Autophagy is a mechanism that enables cells to maintain cellular homeostasis by removing damaged materials and mobilizing energy reserves in conditions of starvation. Although nutrient availability strongly impacts the process of autophagy, the specific metabolites that regulate autophagic responses have not yet been determined. Recent results indicate that S‐adenosylmethionine (SAM) represents a critical inhibitor of methionine starvation–induced autophagy. SAM is primarily involved in four key metabolic pathways: transmethylation, transsulphuration, polyamine synthesis and 5′‐deoxyadenosyl 5′‐radical–mediated biochemical transformations. SAM is the sole methyl group donor involved in the methylation of DNA, RNA and histones, modulating the autophagic process by mediating epigenetic effects. Moreover, the metabolites of SAM, such as homocysteine, glutathione, decarboxylated SAM and spermidine, also exert important influences on the regulation of autophagy. From our perspective, nuclear‐cytosolic SAM is a conserved metabolic inhibitor that connects cellular metabolic status and the regulation of autophagy. In the future, SAM might be a new target of autophagy regulators and be widely used in the treatment of various diseases. 6‐methyladenosine RNA methylation, m6A RNA methylation Working model for SAM‐mediated modulation of autophagy. SAM is an essential metabolite that acts as a high‐energy methyl donor for most methylation modifications of non‐histone, histone, DNA and RNA, which epigenetically affect autophagic flux. Moreover, SAM is the biosynthetic precursor for HCY and cysteine and other sulphur‐containing metabolites such as GSH. All of these roles significantly contribute to the modulation of autophagy. In addition, the SAM metabolite SPD has also been shown to be a physiological inducer of autophagy. Collectively, the metabolites of SAM and the key enzymes in SAM biosynthesis and metabolism influence the core autophagy machinery. S‐adenosylmethionine: SAM, homocysteine: HCY, glutathione: GSH, spermidine: SPD, AMP‐dependent protein kinase: AMPK, mechanistic target of rapamycin: mTOR, migration inhibitory factor: MIF, cystic fibrosis transmembrane conductance regulator: CFTR, mTOR complex 1: mTORC1, protein phosphatase 2A: PP2A, nitrogen permease regulating protein: NPR2P, ten‐eleven translocation methylcytosine dioxygenases: TET, N It was first studied as a cellular response to starvation. However, autophagy is now a key regulator of cell metabolism, growth control, the balance between cell survival and cell death, and ageing.S‐adenosylmethionine (SAM) is a significant biological sulphonium compound that participates in a variety of biochemical processes. SAM is biosynthesized through the reaction of methionine with ATP that is catalysed by SAM synthetase or methionine adenosyltransferase (MAT). The reaction mainly occurs in cytosolic pools via the one‐carbon metabolism pathway that encompasses both the folate and methionine cycles.22.1S‐adenosylmethionine can be synthesized in the cytoplasm of every cell, especially hepatocytes.2.2S‐adenosylmethionine acts as a link between four key metabolic pathways, transmethylation, transsulphuration, polyamine synthesis and 5′‐deoxyadenosine 5′‐radical–mediated biochemical transformation Figure , influen2.2.1The most prominent metabolic function of SAM is its role as a methyl group donor in a variety of reactions catalysed by enzymes from the methyltransferase family.2.2.24) and hydrogen sulphide (H2S), whereas α‐ketobutyrate is converted to succinyl‐CoA, and this reaction mainly occurs in the mitochondria. SAM is a significant precursor of GSH, and the transsulphuration pathway is especially active in liver.The transsulphuration pathway is another crucial catabolism process that links SAM to cysteine biosynthesis. Cysteine is generated from HCY via the transsulphuration reaction, and this process principally occurs in liver and lens.2.2.3S‐adenosylmethionine also participates in the synthesis of polyamines. Spermidine (SPD) and spermine (SPM) are the primary polyamines in mammalian cells. SPD and SPM have crucial effects on senescence, immunity and cancer.2.2.4Under anaerobic conditions, SAM also induces novel radical chemical reactions.3Autophagy involves the lysosomal degradation pathway, which removes damaged and potentially harmful substances and replenishes energy reserves under starvation conditions to maintain cellular homeostasis. Although the regulation of autophagy is strongly dependent on the availability of nutrients, there are few descriptions of the specific metabolites that regulate autophagy. Recently, SAMTOR was found to link methionine and one‐carbon metabolism to mTORC1 signalling, thereby acting as a SAM sensor.46‐methyladenosine (m6A) RNA methylation, the most abundant modification found in messenger RNA (mRNA) in eukaryotic cells, is also reported to play roles in autophagy regulation.Methylation is a burgeoning post‐transcriptional and epigenetic modification research topic within autophagy research. It has been documented that protein methylation, including histone methylation and non‐histone methylation, is one of the post‐transcriptional and epigenetic modifications that regulates autophagy.4.1Previous studies have shown that SAM production inhibits non‐nitrogen starvation (NNS)–induced autophagy through the activation of the methyltransferase PPM1P, which modifies the catalytic subunit of protein phosphatase 2A (PP2A).4.2S‐adenosylmethionine can regulate the histone methylation state as a metabolic coenzyme, which is linked to autophagy control. G9A is a methyltransferase catalysing monomethylation and dimethylation of H3K9 (H3K9me1 and H3K9me2) in chromatin.4.3Among epigenetic modifications, DNA methylation is perhaps the best studied and the most common.4.46A modification consists of the methyltransferase complex, demethylases and RNA‐binding proteins, which adds another critical layer of epigenetic regulation to various cellular processes.6 atom of adenosine mRNA bases.6A modifications acting as RNA demethylases.6A‐binding domain and preferentially recognize m6A in a methylation‐dependent manner.6A modification in autophagy was reported. Lin et al6A level suppression promotes sorafenib resistance in liver cancer by activating the FOXO3‐mediated autophagy signalling pathway. Song et alet al6A modification of ULK1 transcripts, thus inhibiting mRNA degradation and upregulating the abundance of autophagy‐related protein ULK1 (unc‐51 like kinase 1), enhancing autophagy in HeLa cells. Wang et al6A‐dependent and YTHDF2‐mediated manner. Moreover, m6A modifications of RNA during the regulation of autophagy are also found in the leucocytes of chronic kidney disease patients, bone‐derived mesenchymal stem cells and Leydig cells.6A methylation machinery in autophagy. SAM, as a methyl donor, may be a regulator of autophagy through its targeting of the RNA m6A modification.RNA m5S‐adenosylmethionine is more than just a methyl donor. It is the biosynthetic precursor for HCY and cysteine and other sulphur‐containing metabolites such as GSH. All of these roles significantly contribute to the modulation of autophagy.HCY, a sulphhydryl‐containing amino acid, is an intermediate metabolite in methionine metabolism and is derived from SAM. SAM supplementation decreased HCY levels in diet‐induced hyperhomocysteinaemia in mouse plasma and brain.C elegans, which results in a decrease in ATP content and an increase in autophagy.Furthermore, HCY also transforms into cysteine and α‐ketobutyrate by the transsulphuration pathway. Cysteine can be converted to various sulphur‐containing molecules, such as GSH, an important cellular antioxidant and free radical scavenger. SAM treatment in patients with cirrhosis leads to recovery of liver GSH levels.6S‐adenosylmethionine is converted to dcSAM by the enzyme adenosylmethionine decarboxylase. dcSAM is transformed into the polyamines SPM and SPD by donating an aminopropyl group. SPD and SPM are essential polycations that are involved in a wide variety of cellular processes, such as the regulation of chromatin structure, gene transcription and translation, DNA stabilization, signal transduction, cell growth and proliferation.7The connection between SAM and autophagy was described above. As an important metabolite in the body, SAM plays an essential role in cell growth, apoptosis, death and differentiation.7.1Many experimental models and clinical trials have demonstrated the therapeutic effects of SAM in acute liver injury and liver fibrosis.7.2et al showed that suppression of autophagy shifts T cells to a glycolytic phenotype and causes a reduction in SAM, which results in a global loss of H3K27me3 and concomitant gains in H3K4me3. As a consequence, autophagy‐deficient T cells transcriptionally reprogramme immune response genes to an effector memory state and enhance CD8+ T cell–mediated rejection of tumours.et alEmerging evidence from cancer immunotherapy clinical trials has highlighted an important role for T cells in mediating the elimination of tumours. While the results of immunotherapies have been encouraging in the context of haematological cancers and more recently in melanoma, targeting other solid cancers has been largely unsuccessful. Several factors, including metabolic competition in the tumour microenvironment, could suppress T‐cell function following infusion.7.3C elegans.Metformin is an oral anti‐hyperglycaemic agent for type 2 diabetes (T2D) that promotes health through the modulation of epigenomic metabolism. Metformin has recently been shown to promote global DNA methylation in non‐cancerous, cancer‐prone and metastatic cancer cells through positively modulating the activity of AHCY (the SAH hydrolase) in an AMPK‐dependent manner and promoting the accumulation of SAM.7.4+) CD4+ T cells, CD8+ T cells and NK cells and an increase in tumour‐killing M1 macrophages in the tumour microenvironment of an ovarian cancer mouse model.Difluoromethylornithine (DFMO), an inhibitor of ODC, antagonizes polyamine synthesis, resulting in a huge increase in dcSAM, leading to dcSAM levels that are 3‐fold 4‐fold greater than SAM levels.8In summary, SAM is an important intermediate of the methionine cycle and primarily participates in four key metabolic pathways: transmethylation, transsulphuration, polyamine synthesis and 5′‐deoxyadenosyl 5′‐radical–mediated biochemical transformations. SAM can be synthesized in all cells through methionine metabolism. The most important function of SAM is to be the primary methyl donor involved in epigenetic regulation. In addition, SAM is also a precursor of (a) GSH, the main endogenous antioxidant; (b) polyamines, which participate in the modulation of transcription, translation, cell growth and apoptosis; and (c) 5′‐deoxyadenosyl 5′‐radical, which initiates a radical reaction. The metabolites of SAM and the key enzymes in SAM biosynthesis and metabolism influence the core autophagy machinery. The levels of SAM modulate autophagy accompanied by targeting the AMPK/mTOR pathway and regulating methyltransferase activity. SAM metabolism has received increasing attention from autophagy researchers and has provided new insights into the importance of SAM levels for autophagy regulation, thereby opening new avenues for autophagic control. Moreover, SAM treatment has been indicated that to inhibit autophagy and suppress the secretion of inflammatory cytokines, playing a role in ameliorating inflammatory cancers. There is evidence that some therapeutic strategies work against tumour cells by modulating the levels of SAM, GSH or dcSAM and then stimulating autophagy. These therapeutic methods could also be combined with other treatments, such as chemotherapy and immunotherapy, and may provide a new effective therapeutic method to improve prognosis. However, new clinical trials are necessary to unambiguously establish the benefit of these methods in tumour therapy. Future research may investigate how the formation of the transcriptional complex and epigenetic alterations mediate the relationships among SAM accumulation, hypermethylation of DNA and histones, and autophagy repression. The therapeutic potential of SAM in tumours and other diseases should also be investigated.No potential conflicts of interest were disclosed.Q. Wu, Y. Ouyang and SR. Sun conceived and designed the study. S. Sun, Y. Ouyang and Q. Wu. contributed to acquisition of data. Y. Ouyang, Q. Wu and JJ Li analysed and interpreted the data. Y. Ouyang, Q. Wu and SR. Sun wrote, reviewed and revised the manuscript.


