Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:This study sought to examine the clinicopathological features of monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) and to discuss its differential diagnosis.Methods:A total of 36 MEITL cases, collected between June 2015 and January 2024 from the Fourth Affiliated Hospital of Soochow University and the First Affiliated Hospital, College of Medicine, Zhejiang University, were analyzed. Patients underwent immunohistochemistry, in situ hybridization for Epstein-Barr virus-encoded small RNA (EBER), and T-cell receptor (TCR) gene rearrangement testing. Clinical data, laboratory results, and follow-up information were collected for correlation analysis.Results:The cohort included 36 patients (20 males and 16 females) aged 17-76 years (median: 57 years). Tumors outside the intestine were observed in 22 cases (61%). A total of 32 patients (89%) underwent surgical intervention and/or chemotherapy, and one patient received auto-HSCT. The median follow-up duration was 11.5 months (range: 8-73 months), with a median overall survival of 6 months (range: 1-67 months) ; 34 patients died during the follow-up period. Morphologically, nine cases (25%) exhibited significant pleomorphism. Immunohistochemical analysis revealed that high expression levels of both P53 and c-Myc were correlated with atypical morphology ( P=0.003 and P=0.016, respectively). Notably, patients with high P53 expression had significantly shorter survival times than those with low P53 expression ( χ2=4.922, P=0.027), whereas survival did not differ significantly based on c-Myc expression levels ( χ2=0.034, P=0.854). Furthermore, a PD-L1 CPS score ≥10 was observed in 22 cases (68.8%). Scattered EBER positivity in background cells was identified in four cases. All tested cases (17/17, 100.0%) showed clonal TCR gene rearrangements. Conclusions:MEITL is a rare but highly aggressive lymphoma with distinct clinical and pathological features. A subset of cases may exhibit atypical morphological patterns, complicating the diagnostic process. Improving awareness of this neoplasm is helpful for early and precise diagnosis as well as the estabolishment of novel therapy regimen.

Objective:To investigate the value of machine learning models based on CT radiomics for predicting the outcome of neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC).Methods:A retrospective case series study was conducted. A total of 279 LAGC patients receiving NAC before surgery in Shanxi Province Cancer Hospital from January 2018 to November 2020 were included. According to a ratio of 7∶3, all patients were randomly divided into the training set (196 cases) and the validation set (83 cases). According to the tumor regression grade (TRG), the pathological grade was divided into the good response of NAC (GR) group (TRG 0-1, 55 cases) and the poor response of NAC (PR) group (TRG 2-3, 224 cases). The clinicopathological data of patients were collected, such as age, gender, differentiation degree, clinical T and N staging, carcinoembryonic antigen (CEA), and carbohydrate antigen 199 (CA199) level. Radiomics features were extracted from the enhanced CT images in the vein phase, and the features were screened by 3-step dimensionality reduction. And then 5 machine learning algorithms including logistic regression (LR), naive bayes (NB), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB) were applied to build prediction models based on the CT radiomics. The receiver operating characteristic (ROC) curve and the decision analysis (DCA) curve were drawn to evaluate the predictive performance and clinical benefit of each model on the outcome of NAC in patients with LAGC.Results:Among 196 patients in the training set, there were 39 cases in GR group and 157 cases in PR group; among 83 patients in the validation set, there were 16 cases in GR group and 67 cases in PR group. There were no statistically significant differences in clinicopathological data of patients between the training and validation sets, or between GR and PR groups in the training and validation sets (all P > 0.05). A total of 102 radiomics features were extracted from region of interest of CT images in the vein phase, and 6 key features were finally selected including original_firstorder_10Percentile, original_firstorder_RoubustMeanAbsoluteDeviation, original_glcm_Idmn, original_glcm_MCC, original_ngtdm_Busyness, original_ngtdm_Contrast; and there were statistically significant differences in 6 features between the GR and PR groups (all P < 0.05). LR, NB, RF, SVM and XGB machine learning algorithms were used to construct 5 prediction models based on the CT radiomics. The area under ROC curve for NAC prediction in the training set was 0.553, 0.709, 0.668, 0.772 and 0.790, respectively; in the validation set was 0.662, 0.622, 0.683, 0.752 and 0.784, respectively. The model constructed by XGB showed the best comprehensive performance, and its accuracy, sensitivity and specificity was 0.771, 0.562 and 0.821, respectively. In the DCA of 5 machine learning models in the training set, XGB-based model provided a higher net benefit. Conclusions:Machine learning models based on enhanced CT radiomics in the vein phase have a high predictive efficacy in the outcome of NAC in LAGC patients before surgery and it helps make clinical personalized treatment decisions.

