therapy for glioma patients according to the classification of mt-rRNA modification patterns.The flowchart of this work is shown as https://portal.gdc.cancer.gov/), Chinese Glioma Genome Atlas  and Gene Expression Omnibus  databases. After excluding patients with incomplete clinical information, 1136 glioma samples , n = 248) were gathered for further analyses. Subsequently, transcripts per kilobase million (TPM) values of RNA-sequencing data, robust multichip averaging analysis (RMA)-processed values of microarray data (GSE16011), RNA-sequencing data , and real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR) data (SAHNU cohort) of clinical samples were log2-transformed and normalized to make the gene-expression profiles of different platforms comparable. The clinical information of the 1136 public glioma patients is displayed in www.r-project.org/). Amplification or deletion of copy number variation (CNV) data were identified using the GISTIC algorithm (www.cancerrxgene.org/)  from 2015 to 2020. Clinical information was obtained through electronic medical records. Overall survival (OS) data were not available because information based on telephone follow-up were missing. Thirteen samples of high-grade glioma  grade III, IV), 12 samples of low-grade glioma (WHO grade II), and eight samples of non-neoplastic brain tissue  were employed. The clinical information of these 33 patients is also displayed in The study protocol was approved by the medical ethics committee of the Second Affiliated Hospital of Nanchang University  and was undertaken in accordance with the Helsinki Declaration 1964 and its later amendments. Written informed consent was acquired from each glioma patient.www.proteinatlas.org/). Meanwhile, the quantity and intensity of Staining as well as the information of patients were acquired online.The immunohistochemistry of pathologic specimens of the nine regulators of mt-rRNA modification were downloaded from The Human Protein Atlas ; ICBs expression level. First, we obtained the composition of 122 immunomodulators  from the research of Charoentong et al., and collected 28 ICBs with therapeutic potential from the work of Auslander and colleagues , 30. TheThe expression levels of nine regulators of mt-rRNA modification were extracted from the TCGA dataset for further classifying . UnsuperMaftools and GenVisR were run to analyze and visualize the mutation types and frequencies of genes between two mt-rRNA subtypes , 37. Thewww.gsea-msigdb.org/gsea/msigdb/)  using the R package “GSVA “based on the gene sets of “c2.cp.kegg.v6.2.symbols”, which we downloaded from the Molecular Signatures Database (MSigDB) database (msigdb/) . The KEGhttp://tide.dfci.harvard.edu/) is an algorithm used to determine the characteristics of T-cell dysfunction by testing how the expression of each gene in a tumor interacts with the level of cytotoxic T lymphocyte (CTL) invasion to affect the survival of patients and response to immunotherapy (https://cloud.genepattern.org/gp/) method  method . BonferrThe total of 1136 glioma patients were classified into a training set (TCGA) and three validation sets . First, in the training set, we undertook univariate Cox analysis of these mt-rRNA regulators using the R package “survival”. Subsequently, the least absolute shrinkage and selector operation (LASSO) algorithm was utilized to select optimal-candidate differentially expressed mt-rRNA regulators with the best discriminative capability in the training set. Then, we constructed an mt-rRNA predictor based on the expression profiles of mt-rRNA regulators weighted using the multivariate Cox regression coefficient as follows:iCoef is the coefficient and iExp is the expression of mt-rRNA-modification regulators. The coefficient of each regulator was also obtained to calculate the mt-rRNA score in the validation sets. Specifically, glioma patients were classified into low-score and high-score subtypes based on the median mt-rRNA score. The prognostic importance of the mt-rRNA predictor was assessed between the two subtypes using the Kaplan–Meier method, and the prediction efficiency was tested further with receiver operating characteristic (ROC) curves. Moreover, univariate and multivariate Cox regression analyses were carried out to explore the prognostic value of the mt-rRNA predictor with multiple clinical and molecular pathologic characteristics. Similar methods were used to verify the predictive performance of mt-rRNA predictor in the validation sets.where Temozolomide is first-line chemotherapy for glioma patients. We obtained the IC50 of temozolomide in glioma specimens and RNA-sequencing data from the largest publicly available pharmacogenomics database (GDSC) and normalized them by log2-transformation for further analyses. Glioma specimens treated with temozolomide were divided into two groups according to the median mt-rRNA score.ΔΔCT method. The primer sequences used for RT-qPCR were obtained from RiboBio  and were as follows: MRM2 forward 5’-GTGATTCTGAGCGACATGGC-3’, reverse 5’-ATGACTCTTTCCTGCTGGCT-3’; TRMT2B forward 5′-TCAAGAGTCCTAAATGCACAACC-3′, reverse 5′-CCAGGAGTCATCTCTACAATGC −3′.We used methods we described previously to measure mRNA expression of regulators of mt-rRNA modification in glioma specimens . BrieflyCorrelations between variables were assessed using Spearman or Pearson correlation analyses. Variables with a normal distribution were analyzed by the unpaired Student’s t-test. Variables with a non-normal distribution were analyzed by the Mann–Whitney U-test. For comparisons of categorical variables, the Kruskal–Wallis test and one-way ANOVA were used for non-parametric and parametric methods, respectively. The Kaplan–Meier method was employed to calculate the survival curve of categorical variables, whereas the log-rank test was used to estimate significance. According to the method of Hoshida and colleagues, the Bonferroni correction was applied to correct nominal P-values in the subgroup analysis of ICB immunotherapy. Statistical analyses were carried out using R 3.6.5, and P < 0.05 (two-sided) was considered significant.Considering the possible biological functions of mt-rRNA-modification enzymes in gliomas, we conducted a comprehensive study on these regulators in TCGA cohort. As shown in a heatmap, the expression of most regulators of mt-rRNA modification was associated significantly with the WHO grade  and deletion (P < 0.001), in patients with RM2, was significantly higher than that in patients with RM1   was significantly higher in the RM2 subtype  to explore the value of these mt-rRNA regulators for clinical application. First, in the training set, we found eight mt-rRNA regulators to be correlated significantly with OS (P < 0.01), which was consistent with our predictions using the GEPIA2 website    as predicted by the TIDE algorithm. In addition, patients in this group had longer OS and PFS. To make the method more clinically practical for glioma patients, the two most critical prognostic genes for mt-rRNA regulation were identified and defined as mt-rRNA predictors to distinguish different mt-rRNA subtypes by using the LASSO algorithm. The forecasted effect of this predictor was verified further in the CGGA, GSE16011, and SAHNC cohorts.In recent years, immunotherapy for tumors has been studied extensively using ICBs, tumor-cell immunotherapy, and antitumor vaccines. However, the immunotherapy effect in phase-III clinical trials for glioblastoma has not been satisfactory , 43. SevIncreasing evidence has demonstrated that RNA modifications have indispensable roles in inflammation, innate immunity, and anti-tumor activities, among which the most extensive and in-depth research has been on m6A. Bo Zhang and colleagues demonstrated that modification of m6A methylation is involved in the regulation of the microenvironment of gastric cancer, and has a guiding role in immunotherapy . JianyanIn this work, based on nine mt-rRNA regulators, we revealed two distinct subtypes of mt-rRNA modification with significantly different TME characterization. RM2 was characterized by the activation of immunity and related enriched pathways, which corresponded to an immune-activated phenotype. RM1 was characterized by infiltration of innate immune cells , which corresponded to an immune-excluded phenotype , 49. MeaHowever, our study still had two main limitations. First, we obtained only 33 glioma specimens with pathologic data, but no clinical outcomes, to verify the results of our study. We need to collect more glioma samples and obtain the corresponding clinical outcomes from our center in the future. Second, our conclusions are based only on the analysis and prediction of transcriptome data in publicly available databases, the relationship between mt-rRNA modification and immunotherapy responsiveness needs to be tested in the future immunotherapy cohort.In conclusion, according to patterns of mt-rRNA modification, glioma patients can be divided into two distinct subtypes which have a notably different prognosis and response to immunotherapy. Our study provides a potential strategy for “individualized” tumor immunotherapy in the future. We constructed a mt-rRNA predictor which made subgrouping based on mt-rRNA regulators and precision immunotherapy clinically feasible.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by The medical ethics committee of the Second Affiliated Hospital of Nanchang University. The patients/participants provided their written informed consent to participate in this study.XZ and KH designed the research. JL, MW, and MY contributed to the collect the brain tissue samples and matching clinical data. PW contributed to the public data collection and analysis, figures and tables, complete the experiments, and were involved in manuscript writing. XZ and KH performed the correction of the language and revision. All authors contributed to the article and approved the submitted version.The present study was supported by the National Natural Science Foundation of China .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.


Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study . There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of  Renal cancer is one of the most common malignancies, being the sixth most prevalent type of cancer among men and the eighth most prevalent among women. For the past several decades, an increasing number of new patients have been diagnosed with renal cancer. The year 2020 saw approximately 74,000 diagnoses of renal cancer in the United States ,2, and 1Renal cancer is a heterogeneous disease in which the renal cells become malignant (cancerous) and form tumors called renal masses. These renal masses, if not detected early and treated promptly, will lead to mortality. The most common, and also the most aggressive, renal cancer is renal cell carcinoma (RCC), accounting for 70% of all cases ,5. In tuEvidence of renal cancer can be found in complete blood count (CBC) to check for the number of red blood cells; urine tests to look for blood, bacteria, or cancerous cells in urine; and blood chemistry tests to quantify renal function by checking the levels of certain chemicals in the blood. These signs are suggestive at best, and inadequate for diagnosis or typing of renal cancer. Only biopsy, performed using interventional radiology, can provide a definite diagnosis of renal cancer, and thus remains the gold standard ,2. Howevp = 0.022). As a diagnostic indicator of RCC, the entropy feature had high specificity (85.5%), but quite low sensitivity (31.3%) [One of the most important diagnostic imaging modalities for the accurate diagnosis of renal tumors is contrast-enhanced CT (CE-CT) ,21. Besi (31.3%) .Another study was conducted by Kunapuli et al.  to exploKocak et al.  conducteA bigger study was performed by Sun et al.  to compaLee et al.  used TA Oberai et al.  investigZhou et al.  conducteShehata et al.  publisheMost of the studies referenced above were pure applications of TA to CE-CT imaging. That is to say, they did not integrate other features  with two- or three-dimensional textural features to diagnose RCC. Only a few studies addressed typing of RCC, i.e., discrimination between ccRCC and nccRCC, which is vital information for deciding the course of treatment from the beginning. To overcome these limitations, we developed RC-CAD, a two-stage system for comprehensive computer-assisted diagnosis of renal cancer based on CE-CT imaging. RC-CAD  incorporIt is worth noting that this paper extends our recent work  with thet = 0, t = 80, and t = 300 s, respectively. All images were acquired using the following parameters: slice thickness = 2.5 mm; pitch = 0.984; rotation time = 0.75 s.Patients who had undergone renal biopsy for suspected cancer , as shown in For accurate identification of malignant renal tumors and the associated subtype, all 3D segmented volumes were characterized by their morphological, textural, and functional features, as described below.Morphological features: To enhance both the sensitivity and specificity of early renal cancer diagnosis, morphological features of the tumor are incorporated into the algorithm. These features were designed to quantify the complex shape of the tumor boundary. This was motivated by the hypothesis that rapidly growing, malignant tumors develop more irregular/complex shapes relative to more slowly growing, benign tumors. Therefore, the utilization of such shape descriptors would enhance the performance of the automatic diagnosis. Examples of this phenomenon are illustrated in Naturally, in order to measure the irregularity of the boundary, we must first construct an accurate shape model of the tumor. In this paper, we incorporated a state-of-the-art spectral decomposition in terms of spherical harmonics (SHs)  to constechnique  to map ti at the beginning of iteration i to node j by i and j is i in the triangulated mesh. Then, the attraction step updates the position of each node to keep it centered with respect to its neighbors:Each iteration The quantities Just as the attraction step, the repulsion step uses an implementation-defined parameter At the terminal iteration N, yielding an Nth order approximation. In practice, of course, the SH series is truncated by discarding harmonics above degree Equation . The firInitialization:Triangulate the surface of the tumor.Smooth the triangulated mesh with Laplacian filtering.Initialize the spherical parameterization with an arbitrary, topology-preserving map onto the unit sphere.T.Fix values of Attraction–repulsion:For‒For*Calculate ‒For*Calculate Equation *Let ‒IfThen, let Stop.Textural features: Recently, TA has become a popular research topic, particularly in the field of medical imaging. New techniques of TA provide different quantitative patterns/descriptors by combining the grey values of each pixel/voxel in a tumor image/volume. As a result of these abilities, TA has been used in the diagnosis of several tumors and their related subtypes with encouraging classification abilities [bilities ,46,47,48First-order textural features: These textural features include any quantity that can be derived from the gray-level histogram of the tumor volume. In particular, mean, variance, standard deviation, entropy, skewness, kurtosis, cumulative distribution functions, and the grey-level percentiles [centiles  were extSecond-order textural features: Since the first-order textural features might not be sufficient, with their range of values exhibiting significant overlap across classes, especially between subtypes of malignant tumor, second-order textural features were incorporated into the system. These features describe the joint distribution of gray values in multiple voxels that are considered to be neighbors of each other. In particular, the grey-level co-occurrence matrix (GLCM) [x (GLCM)  was usedi and j contribute to row i, column j of the GLCM). To define our neighborhoods, we used a distance criterion that voxels must be separated by ≤To construct the GLCM, we must count the number of times an ordered pair of two grey values occurs in two neighboring voxels within the renal tumor object. This technique is continued until all conceivable occurrence frequencies within the grey-level range of the renal tumor item are found, which covers all possible pairs of neighbors. For this, we first contrast stretched the renal tumor object’s original grey-level range to fit the desired span 0–255, yielding a GLCM matrix with a size of 256 × 256. Then, all feasible pair combinations were identified to construct the GLCM matrix  from benign (AML) tumors. In the case of malignancy, the second stage provides the classification of RCC tumors as ccRCC or nccRCC.N = 6; mean, variance, standard deviation, skewness, kurtosis, and entropy); Feature Set 2 includes first-order percentile textural features ; Feature Set 3 includes second-order GLCM textural features ; Feature Set 4 includes SH reconstruction error (SHRE) morphological features (N = 70); and Feature Set 5 includes functional features . At each classification stage, the individual feature sets were concatenated to obtain the combined features (N = 94) and were fed to a MLP-ANN to obtain the final diagnosis.The multilayer perceptron (MLP) artificial neural network (ANN) consists of at least three layers: an input layer, one or more hidden layers, and an output layer, each with arbitrarily many activation/processing units, known as nodes/neurons. Each layer is fully connected to the next layer in sequence. Neurons use nonlinear activation functions to give the MLP-ANN the capability to divide the feature space into arbitrarily complex regions. The MLP-ANN mainly utilizes supervised backpropagation learning technique in the training phase, in which gradient descent methods are utilized to update the connection weights and additive biases in order to minimize the loss function. To achieve our goal, we utilized the