To study the survival prediction value of lymph node ratio (LNR) and preoperative thyroglobulin (Tg) in the prognosis of thyroid papillary carcinoma (PTC).A total of 495 patients with PTC and lymph node metastasis treated at the Cancer Hospital of Xinjiang Medical University were selected for a retrospective study. The disease‐free survival (DFS) of patients was the follow‐up endpoint. DFS was calculated for all patients. The Cox proportional risk regression model and nomogram were used to predict the survival prognosis of PTC with lymph node metastasis by index. LNR and preoperative Tg level cutoff values were obtained using ROC curves. To express DFS, Kaplan–Meier survival curves were created. Using 3‐ and 5‐year calibration curves and AUC values, the prognostic models' precision and discrimination were assessed. Clinical decision curve analysis was used to forecast clinical benefitability. Finally, the results were validated using internal cross‐validation.p < 0.05) were independent risk factors affecting the prognosis of PTC with lymph node metastasis. Kaplan–Meier curves showed higher DFS rates in the group with low NLNM (<10), LNR (<0.295), and preoperative Tg level (<50.24) groups. The 3‐year and 5‐year calibration curves showed good agreement. A ROC curve analysis was performed on the nomogram model, and its AUC values at 3 and 5 years were, respectively, 0.805 and 0.793. Clinical decision curves indicate good clinical benefit. Finally, internal cross‐validation demonstrated the legitimacy of the prognostic model.The cutoff values of LNR and preoperative Tg level were 0.295 and 50.24, respectively, and they were divided into two groups according to the cutoff values. Multifactorial Cox regression models showed that NLNM, LNR, and preoperative Tg level (all The LNR and preoperative Tg levels, in combination with other independent factors, were effective in predicting the survival prognosis for patients with PTC. The value of lymph node ratio and preoperative thyroglobulin levels in predicting disease free survival in papillary thyroid cancer. The global incidence is three times higher in women than in men.In patients with PTC, regional metastases in the lymph nodes play an important role in predicting tumor recurrence. The lymph node ratio (LNR), which is calculated, is the number of positive lymph nodes removed from the total number of lymph nodes removed. Several studies have shown that the LNR improves the accuracy of recurrence prediction and has also been shown to be a reliable predictor of survival and recurrence in head and neck, pancreatic, gastric, biliary, breast, colorectal, and uterine tumor types.Thyroglobulin (Tg) is a large glycoprotein secreted by thyroid follicular epithelial cells, most of which is synthesized by thyroid cells and released into the residual lumen of the thyroid follicle. It is a tumor marker for PTC and an important reference for the follow‐up of patients with PTC after treatment. In recent years, more studies have shown the role of preoperative Tg in lymph node metastasis and distant metastasisThe previous study used only LNR alone to determine the prognostic relationship with PTC; the present study evaluated performed the combination of preoperative Tg and LNR to predict the prognosis of patients. Therefore, this study attempts to construct a predictive nomogram with the LNR and preoperative serum Tg level to improve the accuracy of treatment and follow‐up plans for patients with lymph node metastases of PTC, providing a valid reference tool.2From January 2010 to December 2020, 495 patients with pathologically confirmed PTC who underwent radical surgery at the Affiliated Cancer Hospital of Xinjiang Medical University were selected for a retrospective study. All patients were staged pathologically according to the 8th edition of TNM staging jointly developed by the American Joint Committee on Cancer (AJCC) and the Union for International Cancer Control (UICC). Inclusion criteria are: (1) PTC with histopathologically confirmed lymph node metastases from January 2010 to December 2020; (2) no preoperative chemotherapy and/or radiotherapy; and (3) serum Tg was collected in the morning 3 days before surgery. Exclusion criteria: (1) previous or concurrent other malignancies; or (2) missing clinical information and data. The last follow‐up date was December 31, 2021. Recurrence was identified based on the following criteria: (1) serologically suppressive Tg ≥1 μg/L or irritant Tg ≥10 μg/L or an increasing trend in Tg antibody (TgAb); (2) imaging verifiable evidence of the presence of structural or functional disease; and (3) recurrence followed by surgery and histopathological confirmation.2.1p < 0.05 were statistically significant. SPSS 26.0 software and R (foreign), , and (rms) software packages were used for statistical analysis.Optimal cutoff values for continuous variables were obtained from subject characteristic curves (ROCs). Rates were compared between groups using the chi‐square test. The Spearman correlation coefficient indicated the correlation between study indicators and pathological parameters. Cox regression analysis models were used to perform one‐way and multifactorial analyses to obtain independent risk factors affecting survival; Cox regression coefficients were used to generate nomograms. Kaplan–Meier survival curves were made to pinpoint disease‐free survival (DFS). Accuracy and non‐specificity were expressed using calibration curves and AUC values; clinical benefit was expressed by clinical decision curve analysis; and finally, internal validation was performed. All analyses with 33.1The ROC curve was used to find out whether the LNR and preoperative Tg levels had appropriate thresholds to predict the patient's final outcome of recurrence or death, which suggested that these indicators may be significant for prognosis. The best critical value for predicting DFS for LNR was 0.295, which was associated with the best sensitivity (0.734) and specificity (0.552). The best critical value for predicting preoperative Tg levels for DFS was 50.24, which was associated with the best sensitivity (0.672) and specificity (0.770) Figure .3.2Among the clinicopathological characteristics of the selected patients Table , the nump = 0.004) and NLNM (p < 0.001). Preoperative Tg levels were significantly correlated with maximum tumor dimension (MTD) (p < 0.001), maximum diameter of lymph node metastasis (MDLNM) (p = 0.002), NLNM (p < 0.001), HT (p < 0.001), T stage (p < 0.001), and N stage (p < 0.001).Based on the correlation between the selected study indicators and pathological parameters Table , the LNR3.3p < 0.001), MDLNM (p = 0.001), NLNM (p < 0.001), LNR (p < 0.001), pathological stage (p = 0.024), T stage (p < 0.001), N stage (p = 0.006) and preoperative Tg level (p < 0.001) were prognostic factors for PTC with lymph node metastasis , LNR (p = 0.004) and preoperative Tg level (p < 0.001) were independent risk factors affecting the prognosis of PTC with lymph node metastasis , MDLNM, NLM, LNR, pathological stage, T stage, N stage, HT and preoperative Tg level. MTD  was an independent influencing factor for PTC. This finding is in line with most studies. In a study of PTC in children and adolescents,This study found that the preoperative Tg level was closely correlated with MTD, MDLNM, NLNM, HT, T stage, and N stage. Moreover, this study found that the preoperative Tg level independently influenced the prognosis of PTC. Therefore, we should pay more attention to the preoperative high Tg level of PTC, which can provide a reference for the prognosis of patients.Therefore, we attempted to construct a nomogram based on LNR, preoperative Tg levels, and clinicopathological parameters to predict prognosis. The calibration curves for 3‐ and 5‐year survival demonstrated the consistency of the predicted probabilities with the actual probabilities, and the clinical decision curves demonstrated the predictive potential of the nomogram. Moreover, internal validation was conducted.Our model has several limitations. First, this study is a review, and in spite of the fact that it examined a large sample, LNR are influenced by the management of clinical surgical scopes and the number of lymph nodes recovered, which may significantly affect the outcome of LNR, and our pathologists only retrospectively analyzed the pathology reports. Second, we did not perform a study to differentiate between N1a and N1b subdivisional LNR. In addition, the number and dose of radioiodine already used postoperatively were not included in the analysis in this study. Finally, our study has only been internally validated, and multi‐institutional, multicenter, and more forward‐looking studies are needed to validate our results.Peng Guo: Conceptualization (lead); data curation (lead); methodology (lead); writing – original draft . Xinhua Wang: Conceptualization (lead); formal analysis ; methodology ; writing – original draft (lead). Luhua Xia: Data curation ; methodology ; writing – original draft . Nadiremu Shawureding: Data curation ; methodology ; writing – original draft . Zhiheng Hu: Data curation ; methodology ; writing – original draft .No funding.None declared.The study was approved by Ethics Committee of the Cancer Hospital Affiliated to Xinjiang Medical University. All individuals participating in the study signed an informed consent form.