Objective:This study sought to examine the clinicopathological features of monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) and to discuss its differential diagnosis.Methods:A total of 36 MEITL cases, collected between June 2015 and January 2024 from the Fourth Affiliated Hospital of Soochow University and the First Affiliated Hospital, College of Medicine, Zhejiang University, were analyzed. Patients underwent immunohistochemistry, in situ hybridization for Epstein-Barr virus-encoded small RNA (EBER), and T-cell receptor (TCR) gene rearrangement testing. Clinical data, laboratory results, and follow-up information were collected for correlation analysis.Results:The cohort included 36 patients (20 males and 16 females) aged 17-76 years (median: 57 years). Tumors outside the intestine were observed in 22 cases (61%). A total of 32 patients (89%) underwent surgical intervention and/or chemotherapy, and one patient received auto-HSCT. The median follow-up duration was 11.5 months (range: 8-73 months), with a median overall survival of 6 months (range: 1-67 months) ; 34 patients died during the follow-up period. Morphologically, nine cases (25%) exhibited significant pleomorphism. Immunohistochemical analysis revealed that high expression levels of both P53 and c-Myc were correlated with atypical morphology ( P=0.003 and P=0.016, respectively). Notably, patients with high P53 expression had significantly shorter survival times than those with low P53 expression ( χ2=4.922, P=0.027), whereas survival did not differ significantly based on c-Myc expression levels ( χ2=0.034, P=0.854). Furthermore, a PD-L1 CPS score ≥10 was observed in 22 cases (68.8%). Scattered EBER positivity in background cells was identified in four cases. All tested cases (17/17, 100.0%) showed clonal TCR gene rearrangements. Conclusions:MEITL is a rare but highly aggressive lymphoma with distinct clinical and pathological features. A subset of cases may exhibit atypical morphological patterns, complicating the diagnostic process. Improving awareness of this neoplasm is helpful for early and precise diagnosis as well as the estabolishment of novel therapy regimen.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To investigate the value of machine learning models based on CT radiomics for predicting the outcome of neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC).Methods:A retrospective case series study was conducted. A total of 279 LAGC patients receiving NAC before surgery in Shanxi Province Cancer Hospital from January 2018 to November 2020 were included. According to a ratio of 7∶3, all patients were randomly divided into the training set (196 cases) and the validation set (83 cases). According to the tumor regression grade (TRG), the pathological grade was divided into the good response of NAC (GR) group (TRG 0-1, 55 cases) and the poor response of NAC (PR) group (TRG 2-3, 224 cases). The clinicopathological data of patients were collected, such as age, gender, differentiation degree, clinical T and N staging, carcinoembryonic antigen (CEA), and carbohydrate antigen 199 (CA199) level. Radiomics features were extracted from the enhanced CT images in the vein phase, and the features were screened by 3-step dimensionality reduction. And then 5 machine learning algorithms including logistic regression (LR), naive bayes (NB), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB) were applied to build prediction models based on the CT radiomics. The receiver operating characteristic (ROC) curve and the decision analysis (DCA) curve were drawn to evaluate the predictive performance and clinical benefit of each model on the outcome of NAC in patients with LAGC.Results:Among 196 patients in the training set, there were 39 cases in GR group and 157 cases in PR group; among 83 patients in the validation set, there were 16 cases in GR group and 67 cases in PR group. There were no statistically significant differences in clinicopathological data of patients between the training and validation sets, or between GR and PR groups in the training and validation sets (all P > 0.05). A total of 102 radiomics features were extracted from region of interest of CT images in the vein phase, and 6 key features were finally selected including original_firstorder_10Percentile, original_firstorder_RoubustMeanAbsoluteDeviation, original_glcm_Idmn, original_glcm_MCC, original_ngtdm_Busyness, original_ngtdm_Contrast; and there were statistically significant differences in 6 features between the GR and PR groups (all P < 0.05). LR, NB, RF, SVM and XGB machine learning algorithms were used to construct 5 prediction models based on the CT radiomics. The area under ROC curve for NAC prediction in the training set was 0.553, 0.709, 0.668, 0.772 and 0.790, respectively; in the validation set was 0.662, 0.622, 0.683, 0.752 and 0.784, respectively. The model constructed by XGB showed the best comprehensive performance, and its accuracy, sensitivity and specificity was 0.771, 0.562 and 0.821, respectively. In the DCA of 5 machine learning models in the training set, XGB-based model provided a higher net benefit. Conclusions:Machine learning models based on enhanced CT radiomics in the vein phase have a high predictive efficacy in the outcome of NAC in LAGC patients before surgery and it helps make clinical personalized treatment decisions.