MLP-ANN in both classification stages to obtain the final diagnosis. Classifier performance was assessed using five different feature sets  as the AThe diagnostic performance of the RC-CAD system on our dataset of 140 renal tumors was assessed using leave-one-subject-out (LOSO) cross-validation. The system’s diagnostic capabilities were assessed, evaluated, and compared in both classification stages using the individual feature sets, as well as the combined features. Each classification process was repeated 10 times, and the results were tabulated in terms of the mean ± the standard deviation to provide a more quantitative expression of the diagnostic performance.The first stage classification (RCC vs. AML) performance for the RC-CAD system was first evaluated using individual Feature Sets 1, 2, 3, 4, and 5 see  along wiThe diagnostic performance of the second stage classification (ccRCC vs. nccRCC) of the RC-CAD system was evaluated using the same LOSO cross-validation approach. As before, specially tailored MLP-ANN models were used with different feature sets. The best second stage classifier performance was obtained using the concatenated feature set .To ensure that our system is not prone to overfitting and to validate the reproducibility and robustness of RC-CAD, we performed a randomly stratified 10-fold cross-validation approach in both stages using the combined features. Likewise, the classification process was repeated 10 times using the same MLP-ANN classification model, and the results are tabulated in terms of the mean ± the standard deviation .To highlight the advantages of using the MLP-ANN classifier, we compared RC-CAD with other, well-known machine learning classifiers  and malignant (RCC) and in classification of the RCC subtype into ccRCC or nccRCC. This early and precise identification of the malignancy status of a given renal tumor and its associated subtype can enable clinicians to provide the appropriate early intervention/treatment plan and improve the outcomes. CE-CT was utilized as it is an imaging modality with the ability to provide different aspects of features, including but not limited to, morphological features, textural features, and functional features. The integration of these features is effective in determining the malignancy status of a given renal tumor when combined with a powerful machine learning classifier such as the MLP-ANN.The grade of malignancy of a given renal tumor largely specifies the morphology of the tumor. Typically, malignant tumors demonstrate a more complex morphology than benign ones. Therefore, morphological features based on using spherical harmonics were utilized to capture possible surface complexity differences between malignant and benign renal tumors, as well as differences between different subtypes of malignancy.First- and second-order textural features have been widely utilized to identify a given renal tumor status as malignant or benign, as well as to describe the malignancy subtype ,31,32,38Additionally, functionality was utilized in identifying the malignancy status of a given renal tumor. The slopes of wash-in and wash-out can capture the existing differences in the enhancement characteristics ,52. In tAlthough individual features have provided a reasonable diagnostic performance, they are not sufficient to rule out surgical intervention in (what may turn out to be) benign lesions. Therefore, the integration process of these features is critical to enhance the diagnostic accuracy to the point of clinical utility. The integration process produced a reliable and accurate RC-CAD system with an enhanced diagnostic performance in both classification stages as documented in This study has some limitations: (i) benign tumors only included AMLs and did not include any ONCs; (ii) the datasets in this study were all collected from the same geographical area, and thus, we did not account for population diversity; (iii) demographics such as age and sex were not included in our analysis; (iv) differentiation between paRCC and chrRCC was not performed due to the limited number of subjects; and (v) the RC-CAD system in its current form still requires expert knowledge to segment the renal tumor manually before the handcrafted features are extracted. Despite these limitations, the RC-CAD system demonstrated the efficacy and feasibility of integrating various types of features to account for different aspects, making the developed RC-CAD a reliable noninvasive diagnostic tool.The developed RC-CAD system demonstrated a high classification sensitivity of


Background: Although smoking status has potential as a biomarker for immune checkpoint blockade in advanced non-small cell lung cancer (NSCLC), its clinical significance remains obscure. This meta-analysis aims to assess the impact of the smoking status on the efficacy of first-line immunotherapy and to find better treatment in never-smoker and ever-smoker patients.Methods: We searched the MEDLINE, EMBASE, and Cochrane database for trials comparing immunotherapy with conventional chemotherapy as front-line treatment for advanced NSCLC. Random-effects models were used to pool estimates of hazard ratios (HRs) for overall survival with 95% confidence intervals (CIs). Predefined subgroup analysis was performed to investigate the difference in the efficacy between the single checkpoint blockade and checkpoint inhibitor plus chemotherapy combination in the never-smokers and current/former smokers.Results: Twelve trials involving 6,446 patients were included in the analysis. A statistically significant overall survival benefit over conventional chemotherapy was found for both checkpoint inhibitor monotherapy  and checkpoint inhibitor plus chemotherapy  in the current/former smoker group. There was no subgroup difference between monotherapy and combination treatment (p=0.67). However, there was an inconsistent survival outcome in the never-smoker group; checkpoint blockade monotherapy did not show significantly better efficacy than chemotherapy alone , but combination treatment showed an overall survival benefit . A significant subgroup difference existed between monotherapy and combination therapy (p=0.04). Similarly, there was a significant difference in efficacy of monotherapy between the current/former smoker and never-smoker group (p=0.01), but the efficacy of the combination treatment was comparable between the two groups (p=0.45).Conclusion: Smoking status, which is easily available information, could be used as a guide in clinical practice to choose better treatment in the front-line setting for advanced NSCLC patients. Recent advancements in immune checkpoint inhibitors have transformed the treatment of advanced non-small cell lung cancer (NSCLC) using targets known immune checkpoint molecules, such as the programmed death-ligand 1 (PD-L1) and its receptor, the programmed death-1 (PD-1) However, as only a limited portion of the population with advanced NSCLC experiences long-term effects of immune checkpoint inhibition, it is still crucial to find key indicators that could maximize the efficacy of immunotherapy and guide clinical decision-making processes. PD-L1 expression is the most studied biomarker to date, and most trials demonstrated a trend between increased level of the PD-L1 expression and improved efficacy of immune checkpoint inhibitors, though its clinical usefulness still remains a topic of debate. Other researched biomarkers such as tumor mutation burden (TMB) or gene expression profiling have potential as a predictive modality, but the standardization issues and the accuracy of the prediction still need to be solved Smoking status has also been reported to have the predictive potential for immunotherapy. Previous studies that assessed the comprehensive mutational landscape of NSCLC reported that smoking exposure enhanced somatic