To investigate microstructural impairments of corticospinal tracts (CSTs) with different origins in amyotrophic lateral sclerosis (ALS) using neurite orientation dispersion and density imaging (NODDI).Diffusion‐weighted imaging data acquired from 39 patients with ALS and 50 controls were used to estimate NODDI and diffusion tensor imaging (DTI) models. Fine maps of CST subfibers originating from the primary motor area (M1), premotor cortex, primary sensory area, and supplementary motor area (SMA) were segmented. NODDI metrics (neurite density index [NDI] and orientation dispersion index [ODI]) and DTI metrics  were computed.The patients with ALS showed microstructural impairments  in CST subfibers, especially in M1 fibers, which correlated with disease severity. Compared with other diffusion metrics, NDI yielded a higher effect size and detected the greatest extent of CST subfibers damage. Logistic regression analyses based on NDI in M1 subfiber yielded the best diagnostic performance compared with other subfibers and the whole CST.Microstructural impairment of CST subfibers  is the key feature of ALS. The combination of NODDI and CST subfibers analysis may improve diagnosing performance for ALS. Microstructural impairment of CST subfibers  is the key feature of ALS. The combination of NODDI and CST subfibers analysis may improve diagnosing performance for ALS. Its pathological hallmarks are combined degeneration of the corticospinal tract (CST) and anterior horn cells, resulting in a decline in motor functions.Magnetic resonance imaging (MRI) is considered the leading tool for biomarker identification in ALS.However, confidence in using the conventional DTI technique to complete the task of detecting WM impairment is limited because of low‐spatial resolution and an inability to represent the crossing of multiple fibers.Although existing diffusion MRI (dMRI) studies based on various models  have consistently reported that one of the classic WM pathologies of ALS is CST degeneration,In the abovementioned context, it is hypothesized that ALS, another disorder characterized by motor dysfunction and CST degeneration, may induce varying degrees of impairment in CST subfibers. In previous studies, researchers have found that there are impairments in CST in ALS patients,22.1We obtained approval for this study from the Research Ethics Committee of Fujian Medical University Union Hospital, China; and all study subjects provided written informed consent before enrollment. A total of 39 sporadic patients with ALS and 50 healthy controls (HC) were enroled in this study. The El Escorial criteria2.2b values = 1000, 2000, and 3000 s/mm2, with 30, 30, and 30 unique gradient directions, respectively. Six b value = 0 images were also acquired. The other parameters of interest were: repetition time = 4200 ms, echo time = 72 ms, number of averages = 1, slice thickness = 2 mm, the field of view = 216 mm × 216 mm, matrix = 108 × 108, voxel size = 2 mm × 2 mm × 2 mm, flip angle = 90°, 72 axial slices without gap, anterior‐to‐posterior phase direction, and multiband factor = 2. We also collected T1‐weighted (T1W) three‐dimensional magnetization‐prepared rapid gradient echo sagittal images using the following parameters: repetition time = 1610 ms, echo time = 2.25 ms, inversion time = 900 ms, field of view = 224 mm × 224 mm, matrix = 224 × 224, flip angle = 8°, slice thickness = 1.0 mm, and slice number = 176.We used a 3 T MRI scanner  to obtain images. dMRI data were obtained using a multi‐shell spin‐echo echo‐planar imaging sequence, using 2.3https://fsl.fmrib.ox.ac.uk), SPM12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12), and MDT v1.2.6 (https://github.com/robbert‐harms/MDT). First, the b0 dMRI image was skull‐stripped to generate the brain mask. Then, the dMRI images underwent eddy current and head motion corrections. Next, DTI and NODDI models were estimated based on the corrected dMRI data within the brain mask. For DTI modeling, a standard linear regression model was used to fit the tensor using the “dtifit” function in FSL, and several metrics were calculated based on the tensor, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Meanwhile, for NODDI modeling, a Powell conjugate‐direction algorithmb0 image, segmented, and non‐linearly normalized into the Montreal Neurological Institute (MNI) space using a Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) algorithm.3.The preprocess and modeling of dMRI data were carried out using FSL6.0 (2.4http://www.humanconnectomeproject.org/).In terms of anatomy, the origins of CST fibers include four cortical areas, namely, M1, PMC, SMA, and S1.2.5p < 0.05 with Bonferroni correction). Cohen's D value was calculated to quantify the effect size (ES) for each comparison. Besides, to identify the specific location of the changed diffusion metric in each fiber, we also carried out voxel‐wise intergroup comparisons using GLM while controlling for age, sex, and year of education . The percentage of voxels with diffusion metric abnormalities was quantified for each fiber. The Spearman's correlation was used to examine the association between NODDI metrics of each CST subfiber and disease severity as reflected by the ALSFRS‐R score (p < 0.05 with Bonferroni correction). Finally, to evaluate the potential of the NODDI metrics of CST subfibers in diagnosing ALS, we carried out logistic regression with NDI values of each CST subfiber (and their combination) as features and group ID  as labels. The area under the receiver operating characteristic (ROC) curve (AUC) was used to estimate the predicted performance, and a paired Delong's test was used to compare the AUC values between each pair of models .The Kolmogorov–Smirnov test was applied to examine the normality. Then, we used generalized linear modeling (GLM) to compare inter‐group differences in averaged diffusion metrics for each ROI of CST fiber, adopting age, sex, and year of education as the nuisance covariates ; however, patients with ALS only exhibited a tiny decrease in ODI in left PMC fiber (averaged ES across fibers = 0.045 ± 0.026). In addition, FA reductions in several CST subfibers  and bilateral whole CSTs were observed in patients with ALS (averaged ES across fibers = 0.097 ± 0.065). MD (averaged ES across fibers = 0.226 ± 0.080), AD (averaged ES across fibers = 0.108 ± 0.036), and RD (averaged ES across fibers = 0.191 ± 0.071) increases in the majority of CST subfibers and bilateral whole CSTs were also found in patients with ALS, although they displayed relatively lower ES values compared with the NDI.The between‐group differences in the diffusion metrics among the CST subfibers are depicted in Figure Among the CST subfibers, bilateral M1 and SMA subfibers had a greater effect size of microstructural changes for NDI  relative to other CST subfibers and bilateral whole CSTs. A similar pattern was also observed for DTI metrics . In addition, across the diffusion metrics, the average ES in bilateral M1 fibers (ES = 0.210 ± 0.118) was larger than that in SMA fibers (ES = 0.190 ± 0.110), PMC fibers (ES = 0.130 ± 0.070), S1 fibers (ES = 0.080 ± 0.050), and whole CSTs (ES = 0.170 ± 0.090), further indicating that the diffusion measurement of M1 fibers was much more sensitive in detecting microstructural abnormalities compared with that of other subfibers and the whole CST.3.2p < 0.05, voxel‐wise FWE correction). Along each of the seven CST subfibers , the voxel‐wise analysis for NDI revealed the greatest extent of between‐group difference compared with the analyses for the other diffusion metrics . Meanwhile, a lesser extent of ODI difference was observed along bilateral PMC, S1, and SMA fibers. In addition, the DTI parameter differences  were found among the seven CST subfibers, including bilateral M1, PMC, and S1 fibers and left SMA fiber; but the extent was smaller than that seen with the NDI difference.The voxel‐wise differences in distinct diffusion parameters among the CST subfibers are presented in Figures Figure 3.3p < 0.05, Bonferroni correction; Figure The mean NDI value in bilateral M1 fibers was significantly positively correlated with the ALSFRS‐R score (3.4p < 0.001) followed by, in the order, MD , FA , RD , AD , and ODI . A similar pattern was observed along S1 fiber, SMA fiber, and whole CST.Among the majority of CST fibers, the analyses based on NDI showed better classification performance compared with those based on other diffusion metrics Table . SpecifiAmong the distinct CST subfibers, M1 fiber exhibited the more important role in classification analyses because its NDI provided a higher classification accuracy (77.5%) relative to the NDI of other CST subfibers. Moreover, a similar pattern was found for DTI metrics . Of note, the diffusion index along several CST subfibers, especially M1, showed a promising classification accuracy (77.5%), which was higher than that obtained in analyses performed using the diffusion metrics of the whole CST as an index.3.5p < 0.001). Statistically, the NDI of M1 fiber yielded a higher AUC than that of PMC fiber , S1 fiber , and the whole CST . Besides, the NDI of SMA fiber had a higher AUC than that of PMC  and S1  fibers.For each of the CST subfibers, the ROC curve analysis based on the NDI metric exhibited a greater AUC for the diagnosis of ALS compared with that based on other diffusion metrics Table . As Figu4In the current study, we adopted NODDI for the first time to investigate the various degrees of microstructural impairment in CST subfibers with different origins in patients with ALS. Our main findings can be summarized as follows: (1) NODDI provided more sensitive metrics than DTI for detecting CST subfiber impairment; (2) patients with ALS showed microstructural impairments in CST subfibers , which was reflected by NDI, ODI, and FA reductions and MD, AD, and RD increases; (3) M1 fiber microstructural impairment correlates with disease severity in ALS; (4) among CST subfibers, M1 fiber showed the greatest degree of microstructural impairment in ALS; (5) compared with other CST subfibers, M1 fiber exhibited the most important role in identifying ALS; and (6) compared with the whole CST analysis, CST subfiber analysis improved the accuracy for diagnosing ALS. To summarize, the CST subfiber analysis based on NODDI could contribute to identifying more sensitive biomarkers, which would be helpful for diagnosing and assessing disease severity in ALS.Research suggests a neuropathological substrate underlying WM microstructural impairment in ALS is associated with axonal degeneration.In a previous neuroimaging study, a reduction in the volume of blood flow has been found in bilateral SMA fibers in patients with ALS, yet the blood flow remained steady in other CST subfibers,Our findings suggest that NODDI could ensure higher sensitivity and greater tissue specificity than DTI in detecting CST abnormalities, concurring with previous studies in which voxel‐basedSeveral limitations in the current study must be acknowledged. First, the small sample size used in the current study could have limited its statistical power. Second, as reported,In conclusion, the microstructural impairment of CST subfibers  is the key feature of ALS. NODDI can offer greater sensitivity and better tissue specificity over DTI in detecting microstructural impairments among CST subfibers in ALS. The combination of NODDI and CST subfibers analysis may improve diagnosing performance for ALS.This research was supported by grant from the Fujian Provincial Health Technology Project (no. 2022QNA022).The authors declare that they do not have any conflicts of interest.Figure S1Click here for additional data file.Figure S2Click here for additional data file.Figure S3Click here for additional data file.Figure S4Click here for additional data file.Appendix S1Click here for additional data file.


This study aimed to systematically evaluate the impact of omega‐3 fatty acids on the health outcomes of women with breast cancer in electronic databases  for interventional studies. The risk of bias and the quality of the included articles were assessed by Cochrane Collaboration Handbook guidance. The statistical analyses were not conducted because of the heterogeneity of the included studies. Of 3676 identified articles, 11 articles were included in this study. The majority of the included studies were not of high quality. Median progression time and overall survival significantly improved. Additionally, surgical site healing complications and infection rates decreased. There was a significant decrease in perceived stress, sleep disturbance, depression, pain, joint stiffness, and fatigue throughout the intervention. Moreover, omega‐3 fatty acids consumption significantly increased the total serum omega‐3, EPA, and DHA, and decreased the omega‐6: omega‐3 ratio, total leukocytes, lymphocytes, leptin, and CRP, accordingly. Mild gastrointestinal symptoms were reported in only two studies without clinically relevant adverse events. Omega‐3 fatty acids may cause improvement in physical, mental, and some inflammatory and metabolic indices during treatment or posttreatment course of breast cancer patients. Due to the possibility of free radical formation, omega‐3 FAs supplementation and consumption must be done very carefully. This study aimed to evaluate systematically the impact of omega‐3 fatty acids supplementation on the health outcomes of women with breast cancer in the electronic databases for interventional studies. In 2020, about 2.3 million new cases and 658,000 deaths due to BC were recorded  supplementation is considered an adjunctive treatment to reduce toxicity and improve disease outcomes , EPA, and alpha‐linolenic acid (ALA), etc., at any dose, by any route , at any duration and dose interval, solely or in combination with other nutrients, were included in this review. Dietary interventions through consuming foods enriched by omega‐3 FAs sources were also incorporated. There was no age restriction.2.2Review articles, animal studies, cellular and molecular studies, study protocols, cross‐sectional studies, cohorts, case–control, case reports, case series, letters to the editor, and editorials were excluded.2.3A comprehensive systematic literature search was carried out in the electronic databases including Cochrane Central Register of Controlled Trials, Web of Sciences, MEDLINE (PubMed), Scopus, EMBASE, Google Scholar, Science Direct, ProQuest, and Clininaltrial.gov, as well as Magiran, Scientific Information Database (SID), Irandoc, and Iranmedex for Persian literature up to July 2022 using Medical Subject Headings terms and Boolean operators. The keywords alone or in combination with the search for articles including BC and omega‐3 FAs were used Table . The seaIn order to manage the findings of the search by the mentioned strategies, Endnote X8 software (Thomas Reuters) was employed.Two independent review team members (AMH and ShO) independently assessed the eligibility of publications on the efficacy of omega‐3 PUFAs in BC patients by reviewing the title and abstract of identified studies. During the screening of the articles, the disagreements were resolved through discussion with a third reviewer (AFKh). This process continued for the screening of full texts.2.4The three reviewers  extracted the data from all eligible studies. They transferred the necessary information into an electronic form designed for this review consisting of the following sections: authors name and the year of publication, country of study, study design, objectives, evaluated outcomes, the number of participants, intervention and control groups, follow‐up period, study results and possible side effects Tables  and 2.2.5The risk of bias and quality of the included articles were assessed through the guidance of the Cochrane Collaboration Handbook by two members of the review (AFKh & AMH). The following items were assessed: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), and selective reporting (reporting bias). Disagreements were resolved through discussion with a third party (ShO). The bias of the studies was demonstrated using Review Manager 5.3  software.2.6Data were synthesized through qualitative approaches and reported as a systematic review because there were inadequate data for data pooling. Three articles were single‐arm interventional studies. The remaining eight studies had not enough similar outcomes to meta‐analysis.33.1n = 1305), not being a human study (n = 352), being a study protocol (n = 21), investigating other neoplasms (n = 195), and intervention by supplements other than omega‐3 FAs or compounds containing them (n = 31). Although 20 studies were selected for systematic review by surveying the title and abstract of the articles, after reviewing the full texts, 11 articles were eligible and finally included in this systematic review.Details of the search process and article selection, as well as the reasons for article exclusion, are presented in Figure 3.2The selected trials in this systematic review have been published from 2009 to 2022. Of the 11 articles reviewed, 10 articles had full English text  and OS (p = .048) were significantly longer in the intervention than the placebo group. The expression levels of proliferation indices including Ki‐67 and vascular endothelial growth factor (VEGF) significantly decreased after the intervention. Bougnoux et al. (p = .014) and pain at its worst in the last 24 h (p = .011) were reduced on day 30 after the intervention. Regarding lipid profile, triglyceride (TG) was reduced by 7% (p = .011). In the study by Shen et al. (p = .02), BPI average pain (p = .002), and BPI pain interference (p = .009) significantly reduced in week 24 in patients who had body mass index (BMI) ≥ 30 kg/m2 compared to placebo. The global rating of change in joint stiffness also significantly decreased in week 12 (p = .02). High‐density lipoprotein cholesterol (HDL‐c) in week 24 significantly increased in supplemented women with BMI < 30 kg/m2 in comparison with placebo (p = .002). TG in week 12, in patients with BMI < 30 and BMI ≥ 30 kg/m2, significantly decreased compared to placebo (p = .02). In double‐blind RCTs by Paixão et al. (p = .005), EPA (.034), and DHA (p < .001) significantly increased, and omega‐6:omega‐3 ratio (p = .012) decreased in the intervention group compared to the placebo counterparts.Bjørklund  investigx et al.  in theirx et al.  also indn et al. , brief po et al.  and Kleco et al. , total o3/mL, p = .002), lymphocytes , leptin , and adiponectin . de la Rosa Oliva et al. (p = .02). However, serum lipid profile and serum albumin were not different in the intervention compared to the placebo group. Kahlenberg et al. (p = .159). Xu et al. (n = 20) had a significant decrease in perceived stress (p < .05), sleep disturbance (p < .001), depression (p < .001), pain , and fatigue (p < .01) compared to the low omega‐3LC dietary group over the course of intervention  suffering from aromatase inhibitor‐related arthralgia after omega‐3 FAs supplementation (3.3 g/day) for 24 weeks. Darwito et al. (2 and BMI ≥ 30 kg/m2 after omega‐3 supplementation (3.3 g/day for 24 weeks). Additionally, HDL significantly increased in BC patients with BMI ≤ 30 kg/m2. Paixão et al.  for 5 months after chemotherapy resulted in increased survival, improved quality of life, and fewer deaths compared to the placebo group  for 6 months, DelaRosa Oliva et al. (2019) in their study showed an increased level of TG and cholesterol in the mentioned group. This phenomenon may be due to the BC type, differences in ethnicity, chemotherapeutic regimen, or patients' nutritional condition.Increased expression of proliferation indices, including Ki‐67 and VEGF reported by Darwito et al.  after omRegarding the immune system responses, omega‐3 FAs supplementation also resulted in unchanged CD4+ and CD8+ T cells in