mutations, thereby could increase tumor response to anti-PD-1/PD-L1 therapy However, in more detail, it has been reported that there was a substantially better response to single agent checkpoint inhibitor as first-line therapy in the current/former than in the never-smoker group This systematic review and meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline immune checkpoint inhibitors, carcinoma, non-small cell lung, and randomized controlled trial. The details of the search strategy are described in the eMethods in the Two authors (J.K. and H.H.) separately carried out a comprehensive systematic search of the literature from inception to January 15, 2021. Randomized controlled trials that compared immune checkpoint inhibitor-based treatment with chemotherapy as first-line therapy for advanced NSCLC were searched in MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials. Searches were confined to human studies without language limitations. The main keywords used for the literature search were the We included studies meeting the following eligibility criteria: randomized controlled trial; studies including patients with advanced NSCLC ; trials comparing first-line immune checkpoint inhibitors (with or without chemotherapy) to a conventional chemotherapy agent; published as full-text articles; and studies with available data on patients' survival data according to smoking status. Studies that were retrospective or prospective observational cohort studies were excluded. Studies that compared checkpoint inhibitors with chemotherapy as second- or later-line treatment were also excluded. In addition, studies that used chemotherapy plus anti-angiogenic agent as control arms were excluded to secure maximum homogeneity.We abstracted the most extended follow-up data including updated survival analysis in cases of multiple sources reported in the same study. The following items were extracted from each included article: trial name, treatment details, PD-L1 expression indication, study patients' clinical information , median follow-up duration, crossover rate, and the number of patients by smoking status. The hazard ratio (HR) with a corresponding 95% confidence interval (CI) for overall survival was extracted from the studies, as the primary outcome was overall survival, the time from randomization to the date of death from any cause. Two authors (J.P. and J.C.) extracted the data independently using a predefined datasheet, and the other two authors (J.H.L. and M.H.L.) resolved the inconsistencies in the extracted data. Two reviewers (J.K. and M.H.L.) evaluated the quality of the included trials using the Cochrane Collaboration risk-of-bias tool.To find out whether the efficacy of checkpoint inhibitor alone and the checkpoint inhibitor plus chemotherapy combination differ according to the smoking status and to support decision-making processes in clinical practice, we planned to perform the following analyses.1) Comparison of the efficacy of the checkpoint inhibitor monotherapy and the checkpoint inhibitor plus chemotherapy combination in the current/former smoker group and never-smoker group, respectively.2) Comparison of the efficacy of the checkpoint inhibitor monotherapy and the checkpoint inhibitor plus chemotherapy combination, which were FDA-approved, in the current/former smoker group and never-smoker group, respectively.3) Comparison of efficacy of the current/former smoker and never-smoker group in the checkpoint inhibitor monotherapy group and the checkpoint inhibitor plus chemotherapy combination group, respectively.2 statistic. All reported P values were two-sided, and less than .05 were considered statistically significant.We conducted a meta-analysis using Review Manager, version 5.4. The inverse variance method for meta-analysis of HR was used. A random-effects models were utilized to calculate pooled HRs, corresponding 95% CIs, and P values under the assumption of clinical heterogeneity inherent in the pooled data. Study-level heterogeneity was assessed using the Q test and the IA total of 3,143 articles were searched by the initial search strategy. After the removal of 505 duplicates, 2638 studies' titles and abstracts were screened. After a full-text review of the 94 potentially eligible studies, 12 trials meeting the inclusion criteria were selected for the quantitative analysis . In the current/former smoker group, both checkpoint inhibitor plus chemotherapy combination  and checkpoint inhibitor monotherapy  showed a significantly better overall survival than chemotherapy .In the never-smoker group, checkpoint inhibitor plus chemotherapy combination versus chemotherapy revealed a significantly better outcome of combination treatment . However, checkpoint inhibitor monotherapy  did not demonstrate survival benefit over chemotherapy  and regimens, we performed another quantitative analysis confined to the trials with FDA-approved checkpoint inhibitor regimens. Similarly, the efficacy of checkpoint inhibitor plus chemotherapy combination and checkpoint inhibitor monotherapy versus chemotherapy were significantly different in the never-smoker group p=0.03, A but werp=0.01). However, the combination treatment was associated with improved overall survival compared to chemotherapy in both never  and current/former smoker groups .Reversely, the efficacy of checkpoint inhibitor monotherapy and checkpoint inhibitor plus chemotherapy combination was also evaluated according to smoking status (never vs. ever-smoker). The monotherapy revealed the significantly improved treatment effect compared to cytotoxic chemotherapy in the current/former smoker group , while a trend of better survival outcome for chemotherapy compared to single agent checkpoint blockade treatment in the never-smoker group was shown , but showed a significantly superior survival outcome over chemotherapy in current/former smoker patients with PD-L1 expression ≥50% (HR 0.60; 95% CI, 0.50-0.72, Fig. Despite the therapeutic efficacy of immune checkpoint inhibitors in a subset of patients, easily obtainable and consistent predictors of efficacy remain somewhat elusive. Regarding the smoking status as a predictive biomarker for immunotherapy, the results of several reports were substantially heterogeneous because of the massive scope of the previous pooled analyses, which comprised clinical trials studying multiple tumor types and using checkpoint inhibitors for different lines of treatment .There has been a recently published study regarding the impact of smoking status on the efficacy of immunotherapy in advanced NSCLC. A meta-analysis by Dai et alSmoking can influence TMB level via the accumulation of somatic mutations by carcinogens in tobacco, leading to a higher neoantigen load Although only a small number of patients without a smoking history were included in each trial, it has been reported that there was a noticeable different response to checkpoint inhibitor monotherapy as front-line treatment between the current/former and never-smoker However, in contrast, trials of the checkpoint inhibitor and chemotherapy combination mostly did not demonstrate a significant difference in treatment effectiveness between the two groups; subgroup analysis according to TMB level also showed a similar survival benefit of combination treatment in the TMB-high and TMB-low subgroups Our study's major limitation was the discrepancy of PD-L1 expression indication among the included trials. Most of the trials with checkpoint inhibitor monotherapy enrolled patients harboring PD-L1 expression more than 50%, and trials with combination treatment included patients regardless of PD-L1 expression. What we found in this study was that checkpoint inhibitor monotherapy was significantly less effective than the combination treatment in the never-smoker subgroup; considering the efficacy analysis according to the