BC patients without metastasis receiving EPA‐ and DHA‐supplemented FO (2 g/day of FO concentrate containing 1.8 g of omega‐3 fatty acids for 30 days) in Paixão et al.'s  study. I4.1The included studies had some limitations, which necessitate further research regarding the effects of omega‐3 FAs supplementation in BC patients under chemotherapy regimens. Included studies were different in study design, type of BC, chemotherapy regimen, patient's BMI, disease staging, used omega‐3 FAs dose, supplementation duration, and sample sizes. Additionally, ethnic differences were another confounding factor, which may affect the results of the study. Moreover, included studies evaluated different parameters which make it difficult to compare the outcomes of the omega‐3 FAs supplementation in BC. Due to the mentioned limitation, it was impossible conducting a meta‐analysis for this systematic review.Considering the above‐mentioned limitations, further research regarding the effect of omega‐3 FAs on health outcomes in BC patients under chemotherapy is suggested. This review focused on human articles and, as such, it is suggested that a review be made on animal subjects in this field.5This systematic review revealed that omega‐3 FAs alone or accompanied by other supplements lead to improved physical and mental symptoms, as well as some inflammatory and metabolic indices during the treatment or post‐treatment course of BC patients. Mild gastrointestinal symptoms were only reported in two studies without clinically relevant adverse events. We strongly recommend performing more high‐quality RCTs to pool the results and draw more accurate conclusions on the efficacy and safety of omega‐3 FAs supplementation on diverse health outcomes in BC patients under standard medical therapy.Shirin Osouli‐Tabrizi: Conceptualization ; data curation ; formal analysis ; writing – original draft . Amir Mehdizadeh: Conceptualization ; data curation ; formal analysis ; project administration ; writing – original draft . Mina Naghdi: Data curation ; formal analysis ; investigation ; writing – review and editing . Zohreh Sanaat: Data curation ; formal analysis ; investigation ; writing – review and editing . Nafiseh Vahed: Conceptualization ; data curation ; formal analysis ; investigation ; writing – review and editing . Azizeh Farshbafkhalili: Conceptualization ; data curation ; investigation ; methodology ; resources ; validation ; writing – original draft ; writing – review and editing .The authors declare no conflict of interest for this study.No ethical conflicts are involved in this project as this work is a systematic review research of already published data.Data S1.Click here for additional data file.


The time for posttreatment tumor progression differs between nasopharyngeal carcinoma (NPC) patients. Herein, we established effective nomograms for predicting early tumor progression (ETP) and late tumor progression (LTP) in NPC patients.n = 6219; validation cohort: n = 2073). The ELP and LTP were defined as the time to tumor progression ≤24 and >24 months after treatment, respectively.We retrospectively enrolled 8292 NPC patients (training cohort: p < 0.001). Further, we identified 10/6 predictive factors significantly associated with ETP/LTP via logistic regression analyses. These indicators were used separately to construct two predictive nomograms for ETP and LTP. In the training group, the ETP nomogram  and LTP nomogram  were significantly superior for risk stratification than the TNM staging. These results were supported in the validation group with a C‐index value of 0.753 and 0.738 for the ETP and LTP nomograms, respectively. High‐risk patients defined by ETP/LTP nomograms had shorter progression‐free survival than low‐risk patients .The ETP and LTP accounted for 52.6 and 47.4% of the total patient cohort, respectively. Patients who developed ETP had markedly worse overall survival (OS) versus patients who suffered from LTP  and late tumor progression (LEP) risk stratification for NPC patients. The ETP and LTP nomograms demonstrated that the high‐isk group had a shorter progression‐free survival (PFS) than the low‐risk group. Moreover, this study facilitated the prediction of ETP or LTP risk, which might also provide valuable insights into treatment decisions and designing surveillance strategies for NPC after definitive treatment. For example, more than 70% of newly diagnosed NPC cases are concentrated in Southeast and East Asia.After treatment completion, up to 54.8% of tumor progression is observed within 24 months for NPC. Additionally, NPC failure hazard rate varies over time, with a sharp peak at 24 months followed by a decline.Given the poor disease control measures and fatal late complications, salvage treatment of metastatic/recurrent NPC is challenging.Herein, we investigated the prognostic indicators of ETP and LTP after definitive treatment for NPC in an endemic area. Moreover, we constructed more accurate prognostic prediction models by combining TNM staging with clinical and molecular variables. The models were superior for risk stratification than TNM staging and might facilitate pretreatment prediction of ETP and LTP.22.1A big data intelligence platform able to facilitate real‐world NPC research was previously constructed at the Sun Yat‐sen University Cancer Center (SYSUCC).n = 6219) and validation (n = 2073) groups according to a 2:1 ratio using computer‐generated random numbers.Using the big data intelligence platform, we retrospectively extracted data from 10,126 patients who were newly diagnosed and received radical treatment at the SYSUCC from 2011 to 2015. Eligibility criteria included (1) pathologically confirmed, nondisseminated NPC; (2) complete baseline evaluation and pretreatment clinicopathological data; (3) intact follow‐up data; and (4) treated with definitive intensity‐modulated radiation therapy ± chemotherapy. Finally, 8292 NPC patients were enrolled in this research. We randomly assigned eligible patients to training  of head and neck to the collarbone for primary tumor staging, abdominal ultrasonography, skeletal scintigraphy, chest radiography, and enhanced computed tomography (CT). We restaged patients using the 8th edition of the clinical TNM stage. Treatment regimens are summarized in the Data 2.3After treatment, patients needed to attend regular follow‐up examinations once per 3 months for the first 36 months, once per 6 months for the next 24 months, then once per 12 months. Follow‐up visits were conducted to detect possible ETP and LTP. They consisted of detailed medical history evaluation, biochemistry and hematology profiles, complete physical examination, plasma EBV DNA load, electronic nasopharyngoscopy, enhanced computed tomography or chest radiography, abdominal ultrasound or CT, skeletal scintigraphy, and head and neck MRI. Additional PET/CT examinations were recommended when necessary.The endpoint of this study was progression‐free survival . Treatment progression was defined by detecting a newly confirmed distant metastasis or/and locoregional recurrence. Suspected lesions of distant metastasis and locoregional recurrence were confirmed by imaging examination and/or cytological biopsies. The follow‐up time was calculated from treatment initiation to the last follow‐up visit or death. In the current research, locoregional recurrence/metastasis at ≤24 months and >24 months after definitive treatment for NPC were defined as ETP and LTP, respectively.2.4χ2 tests. Using clinically recognized cutoff points, descriptive statistics of continuous variables were converted to categorical variables.p < 0.05 indicated statistical significance.Statistical analyses were conducted using R  or SPSS . Thirteen baseline covariates, which have been reported to contribute to the prognosis of NPC patients, were included in the survival analysis. For categorical variables, differences were compared using Fisher's exact or 2.5The Institutional Review Board and Ethics Committee of our institution approved the current retrospective analysis using anonymous data. They also waived the requirement to obtain written informed consent from patients, given the study's retrospective nature .33.1Among the 8292 patients enrolled, 6219 and 2073 patients were assigned to the training and validation sets. The baseline demographics and clinical characteristics of the two sets are listed in Table 3.2p < 0.001); 28.0 and 63.5% for the validation set (p < 0.001); 26.2 and 59.7% for the entire set (p < 0.001). The Kaplan–Meier analyses revealed that the OS for patients who suffered ETP was worse than for patients who suffered LTP  and 69.9 (95% CI: 66.3–73.5) months in the entire set; 31.7 (95% CI: 29.7–33.7) and 68.6 (95% CI: 64.4–72.8) months in the training set; 32.0 (95% CI: 28.3–37.8) and 74.2 (95% CI: 66.8–81.6) months in the validation set. The 5‐year OS rates of the ETP and LTP groups were 25.7 and 58.5% for the training set  using the Cox‐proportional hazards model. The univariate analyses determined 10 potential predictors of ETP in the training set: T stage, N stage, gender, age, WHO histologic type, CRP, ALB, LDH, EBV DNA load, and cigarette consumption Figure . In the 3.4The independent prognostic factors identified in the multivariate analysis for the training cohort were incorporated into the ETP nomogram to visually quantify the ETP probability Figure . For theFor the training  and validation  sets, the ETP nomogram exhibited a high accuracy for ETP prediction. Similarly, the AUC of the ETP nomogram was 0.711 in the training set and 0.753 in the validation set for ETP prediction. These results indicated the good discrimination ability for prognosis prediction using the ETP nomogram.p < 0.05; Table Further, we compared our models with the eighth TNM stage and 10 single risk factors. For the training and validation sets, the results showed that the C‐index of the ETP nomogram for ETP prediction was 0.711 (95% CI: 0.694–0.729) and 0.753 (95% CI: 0.725–0.780), statistically higher than the C‐index of the eighth clinical TNM stage and single risk factors of T stage, N stage, gender, age, WHO histologic type, CRP, ALB, LDH, EBV DNA load, and cigarette consumption  were superior than the eighth clinical TNM stage and single risk factors for ETP prediction Figure .3.5p < 0.05). With the multivariate analysis adjustment, six covariates  significantly affected LTP (p < 0.05). Detailed results are shown in Figure After excluding 1027 cases who suffered ETP, we included 7265 cases in the univariate and multivariate analyses to determine pretreatment risk factors associated with LTP. In the univariate analysis, eight covariates  were identified as significant prognostic factors for LTP  compared with single risk factors of T stage, N stage, age, LDH, ALB, EBV DNA load, and the eighth clinical TNM stage for LTP prediction Figure .3.7p < 0.001; Figure According to the scores obtained using the ETP nomogram in the training set, patients were categorized into either low‐ or high‐risk (risk score <1.8 or ≥1.8) groups using an X‐tile Figure . A totalp < 0.001).The best cutoff point of the risk score was 3.4 for the LTP‐prediction nomogram. After excluding 1027 patients with ETP, 7265 cases were classified into low‐ or high‐risk groups Figure . A total4Herein, we used a big data intelligence platform‐generated database to evaluate the incidence, risk factors, and long‐term survival outcomes of ETP or LTP after definitive treatment for NPC. We successfully generated and validated two novel nomograms that can be used to predict ETP or LTP in individual NPC patients. Our current data might also provide valuable insights into treatment decisions and designing rational surveillance strategies for NPC patients.In this retrospective research, 23.6% of patients in the 8292 patient cohort had ETP and LTP. Most patients suffered tumor progression within 24 months after treatment. Among the 8292 patients, 12.4%  experienced first tumor progression within 24 months after definitive treatment, and 11.2%  experienced the first tumor progression after 24 months. Additionally, the disease failure patterns differed among patients with ETP and LTP. Recently, Li et al.Thus, differential pretreatment prediction of ETP and LTP might facilitate individualized treatment decisions in a clinical setting and identify rational strategies for monitoring metastasis/recurrence after treatment completion. In our previous research,Data on risk assessments associated with post‐treatment ETP/LTP might also help direct postoperative surveillance strategies. Our current data indicated that the post‐treatment period should be considered to establish an individualized and more cost‐effective surveillance strategy for NPC patients. Individual patient risk for tumor progression was evaluated using the LTP nomogram for the first 2 years after treatment as well as the ETP nomogram. For example, if a patient was evaluated as high‐risk of ETP using the ETP nomogram, this patient should be on a stringent surveillance program during the follow‐up, allowing timely treatment when tumor progression occurs. If a patient did not suffer tumor progression during the first 2 years after treatment and with a high risk of LTP predicted by the LTP nomogram, this patient would need to be on close and stringent recurrence surveillance 2 years after treatment.Finally, our study also has some limitations. First, the nomogram model is classic and has been applied in many studies. Thus, the statistical methods applied in this study were not innovative enough. We choose this statistical method because nomograms are a convenient, easy‐to‐use, and reliable prognosis prediction tool for clinicians. Second, given the retrospective and nonrandomized nature, the current results might be affected by selection bias. Notwithstanding this limitation, a large sample size (up to 8292 cases) was used to minimize the bias generated by the retrospective data collection nature. Third, all data were obtained from a single cancer treatment center, limiting the applicability of our findings. Therefore, large‐scale and multicenter studies on other populations are warranted to corroborate these findings further. We are conducting a multicenter prospective study to confirm our findings in the SYSUCC and Wuzhou Red Cross Hospital, and we look forward to sharing the results in the future.5ETP and LTP are correlated with differential 5‐year OS of NPC patients (26.2% vs. 59.7%). We successfully constructed and validated two nomograms to facilitate ETP and LTP risk stratification for NPC patients. The ETP and LTP nomograms demonstrated that the high‐risk group had a shorter PFS than the low‐risk group. Moreover, this study facilitated the prediction of ETP or LTP risk, which might also provide valuable insights into treatment decisions and designing surveillance strategies for NPC after definitive treatment.Lu‐lu Zhang: Conceptualization ; funding acquisition ; methodology ; writing – original draft ; writing – review and editing . Weihong Zheng: Data curation ; formal analysis ; investigation ; methodology ; writing – original draft . Wei‐Jie Zhu: Data curation ; formal analysis ; methodology ; writing – original draft . Qi‐Ling Deng: Data curation . Jun‐Ling Peng: Data curation . Yi‐Yang Li: Data curation . Ying Sun: Conceptualization ; funding acquisition . Li Lin: Conceptualization .This work was supported by the National Key Research and Development Program of China (grant nos. 2020YFC1316900 and 2020YFC1316901), the National Natural Science Foundation of China (grant nos. 82002220 and 81902762), the China Postdoctoral Science Foundation (grant no. 2019M663305), the National Key R&D Program of China (grant nos. 2020YFC1316900 and No. 2020YFC1316904). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.The authors have no conflict of interest to declare otherwise.The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Data S1Click here for additional data file.


Mitochondrial RNA polymerase (POLRMT) is essential for the expression of mitochondrial genes. In recent studies, POLRMT expression promoted non‐small cell cancer cell proliferation in cell lines and xenografts. The present study investigated the impact of POLRMT expression and function on lung adenocarcinoma (LUAD) patients.Multi‐omics data  from publicly available databases were used to assess the role of POLRMT expression and function in LUAD. These findings were further verified using cancer tissues from clinical samples.POLRMT was over‐expressed in LUADs, with mutation frequencies ranging from 1.30% to 5.71%. Over‐expression of POLRMT was associated with an abnormal clinicopathological condition resulting in a decreased lifespan. Furthermore, gene sets enrich analysis revealed that POLRMT expression was linked to WNT/beta‐catenin signaling; the expression of downstream target genes was positively correlated with POLRMT expression. Also, POLRMT expression was positively correlated with immunosuppressive genes, thereby affecting immune infiltration.POLRMT is over‐expressed in LUAD, thereby impacting patient survival. It is also involved in WNT/beta‐catenin signaling and may affect tumor infiltration. All tissue samples were stored at −80°C. All patients in the study were pathologically diagnosed with lung cancer after surgery and did not exhibit any other severe health complications . The clinical data of patients were collected, which included age, gender, smoking history, histopathological grade, carcinoembryonic antigen, and TNM classification, the overall survival (OS). The survival data were ultimately collected by February 2022.This study was approved by the ethics committee of the Second Affiliated Hospital of Soochow University after review.2.2https://www.cbioportal.org/), giving preference to the dataset with the larger sample size if there were overlapping samples. The promoter methylation level of LUAD was assessed based on The Cancer Genome Atlas Lung Adenocarcinoma Project (TCGA‐LUAD) using the UALCAN websitehttps://ualcan.path.uab.edu/). For mRNA expression analysis, RNA sequence data on LUAD were obtained from TCGA‐LUAD using the GDC data portal , and transcripts per kilobase million were extracted for POLRMT expression analysis. Gene chip data were acquired from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) using the search term “lung adenocarcinoma” with organism, entry type, and study type restricted to Homo sapiens, serial, and expression profiling by array, respectively. We preferred datasets that contained both tumor and normal samples with comparable sample sizes, and ultimately chose two datasets (GSE46539 and GSE10072) to investigate POLRMT expression. Protein expression was evaluated using Clinical Proteomic Tumor Analysis Consortium (CPTAC) samples analyzed via the UALCAN website.We investigated the somatic mutation frequency of POLRMT in LUAD data from the cBioPortal database (2.3http://www.gsea‐msigdb.org/gsea/msigdb/index.jsp), including hallmark gene sets,https://cistrome.shinyapps.io/timer/), an online tool that can draw expression scatterplots and provide Spearman's rho value and statistical significance for given genes.Tumor samples from TCGA‐LUAD were divided into POLRMT‐high and POLRMT‐low groups based on median POLRMT expression. The counts of each gene were extracted to calculate log2‐fold change using the edgeR package2.4https://cibersortx.stanford.edu) and used as the signature gene file.The correlation between the expression of POLRMT and immune cell infiltration was examined using CIBERSORT2.52O2 for 15–40 min and overlaying them with 10% normal serum blocking reagent for 1 h at room temperature. Subsequently, a diluted solution composed of the primary antibody and blocking reagent was applied to the tissues with parafilm for 2 h in a humidified container at room temperature. The tissues were then