PD-L1 expression level, it is unlikely that the never-smoker subpopulation with PD-L1 expression ≥50% is less responsive to the checkpoint inhibitor plus chemotherapy combination than the subpopulation with low PD-L1 expression. Additionally, subgroup analysis of the Keynote-042 trial reported that the difference in efficacy of pembrolizumab between the patients with the PD-L1 expression ≥1% and ≥50% in the never-smoker group was almost similar Additionally, we encountered a few other limitations during this study. First, due to the intrinsic limitation for analysis of subgroup population, there could be slightly unbalanced patient distribution and features between the intervention and control according to smoking status. Second, we could not perform subgroup analysis to assess heterogeneity within combination therapy and monotherapy group due to the scarcity of included studies. However, the current analysis is a study that performed a predefined subgroup analysis by the smoking status, and additional analysis confined to FDA-approved trials was carried out to reflect the actual situation and control various sources of heterogeneity. Third, the current and former smokers were grouped together and analyzed as a single group, and detailed analysis according to the quantitative information on smoking was not possible. Despite these limitations, the present analysis provided evidence that the smoking status, which is easily obtainable information from patients, could be used as a guide to help with clinical decision-making processes in front-line treatment for advanced NSCLC.In summary, the current study suggests that checkpoint inhibitor monotherapy should be cautiously used in first-line systemic treatment for never-smoker patients with advanced NSCLC, and checkpoint inhibitor and chemotherapy combination treatment should be considered first in this group.Supplementary methods and figures.Click here for additional data file.


Elite athletes use high-intensity training to maintain their fitness level. However, intense training can harm the immune system, making athletes suspectable to COVID-19 and negatively affecting their performance. In addition, the diet of athletes should be appreciated more as it is another influencer of the immune system, especially during the COVID 19 pandemic. The other important issue elite athletes face currently is vaccination and its possible intervention with their training. The present study attempts to discuss the impact of different training intensities, nutritional strategies, and vaccination on the immune system function in elite athletes. To this end, Scopus, ISC, PubMed, Web of Science, and Google Scholar databases were searched from 1988 to 2021 using the related keywords. The results of our review showed that although high-intensity exercise can suppress the immune system, elite athletes should not stop training in the time of infection but use low- and moderate-intensity training. Moderate-intensity exercise can improve immune function and maintain physical fitness. In addition, it is also better for athletes not to undertake high-intensity training at the time of vaccination, but instead perform moderate to low-intensity training. Furthermore, nutritional strategies can be employed to improve immune function during high-intensity training periods. The immune system plays a significant role in protecting the human body from bacterial and viral infections and other environmental contaminants . SeveralThe immune system plays a vital role in the success of elite athletes . Immune After undergoing home quarantine, many athletes have resumed intense training to maintain their physical fitness levels for upcoming competitions. However, studies have shown that after exercise cessation due to the COVID-19 pandemic, retraining should be started with caution . AnotherNutrition is another critical factor influencing immunity because macro- and micro-nutrients are involved in multiple immune processes . NutritiA literature search was conducted on Scopus, Web of Science, ISC, Pub-Med, and Google Scholar databases from 1988 to 2021 to find the related articles using several keywords, i.e., low-intensity exercise and immune system, moderate-intensity exercise and immune system, high-intensity exercise and immune system, low-intensity exercise and athlete recovery, moderate-intensity exercise and athlete recovery, high-intensity exercise and athlete recover, sports nutrition and athlete immune system, athlete stress and immune system, athlete sleep and immune system, carbohydrate and athlete immune system, vaccination and elite athletes.In this review, we included any randomized single or double-blinded case-control, cohort, or experimental study with an intervention involving or an objective to explore the relationship between different intensities of exercise and immunity in athletes. Studies with unclear statements or results, not mentioning exercise intensity, and do not have a control group, were excluded. In addition, studies published in languages other than English were excluded.2max), is also referred to as long slow distance training or zone-1 training. Steensberg et al. [2max) for less than 60 min can reduce inflammation and improve immune function [There is very little research on low-intensity training and athletes’ immune systems. Low-intensity training  and causes accumulated lactate levels . PerformMacIntosh et al.  reportedIn another study, Fashi et al.  concludeTaken together, these studies demonstrate that moderate-intensity exercise is the best strategy in preventing suppression of the immune system .2max [High-intensity exercise refers to an intensity higher than 70% of VO2max . High-in2max . Some ma2max . In addi2max  (Figure Evidence suggests that chronic exercise can increase upper respiratory tract infections in athletes . The risSvenndsen et al.  reportedAllergic rhinitis is common among athletes due to regular high-intensity exercise . To prepIt has been found that viral infections can happen due to intense training in elite athletes, leading to a decrease in aerobic performance, especially among those affected by COVID-19 . It has In the previous sections, the importance of vaccination in elite athletes was highlighted. One concern with athlete vaccination is that the first dose may have short-term side effects. These side effects vary depending on the type of vaccine . It is rAdherence to diets and dietary supplements during high-intensity interval training can minimize the suppression of the immune system .It has been shown that carbohydrate consumption may improve immune function , furtherIn addition to carbohydrates as the primary fuel for athletes, protein is needed to maintain/increase their muscle mass. The amount of protein an athlete needs depends on their type of activity. The general recommendation is an average intake of 1.3 to 1.8 g per kg per day . A proteHerbal supplements or complementary and alternative medicines are familiar due to their unique medicinal properties. They also help to enhance the immune system by adding isolated vitamins and minerals. A wide range of phytonutrients, such as polyphenols, flavonoids, carotenoids, sulforaphane, curcumin, sulfides, and plant sterols, are found in herbs . Some ofThe results of our review showed that although high-intensity exercise can suppress the immune system, elite athletes should not stop training during the period of infection, but use low- and moderate-intensity training. Moderate-intensity exercise can improve immune function and maintain physical fitness. In addition, it is also better for athletes not to perform high-intensity training at the time of vaccination but instead perform moderate- to low-intensity training. Furthermore, nutritional strategies can be employed to improve immune function during high-intensity training periods.