incubated with biotinylated secondary antibodies in a block with Cell G reagent for 1 h after elution, followed by incubation with 3,3′‐diaminobenzidine tetra‐hydrochloride and 10% Mayer's hematoxylin to counterstain nuclei. Finally, the prepared slides were viewed under a light microscope for protein detection.Of 117 pairs of paraffin‐embedded NSCLC tissues, 113 LUAD were selected for immunohistochemical (IHC) assays. After paraffin detachment and rehydration, epitopes of the tissues were retrieved by microwaving in antigen retrieval buffer. The slides were then washed gently by soaking in phosphate‐buffered saline three times. Next, endogenous peroxidase and non‐specific binding sites were blocked by submerging the slides in 0.3% H2.6Clinical samples were divided into subgroups based on the expression scores of POLRMT, which were calculated by immunohistochemistry. An empirical value of 4 was set as the cutoff value to distinguish between high‐ and low‐level expressions. The expression score was determined as the product of two factors: the total number of cells stained, which were categorized as 0 (negative), 1 (weak), 2 (moderate), or 3 (strong), and the intensity of staining, which was categorized as 1 (0%–25% positive), 2 (26%–50% positive), 3 (51%–75% positive), or 4 (76%–100% positive).2.7t‐test was used for continuous variables while Pearson's chi‐square test or the Mann–Whitney U test was used for dichotomous variables. The survival data were analyzed and visualized using Kaplan–Meier curve via survivalp‐value of no more than 0.05 on univariate Cox regression analysis were subjected to multivariate Cox regression models when exploring the risk factors for lung cancer survival. A p‐value <0.05 indicated statistical significance.Statistical analyses were performed and visualized in R (version 4.1.1) or SPSS (version 17.0). For continuous and dichotomous variables, we report the mean and standard deviation, frequency, and proportion. The 33.1We first investigated POLRMT expression using RNA sequence data from TCGA database. POLRMT was found to be significantly over‐expressed in the tumor samples Figure . Considen = 113). There were 39 cases of strong POLRMT expression in the tumor samples , while there were only 11 cases of strong POLRMT expression in the adjacent normal tissues . The inter‐group difference was found to be significant (p < 0.0001).To verify these findings, an IHC assay was performed using surgical samples from clinical patients  using KM Plotter.p = 0.022)   Figure . As TNM 7) Table . Moreove7) Table .3.3p = 0.023)  and CPTAC datasets   Figure ; this wa) Figure .3.4As the POLRMT expression level was associated with an unfavorable clinical outcome in patients and promoted NSCLC proliferation in cell lines and xenografts as shown in our previous study,Moreover, GSEA of vital biological process‐related gene sets like canonical pathways and gene ontology biological process subsets revealed that metabolism and synthesis of steroid hormones and three primary nutrients  were upregulated. In addition, gene sets responsible for body immunity, signal transduction, and material transport were upregulated, showing a clear impact of POLMRT on these genes Table .3.5TIGIT, CTLA4, CSF1R, LAG3, and PDCD1 , natural killer (NK) cells, monocytes, neutrophils, dendritic cells (DCs), and macrophages using TIMER2.+ T cells. Treg cell markers were positively associated with POLRMT , activated NK cells, and M0 macrophages showed more infiltration Figure .n = 4), DCs (n = 2), and B lymphocytes (n = 2) , implying that it could be used as a biomarker for early diagnosis and a potential target for drug development. There are putative drugs in the pipeline that target POLRMT and they have already shown promising antitumor effects in mice without causing oxidative phosphorylation dysfunction or toxic effects in normal tissues.Besides the role of POLRMT in NSCLC, recently published studies have revealed the protumoral role of POLRMT in skin squamous cell carcinoma, osteosarcoma, acute myeloid leukemia, and breast cancer. Considering the role of POLRMT in mtDNA transcription, these studies assessed the transcripts of mtDNA genes , and found that the expression of these genes decreased with decreasing POLRMT expression.Notably, our further analysis revealed a positive correlation between POLRMT and beta‐catenin , TCF and LEF ; formal analysis ; methodology ; visualization ; writing – original draft . Yajuan Qian: Investigation ; writing – original draft . Yufei Xing: Investigation ; methodology ; writing – review and editing . Yongjian Pei: Data curation ; validation ; writing – review and editing . Beilei Zhang: Resources ; software . Ting Li: Investigation (supporting); validation ; visualization (supporting). Xue Pan: Software ; visualization . Anyuan Zhong: Software ; writing – review and editing . Juan Du: Data curation (supporting); validation ; writing – review and editing . Tong Zhou: Conceptualization ; funding acquisition ; project administration ; supervision . Minhua Shi: Conceptualization ; funding acquisition ; project administration ; supervision .This study was approved by the Ethics Committee of the Second Affiliated Hospital of Soochow University.Figure S1.Figure S2.Click here for additional data file.Table S1.Click here for additional data file.


Para‐tracheal or para‐carinal air cysts (PACs) are often asymptomatic and usually detected incidentally by methods such as computed tomography. Their clinical significance is unclear in patients with pleuroparenchymal fibroelastosis (PPFE).We evaluated the clinical significance of PACs in PPFE and their relationship with pneumomediastinum or pneumothorax.n = 30). A para‐tracheal air cyst was detected in only nine patients, which included five patients having both para‐carinal and para‐tracheal air cysts. Overall median survival was 24.7 months. Survival was not significantly different between the patients with [PACs(+)] and without PACs (P = 0.268). A high frequency (64%) of the complication of pneumomediastinum or pneumothorax occurred in the overall population during follow‐up. Pneumomediastinum/pneumothorax occurred significantly more frequently in patients with PACs(+) than in those without . PACs(+) was the only significant risk factor for pneumomediastinum/pneumothorax.In total, 50 patients had PPFE and 34 (68%) had PACs. Most PACs were para‐carinal  was a significant risk factor for pneumomediastinum/pneumothorax; therefore, clinicians should be more aware of these complications during follow‐up examination, particular in PACs(+) patients with PPFE. Para‐tracheal or para‐carinal air cysts (PACs) commonly occur in patients with pleuroparenchymal fibroelastosis (PPFE), and most PACs were “para‐carinal air cysts.” PACs was significant risk factor for pneumomediastinum/pneumothorax; therefore, clinicians might be more careful of these complications during follow‐up examination, particular in having PACs patients with PPFE. The Kaplan–Meier method and log‐rank test were used to display and compare survival curves of the cohort stratified into two groups. Logistic regression analysis was performed to identify predictive factors associated with complications of pneumomediastinum/pneumothorax during follow‐up. P < 0.05 indicated statistical significance. All data were analyzed with SPSS version 22.0 . Our Institutional Review Board approved this study (21‐W) and waived requirements for informed consent.Para‐tracheal or para‐carinal air cysts (PACs) are often asymptomatic and usually detected incidentally by various imaging methods.n = 30) in a para‐carinal location  patients were males, and 21 (42%) had a smoking history (Table P = 0.268). Overall median survival was 24.7 months. Five‐year survival rates were 20.8% in the PACs(+) group and 54.1% in the PACs(−) group.Dyspnea on exertion was significantly more frequent in the PACs(+) than PACs(−) group Table . No signP = 0.012) (Table n = 26), seven patients had pneumomediastinum only, six had pneumothorax only, and 13 had both pneumomediastinum and pneumothorax. Contrastingly, in the PACs(−) group (n = 6), four patients had pneumothorax only, two had both pneumomediastinum and pneumothorax, and none had pneumomediastinum only. PACs(+) was the only significant risk factor for pneumomediastinum/pneumothorax  by multivariate analysis with forward variable selection , in the six patients with PACs on the right side, the pneumothorax was on the right side in one, left side in two, and both sides in three patients. In the eight patients with PACs on the left side, the pneumothorax was on the right side in one, left side in four, and both sides in three patients. In the patients with bilateral PACs (n = 5), the pneumothorax was on the right side in one, left side in four, and both sides in none.During follow‐up, complications of pneumomediastinum/pneumothorax occurred highly frequently (64%) among all patients and significantly more frequently in the PACs(+) versus PACs(−) group  patients with PPFE. As limitations, this retrospective study conducted in a single referral hospital limits the generalizability of our findings, and accurate conclusions cannot be drawn due to the small number of patients. Further studies with larger sample size are needed to clarify the significance of PACs in patients with PPFE.Hideaki Yamakawa, Shintaro Sato, and Hiroki Ohta acquired the data; Hideaki Yamakawa, Shintaro Sato, Hiroki Ohta, Makiko Takatsuka, Kenji Kusano, Rie Kawabe, Tomohiro Oba, Keiichi Akasaka, Masako Amano, Jun Araya, Hiroki Sasaki, and Hidekazu Matsushima analyzed and interpreted the clinical data; and Hideaki Yamakawa and Hidekazu Matsushima drafted the manuscript. All authors read and approved the final manuscript.All authors declare no conflicts of interest.The current study got ethical approval from the Saitama Red Cross Hospital Research Ethics Board and did not need informed consent. The ethics number is 21‐W.