Resistance to tamoxifen treatment is an obstacle for ER+ve breast cancer therapy. The overexpression of c-MYC is a known driver of cancer progression and is associated with tamoxifen resistance. Through mediating the up-regulation of ribosome biogenesis and alteration of the transcriptome, c-MYC modulates the translation profile to facilitate the development of tamoxifen resistance. c-MYC is, however, undruggable. Thus, targeting downstream mechanisms mediated by c-MYC might be a more feasible approach. Studies have demonstrated that inhibition of ribosome biogenesis can achieve tumour suppression. Targeting ribosome biogenesis may thus be a feasible strategy to reverse tamoxifen resistance. This article reviews the current evidence to support the feasibility of suppressing ribosome biogenesis to reverse tamoxifen resistance in ER+ve breast cancer.Breast cancer is a heterogeneous disease. Around 70% of breast cancers are estrogen receptor-positive (ER+ve), with tamoxifen being most commonly used as an adjuvant treatment to prevent recurrence and metastasis. However, half of the patients will eventually develop tamoxifen resistance. The overexpression of c-MYC can drive the development of ER+ve breast cancer and confer tamoxifen resistance through multiple pathways. One key mechanism is to enhance ribosome biogenesis, synthesising mature ribosomes. The over-production of ribosomes sustains the demand for proteins necessary to maintain a high cell proliferation rate and combat apoptosis induced by therapeutic agents. c-MYC overexpression can induce the expression of eIF4E that favours the translation of structured mRNA to produce oncogenic factors that promote cell proliferation and confer tamoxifen resistance. Either non-phosphorylated or phosphorylated eIF4E can mediate such an effect. Since ribosomes play an essential role in c-MYC-mediated cancer development, suppressing ribosome biogenesis may help reduce aggressiveness and reverse tamoxifen resistance in breast cancer. CX-5461, CX-3543 and haemanthamine have been shown to repress ribosome biogenesis. Using these chemicals might help reverse tamoxifen resistance in ER+ve breast cancer, provided that c-MYC-mediated ribosome biogenesis is the crucial factor for tamoxifen resistance. To employ these ribosome biogenesis inhibitors to combat tamoxifen resistance in the future, identification of predictive markers will be necessary. As of the end of 2020, 7.8 million women had been diagnosed with breast cancer in the past five years, making it the world’s most prevalent cancer [Breast cancer is the most common female malignancy and the second most common cause of cancer death in women. According to the World Health Organization (WHO) (The expression of estrogen receptor-α (ER), progesterone receptor (PR), and HER2 are important determinants for treatment and management, with about 70% of breast cancers being ER-positive (ER+ve). ER signalling is the main driving factor for cancer development in ER+ve breast cancer. ER is a ligand-dependent transcription factor. Upon estrogen binding, ER will bind to the responsive element in the target gene promoter, thus regulating the expression of target genes to promote cancer development . SubsequTamoxifen is the most commonly used adjuvant endocrine therapy. Tamoxifen directly competes with estrogen for binding to ER. In contrast to estrogen, the tamoxifen-receptor complex recruits co-repressors, rather than co-activators, to the promoter regions of estrogen-responsive genes. This blocks their transcription, thus suppressing genomic signalling mediated by ER . Due to c-MYC is a transcription factor. It can regulate the expression of genes, controlling the growth and proliferation of cells. It also plays a significant role in enabling tumours to escape immunosurveillance through decreased MHCI expression or upregulation of inhibitory cytokines and immune checkpoint proteins such as PD-L1 and CD47. These provide a compelling rationale for combining c-MYC inhibition with an immune checkpoint blockade . c-MYC ic-MYC was discovered almost 40 years ago as the cellular homolog of v-Myc, a viral oncogene derived from an avian myelocytomatosis virus that caused leukaemia and sarcoma in chicken . Two addc-MYC is documented to be one of the most up-regulated oncogenes in different types of cancers ,26. c-MYc-MYC has also been reported to induce the expression of HOXB7, which is a co-factor of ER, to favour the transcriptional activity of ER . c-MYC cc-MYC can regulate epithelial-to-mesenchymal transition (EMT) necessary for cellular invasion and migration, and thus metastasis. Studies report that c-MYC promotes TGFβ-mediated activation of the SNAIL transcription factor, both directly and indirectly, through a microRNA network involving a LIN28B/let-7/HMGA2 cascade to facilitate metastasis ,41. c-MYc-MYC is an estrogen-responsive gene . The ovec-MYC can regulate various cellular metabolisms, including glucose and glutamine, which are essential for generating enough energy and intermediates of macromolecules to support the high rate of cancer cell proliferation . In addic-MYC has been reported to enhance ribosome biogenesis by various mechanisms . First, On the other hand, the depletion of c-MYC has been shown to diminish ribosome production in colon cancer . Knockdoc-MYC can induce tamoxifen resistance through various mechanisms. For example, c-MYC has been reported to induce the transcription of survivin (encoded by the gene BIRC5), an essential member of the inhibitor of apoptosis protein (IAP) family, which plays an essential role in tumorigenesis . ThroughIn addition, c-MYC can induce tamoxifen resistance via the mRNA translation mechanism. c-MYC has been known to regulate mRNA translation through inducing the expression of translation initiation factors, such as eIF4E, eIF2α, eIF4AI and eIF4GI, which are needed for CAP-dependent translation . c-MYC cThe activity of eIF4E is governed by phosphorylation, especially Ser209 . MNK1 anThe overexpression of non-phosphorylated eIF4E can also confer tamoxifen resistance . By inveAberrant increases in nucleolar size and number that reflects increased ribosome biogenesis has been recognised for over a century as the hallmark of many cancers and has been associated with poor prognosis . Hyper-ac-MYC is a crucial regulator of protein biosynthesis. Target genes of c-MYC mediating transcription are essential for crucial steps in the ribosome biogenesis process, including the synthesis of ribosomal RNAs and proteins ,105. TheA research group demonstrated that the novel small molecule inhibitor CX-5461 could specifically target rDNA transcription, leading to the suppression of tumour development. This chemical has been used in phase 1 clinical trials  ,112. In Tamoxifen is commonly used for treating ER+ve breast cancer. However, tamoxifen resistance hinders the usefulness of tamoxifen. Identification of agents to reduce the resistance is urgently needed. The overexpression of c-MYC has long been suggested to be one of the significant factors for triggering the resistance ,119,120.The overexpression of c-MYC can enhance ribosome biogenesis to sustain the demand from cancer cells. Therefore, targeting the steps in ribosome biogenesis instead of c-MYC may provide new cues for reducing tamoxifen resistance in breast cancer. CX-5461 , CX-3543Breast cancer is a heterogeneous disease. Different subtypes will result in a different response to a particular treatment. Having a single therapy that can be used universally is unrealistic. Similarly, various molecular mechanisms, such as mutations and altered gene expression, can contribute to tamoxifen resistance; some even cross-talk with each other to make the tamoxifen-resistant mechanism more complicated. Therefore, it is not feasible to rely on a single agent to reverse tamoxifen resistance in all patients. c-MYC overexpression is a driver of cancer and drug resistance. The overexpression of c-MYC can rewire the gene expression profile to sustain the demands of cancer cells, enhanced ribosome biogenesis being one of them. Since c-MYC is undruggable, the downstream mechanisms mediated by c-MYC become the targets. Ribosomes are the place to produce proteins for supporting cell proliferation and combating cell death pathways induced by therapeutic agents. The suppression of ribosome biogenesis will therefore compromise the survival advantages of cancer cells. On the one hand, it will suppress the development of cancer cells; on the other hand, it will make the cancer cells more sensitive to the drug or even reverse drug resistance. Therefore, in ER+ve breast cancer, we believe that targeting ribosome biogenesis will be a feasible strategy to deal with tamoxifen resistance.