Cannabis use, be it either cannabidiol (CBD) use and/or delta-9-tetrahydrocannabinol (THC) use, shows promise to enhance exercise recovery. The present study aimed to determine if individuals are using CBD and/or THC as a means of recovery from aerobic and/or resistance exercise, as well as additional modalities that might be used to aid in recovery.Following consent, 111 participants (Mean ± SD: Age: 31 ± 13 years) completed an anonymous survey. All participants were regularly using cannabis (CBD and/or THC) as well as were currently exercising. Questions pertained to level of cannabis use, methods used for consumption of cannabis, exercise habits, exercise recovery strategies, and demographics.Eighty-five percent of participants reported participating in aerobic training. In addition, 85% of participants also reported regular participation in resistance exercise. Seventy-two percent of participants participated in both aerobic and resistance exercise. Ninety-three percent of participants felt that CBD use assisted them with recovery from exercise, while 87% of participants felt the same regarding THC use.Individuals who habitually use cannabis, CBD or THC, and regularly engage in exercise do feel that cannabis assists them with exercise recovery. More data are necessary to understand the role of cannabis in exercise recovery as well as perceived ergogenic benefits of cannabis by individuals who both regularly participate in exercise and habitually use cannabis.The online version contains supplementary material available at 10.1186/s42238-023-00198-5. Over the past decade, cannabis use has become more widespread in the United States, both medically and recreationally , foam rolling, cupping, electrical stimulation, compression garments, cryotherapy, contrast water bath, cold-water immersion and other supplements focused on recovery have all been purported to enhance exercise recovery as well that are highlighted in a review by Dupuy et al. . Data frTo date, few investigations have directly evaluated changes in muscle soreness or recovery with ingestion of cannabis surrounding aerobic or resistance exercise , through word of mouth, and posted flyers. All participant recruitment material contained a QR code linked to the survey. The data were collected from June 2022-November 2022. Inclusion criteria included being at least 18 years of age, currently consuming cannabis, and currently engaged in regular exercise. Once individuals read the informed consent they selected ‘yes’, or ‘no’. If the individuals selected ‘yes’ they were given access to the questionnaire, allowing participants to answer questions pertaining to cannabis use (CBD and THC), exercise habits (aerobic and resistance exercise), views on recovery methods, and demographics. If the individuals selected ‘no’ then the questionnaire ended. Participants were able to skip questions as they saw fit, and all data were anonymous. This study was approved by the Kent State University Institutional Review Board.The questionnaire utilized multiple question formats including multiple choice, Likert scale, and check all that apply . Once consent was provided by the participant, the first part of the questionnaire was completed regarding cannabis use; one set of questions pertaining to the use of CBD, and another set pertaining to the use of THC. Each set of questions had five subsets; the duration participants have been using, number of days per week, number of times per day, amount of money spent on these products, and preferred method of consumption.The second set of questions pertained to current exercise habits and included nine subsets; currently physically active/exercise, duration participant has been exercising, exercise with a team, minutes of exercise per week, preferred modalities of exercises, engagement in aerobic exercise, engagement in resistance exercise, the use of supplements to enhance performance, and additional recovery methods that do not include cannabis. Participants were also asked if they used cannabis as a recovery modality and were also asked if they felt that cannabis aided in recovery. There were also a set of questions related to intensity of the workouts, the number of days spent exercising, and how many minutes per week for both aerobic exercise and resistance exercise.2) was used to evaluate differences between CBD users and THC users on exercise habits. In addition, Pearson’s correlation coefficients were used to assess the relationship between motivations for cannabis use and recovery. All statistical analyses were completed using IBM SPSS . All data are presented as mean ± standard deviation. Statistical significance was set a priori at p ≤ 0.05 for all comparisons.Differences between men, women, and non-binary individuals were assessed with a Kruskal–Wallis nonparametric test. Chi-square  performed moderate-vigorous aerobic exercise, three-four days a week for 31–150 min. Participants (n = 72) that resistance trained reported doing so three days a week for 31–150 min/wk at a vigorous intensity. When comparing CBD users to THC users it was noted that men CBD users preferred a higher intensity for their aerobic  = 21.7, p = 0.001) and resistance exercise  = 10.1, p = 0.017), and women CBD users preferred a higher intensity resistance exercise  = 29.8, p = 0.001). There were no differences in those that identified as non-binary. Exercise modalities mentioned by the participants are included in Table Eighty-five percent of participants reported they regularly performed aerobic exercise and 85% reported they resistance trained, with 72% participating in both modalities. Data regarding sex specific differences in exercise habits are presented in Table n = 7), lightheadedness (n = 3), anxiety (n = 3), heart racing (n = 2), paranoia (n = 1), loss of coordination (n = 1), difficulty breathing (n = 1), poor reaction time (n = 1), other (n = 18), and none (n = 55).The majority of participants indicated they had been using CBD for 1–3 years (37%), with participants reporting 1–6 months (10%), 6 months to 1 year (9%), 3–6 years (34%), 6–9 years (10%) and more than 10 years (0%). Thirty-two percent of those CBD users used it seven d/wk, 1–3 times a day (50%), and spent $50–100 per month (21%) on CBD products. Forms of CBD consumption included edibles (30%), vaping (27%), smoking (25%), tinctures (11%), topicals (7%). Negative effects in participants using CBD included felt dehydrated (n = 16), too sleepy (n = 14), heart racing (n = 12), paranoia (n = 6), anxiety (n = 6), weakness (n = 6), difficulty breathing (n = 5), laughing uncontrollably (n = 3), poor reaction time (n = 3), lightheadedness (n = 3), poor balance (n = 2), enhanced pain (n = 1), other (n = 16), and none (n = 33).THC use was the highest at 1–3 years of use (44%), with participants reporting less than 1 month (0%), 1–6 months (7%), 6 months to 1 year (1%), 3–6 years (25%), 6–9 years (5%) and more than 10 years (18%). Fifty-two percent of participants stated that they used THC seven d/wk, 1–3 times a day (76%), and spent $50–100 per month (39%) on products with THC. The forms of THC consumption included smoking (40%), edibles (30%), vaping (26%), tinctures (2%), topicals (1%) and other (1%). Negative effects of THC use included got too high (Twenty-two participants (20%) reported using CBD for recovery from aerobic exercise and 25 participants (23%) reported CBD use to recover from resistance exercise. Sixty-eight participants (61%) reported using THC after aerobic exercise for recovery. Similarly, 67 participants (60%) reported using THC after resistance exercise for recovery. When participants were asked, ‘Do you feel that cannabis in the form of CBD aids in your recovery?’ 93% stated ‘yes’ while 7% stated, “I’m not sure.’ When asked, ‘Do you feel that THC aids in your recovery?’, 87% of participants stated, ‘yes’ while 13% stated, ‘I’m not sure’. No participant answered, ‘no’, for either of these questions. All of the participants felt that use of cannabis was low risk regarding health outcomes.Supplements used by the participants are shown in Table Motives for CBD use demonstrated that participants primarily used it to assist with sleep (3.89) and to relax (3.89). Other reasons for CBD use were because it gives a pleasant feeling (3.37), and to alleviate pain (3.05). Motives for THC use was primarily to relax (4.40). Other notable reasons included because it gives a pleasant feeling (4.17), is fun (3.79), to assist with sleep (3.81), to alleviate pain (3.10), to help alleviate depression or nervousness (3.29), and to cheer up when in a bad mood (3.21). There were no significant correlations between motivations for cannabis use and self-reported recovery.The primary aim of this study was to determine if individuals are using CBD and/or THC to assist recovery from exercise and if they believed cannabis to be an effective recovery modality. The data from the present study suggest that individuals actively use CBD and/or THC as part of their recovery. In addition, our participants stated that they felt that cannabis assisted with their recovery from both aerobic and resistance exercise.Studies have shown cannabis, CBD or THC, has a minimal effect on performance (Renaud and Cormier One of the primary purported roles of CBD or THC is pain management (Ajrawat et al. Multiple studies to date have detailed immune responses with CBD or THC consumption surrounding exercise (Sahinovic et al. Sleep is an important aspect of recovering from exercise (Kasper et al. While the present study demonstrates that individuals are using cannabis to recover from exercise, there are a litany of other modalities that are being used in conjunction as well. However, evidence varies heavily between modalities. Studies have demonstrated that implementation of a static stretching protocol following a bout of exercise does not improve perceived muscle soreness, enhance exercise performance in a subsequent exercise bout, or reduce inflammatory markers in both recreationally active and athletic populations (Bonfim et al. The present study is not without limitations. The legality of cannabis across states generates an inherent issue regarding legality of cannabis use across individuals that participated in the present study. While the present study collected data on which states individuals reside, it was not the purpose of the present study to delineate which users were following state laws regarding cannabis use. The purpose of the present was exploratory in nature, such that connections between cannabis use and recovery from exercise are anecdotal. In addition, the data collected are self-reported. The sample size was also small, which may limit the ability to draw conclusions. Lastly, the majority of the participants were from the Midwest region of the United States, which has limited access to cannabis medically or recreationally.The effects of cannabis on recovery are not well understood and there are limited data directly examining the relationship. However, recovery from exercise is multi-faceted and complex. CBD or THC use have been suggested to play a role in pain management, inflammation, and sleep, which are hallmarks for recovery from exercise. While data are lacking, it is clear that individuals are using cannabis and believe cannabis to have a positive effect on recovery from exercise. The present study demonstrated that in addition to more traditional recovery methods, cannabis is used as an ergogenic recovery aid by individuals that exercise regularly. Regardless, the present study was exploratory in nature, and it is clear that more data are needed regarding cannabis, CBD and THC, on recovery from exercise.Additional file 1. Cannabis survey.