Teaching point: A space-occupying lesion should be considered in the differential diagnosis of progressive unilateral carpal tunnel syndrome. A 56-year-old women presented with long-lasting, radial-sided paraesthesia in her left hand, having suffered from a conservatively treated ipsilateral distal radius fracture six years prior. A mass was palpable in the palm of the hand, which moved proximally under the transverse carpal ligament upon flexion of the fingers accompanied by a painless “snap” (trigger phenomenon). Carpal tunnel syndrome (CTS) provocative tests were positive, and electromyography (EMG) confirmed CTS.Figure 1, arrow). On magnetic resonance imaging (MRI), the signal intensity was similar to that of subcutaneous fat, except for thin intralesional strands of non-lipomatous tissue, showing enhancement . The preferential diagnosis of fibrolipoma was made, although an atypical lipoma could not be excluded. Resection followed by histopathological examination showed multiple adipocytes with absence of atypical cells and fibrous bands were seen in between, confirming the diagnosis of a fibrolipoma .Conventional radiography was unremarkable. Ultrasound (US) showed a well-delineated fusiform lesion at the ulnar side of the carpal tunnel, extending between the third and fourth metacarpal. The lesion was homogeneously hyperechoic with absence of increased intralesional doppler signal (A lipoma is a benign tumour, originating from adipose tissue. Although it is the most common soft tissue tumour in the body, it is rarely seen in the hand . They arCTS usually presents as a bilateral disorder. In case of long-standing unilateral symptoms, secondary causes due to a space-occupying lesion should be suspected. Further work-up with EMG, US, and MRI is needed for detection and characterization of the lesion . Althoug


Abstract.Background: Media reporting may influence suicidalbehavior. In-depth exploration of how self-harm and suicide are portrayed in newspaperarticles in a middle-income country such as Sri Lanka is lacking. Aims:We aimed to explore how self-harm and suicide are portrayed in Sri Lankan printednewspapers. Method: Seven English- and Sinhala-language SriLankan newspapers were screened for articles reporting on self-harm and suicide . A thematic analysis was conducted. Results: In the 78 articles identified for analysis,certain aspects were overemphasized (inappropriate behavior) and others underemphasized. Explanations of self-harm were one-sided and asuicide prevention narrative was lacking. Limitations: Another time-frame and inclusion of Tamilnewspapers as well as social media and online publications would provide additionalunderstanding. Conclusion: The study found an indication ofsimplistic reporting. Greater focus on prevention and a nuanced portrayal of self-harmcould reduce stigma and imitative behavior. Thistime was chosen owing to the availability of staff resources. Articles and commentaries wereincluded when they reported on self-harm and/or suicide and excluded when: self-harm orsuicide was not mentioned; the theme was suicide bombing; the text focused on children; andwhen content was fictional. MW hand-searched the Sinhala newspapers and MWA the English onesfor reports of self-harm and suicide. Sinhala articles were translated into English by anEnglish literature graduate student. Individuals, whose cases of self-harm were portrayed innewspapers are presented with pseudonyms to safeguard anonymity.intertextuality oremerging discourses across articles, where the developments in the same story are portrayedover time, and themes not included in the texts, since meaning iscommunicated as much by what is excluded as included , primarily from the Sinhala-languagenewspapers Divaina and Lankadeepa and the English-languagenewspaper Ceylon Today. Of all articles, 73 reported on specific episodes ofself-harm/suicide : 47% reported on suicide deaths; 17% onnonfatal self-harm; 18% debated whether it was self-harm, crime, or an accident; and 4%reported about nonfatal self-harm and suicide in the same report. One article reported onsuicidal ideation. Five articles did not report on a specific case of self-harm or suicide.Six articles reported on suicides outside of Sri Lanka. The selection process is described inCeylon Today. Four of the commentaries focused on explanations ofself-harm/suicide, emphasizing mental illness, social and cultural factors; one focused onmeans of self-harm and another educated about how suicide is not a legal defense in SriLanka. All articles reporting on self-harm/suicide in Sri Lanka referred to Sinhalaindividuals except for two articles describing self-harm by a Tamil individual. Ceylon Today. Here, large colorful drawings of means used forself-harm/suicide were presented alongside the text. For instance, an article headlined "ALove That Took the Life of a Soldier" included a drawing of a soldier holding a gun to hishead (ENG1). These articles were highly evocative, in a style reminiscent of fiction. Whilethey portrayed a rural, poor individual in a difficult situation, the newspaper's audiencewould likely be located in urban areas. Other articles included photographs of the deceasedand/or the location of the self-harm. In two of the Sinhala articles, a photograph of apolice-person who had played a role in the case was included.Photographs or drawings were included in 26% ofarticles, especially in the short-story-styled feature articles in the English-languageA reoccurring theme was provisions ofpersonality traits of the individuals who self-harmed. Evaluative adjectives and verbsdescribed the individuals before their self-harm. Often this included positive evaluations: Nipuni's mother has said that she was areally good daughter-in-law. (SIN39) Tharindu, as an employer was a man ofnobility and gentleness. He paid the salaries of his employees on time. (ENG2) These articles memorialized the victimwhile expressing disbelief about the situation. Although evoking feelings of empathy towardthe individual, these articles also questioned how self-harm could happen to individuals whoplayed their social role appropriately. For example, one article stated that:He is a very social, friendly and helpfulperson and he did not have a reason to commit [sic] suicide. (SIN17)In other cases, the individuals'character was portrayed in negative terms, evoking feelings of disdain: Their [individuals who self-harm]personality is weak. (SIN28)She has never undergone any hardships. Shedoes not even know what suffering or sadness mean because she led a luxurious life thanks toher father's money (…). (SIN7)In these quotes, stigma surroundingindividuals who self-harm was evident. Not only was their personality questioned, but alsotheir reasons to self-harm. Several articles commented on the individual's children. Forinstance, a husband reflected that his wife had no reason to self-harm because of her lovefor their children. Another article highlighted how a woman should not be blamed for herself-harm although she had children:She can't be viewed as a mother who has nolove toward her children, but one that loved them more than her life and when she had failedto give them a contented life. The ignorant woman thought by ending her life [she] would endher troubles. (ENG4)Here, the woman's behavior was excused byhighlighting her ignorance while communicating how it was linked to a lack of knowledge and apaternalistic attitude of condescension. A woman's love for her children thus appeared toplay a part in how her self-harm was perceived. A conflict between close relations was atthe center of most self-harm/suicide cases presented. Suicidal individuals were often definedthrough their relationship with others and their actions, for example, a daughter's affair ora wife's disobedience. In some instances, the others in a relationship were portrayed ashaving no influence over the suicidal individual's actions:Samadhi, on the other hand, could donothing for her husband as she had already helped him in many ways. She, as usual, engagedin household chores. (ENG2)In other instances, the relatives wereblamed for the self-harm, thereby placing responsibility for the event with someone else: His wife showed little relaxed attitudeover some affairs of life some of which were of apparent contrast to her husband's. Shecannot be called a disobedient wife, but had a habit of standing firm on her decisionregardless of her husband's stand on issues. (ENG5) In this man's instance, his suicide wasexplained to be rooted in a conflict over his wife's actions – she did not follow his advicein avoiding involvement in political matters. Often the personality traits of theindividual who self-harmed and their relatives were linked to gender. Men were typicallydescribed as hardworking: He worked hard (…) and was known in thearea as a man. (ENG5) An in-depth article describing theheartache and suicide of a soldier highlighted how: Shehan burst into tears in secret to hisfellow soldiers. He knew that if anybody would see him cry, it would be a grave damage tohis bravery as a soldier who should possess a heart which can bear any agony. (ENG1) These newspaper articles communicated amasculine behavior, where men were supposed to be self-controlled and hard-working. Women, onthe other hand, were often described as disobedient and inactive: His wife displayed her idleness. (ENG5)In other instances, articles highlightedhow women were able to bear up under problems: She tolerated everything because of herdaughters (SIN28) An evaluation of the individual'sbehavior, therefore, played a pivotal role in portraying the cases of self-harm and suicide.n =3, English n = 3) from the act of self-harm, hospitalization, and death.Another article featured how an astrologer had "vowed to carry out an act of self-immolation"if the sitting president was not re-elected . In both cases, self-harm/suicideseemed to demonstrate devotion to the politician. Six episodes of self-harm/suicide wereportrayed in several newspaper articles. Often the case was related to political issues,specifically the presidential election. One article depicted a man who seemingly self-harmedowing to his wife's political engagement. Two running stories portrayed the defeat of thesitting president. One reported how a man self-harmed as a result of distress over theelection results, following his story through several articles Initial investigations revealed that thedeterioration of his mental health due to his illness triggered the incidence. (ENG16) A person has committed [sic] suicide forbeing afraid to go to court. (SIN30) Except for two commentaries by healthprofessionals focusing on the complexities of self-harm, a single, unsubstantiated cause wascommunicated in the articles. Only three of the included articles mentioned alcohol as afactor. None provided information about help-seeking. To the best of our knowledge, this is thefirst in-depth exploration of Sri Lankan newspaper articles' portrayal of self-harm andsuicide. The analysis found four relevant aspects of reporting in the selected articles: •Individuals who self-harmed were judgedaccording to their own or a close relations' behavior. •Behavior was often linked to theindividual's gender.•Explanations for cases of self-harm wereone-sided. •Suicide prevention narratives were absent.In the selected newspaper articles,reoccurring themes were individual behavior and relational breakdown. While some articlesexpressed judgment toward the individual's inappropriate behavior, others communicated shockwhen the individual had stayed within the boundaries of acceptable behavior, questioning howsomeone who played their social role appropriately would self-harm. Such norms stem from asocial structure where restraint is valued and expression of emotions suppressed . There wIn addition to individual behavior, relationshipbreakdown was a reoccurring explanation for self-harm in the included articles. This haspreviously been found in Sri Lankan self-harm literature where several studies havehighlighted how a purpose of self-harm is to communicate something that cannot be said inwords, typically to close relations . TheimpThe international literature on suicidestrongly emphasize how self-harm is rarely the result of a single factor . StudiesThe newspaper-portrayal of self-harm likelyinfluenced public perceptions of self-harm by over- and under-emphasizing certain aspects. Anissue rarely mentioned in the articles was alcohol consumption, which is similar to a contentanalysis of newspapers in India where few articles mentioned the link between alcohol andsuicide . HoweverMigration was continuously brought up in relationto self-harm. Presenting an opportunity for income generation , women iA striking finding was that the majorityof articles included no suicide prevention narrative, consistent with other research from theregion . NewspapThis study illustrates how a publicdiscourse on self-harm is reflected in newspaper reporting. When self-harm is simplified andstereotyped, there is a danger for imitation and that individuals who self-harm feelstigmatized, while leaving larger structural issues ignored. A next step is to engage mediaprofessionals in a dialogue about sensible reporting, which may result in guidelinessensitive to the Sri Lankan context, as exemplified in other parts of the world . Although inspired by CDA, we did notconduct a full discourse analysis . We analThis study explored the portrayal ofself-harm and suicide in Sri Lankan newspapers. Certain aspects were over- andunder-emphasized. Cases were portrayed simplistically and individuals who self-harmed werejudged according to their own or a close relation's behavior. The newspapers contributed witha discourse about self-harm that lacked visibility about the complexities of self-harm. Therewas a striking absence of a suicide prevention narrative. The findings are vital in informingfuture guideline development and communication strategies with media professionals.