Itaconate is a pivotal intermediate metabolite in the tricarboxylic acid (TCA) cycle of immune cells. It is produced by decarboxylation of cis-aconitic acid under the catalysis of aconitate decarboxylase 1 (ACOD1), which is encoded by the immune response gene 1 (IRG1). Itaconate has become a focal point of research on immunometabolism. Studies have demonstrated that itaconate plays a crucial role in diseases by regulating inflammation, remodeling cell metabolism, and participating in epigenetic regulation. This paper reviewed the research progress in itaconnate from its chemical structure, regulatory effects on different diseases, and mechanisms, proposes the future research directions, aiming to provide a theoretical basis for the development of itaconate-related drugs.
Humans ; Succinates/metabolism* ; Carboxy-Lyases/genetics* ; Inflammation/metabolism* ; Citric Acid Cycle ; Animals ; Epigenesis, Genetic ; Neoplasms/immunology*

Ten-eleven translocation 1 (TET1) protein is an alpha-ketoglutaric acid (α-KG) and Fe²⁺-dependent dioxygenase. It plays a role in the active demethylation of DNA by hydroxylation of 5-methyl-cytosine (5-mC) to 5-hydroxymethyl-cytosine (5-hmC). Ten-eleven translocation 1 (TET1) protein is involved in maintaining genome methylation homeostasis and epigenetic regulation. Abnormally expressed TET1 and 5-mC oxidative derivatives have become potential markers in various biological and pathological processes and a research focus in the fields of embryonic development and malignant tumors. This paper introduces the structure and demethylation mechanism of TET1, reviews the research status of epigenetic regulation by TET1 in embryonic development, immune responses, stem cell regulation, cancer progression, and nervous system development, and briefs the upstream regulatory mechanism of TET1, hoping to provide new inspirations for further research in related fields.
Proto-Oncogene Proteins/genetics* ; Epigenesis, Genetic ; Humans ; DNA-Binding Proteins/metabolism* ; DNA Methylation ; Mixed Function Oxygenases/metabolism* ; 5-Methylcytosine/analogs & derivatives* ; Animals ; Embryonic Development/genetics* ; Neoplasms/genetics* ; Dioxygenases/metabolism*

Objective:To establish a rapid quantitative PCR (qPCR) technique for Mycobacterium marinum skin infections, and to analyze its clinical diagnostic efficiency. Methods:DNA was extracted from Mycobacterium marinum colonies and serially diluted (10 -1 to 10 -8). Twelve pairs of previously reported primers and probes, as well as 6 pairs of newly designed primers and probes in this study, were used for qPCR amplification to identify the most sensitive primers and probes for the detection of Mycobacterium marinum. Skin lesion tissues were collected from 72 patients with confirmed Mycobacterium marinum infections (experimental group) and 68 with other mycobacterial infections (control group) at Shandong Provincial Hospital for Skin Diseases & Shandong Provincial Institute of Dermatology and Venereology, Shandong First Medical University & Shandong Academy of Medical Sciences in 2021. These skin tissues were subjected to qPCR amplification, interferon-gamma release assay (IGRA), acid-fast staining, and tissue culture to evaluate the diagnostic efficacy. Results:The newly designed primers and probes targeting the mycobacterial enhanced infection locus 2 (Mel2) demonstrated the highest sensitivity, with a detection limit of 0.86 copies/μl (cycle threshold value = 37) ; the qPCR amplification with the Mel2 primers/probes did not yield positive results when used for the detection of other mycobacteria (including Mycobacterium leprae and Staphylococcus spp) . Among the 72 patients in the experimental group, 44 were positive for qPCR with a sensitivity of 61.1% (95% CI: 49.6% - 71.5%), and 47 were positive for culture with a sensitivity of 65.2% (95% CI: 53.8% - 75.3%) ; all the 68 controls were negative for both qPCR and culture, with their specificities both being 100%. Among 65 patients subjected to IGRA, 31 were positive with a sensitivity of 47.7% (95% CI: 36.0% - 59.6%), while 16 out of 25 controls were negative for IGRA with a specificity of 64.0% (95% CI: 44.5% - 79.8%). Among 58 patients subjected to acid-fast staining, 37 were positive with a sensitivity of 63.8% (95% CI: 50.9% - 74.9%), and 52 out of 66 controls were negative for acid-fast staining with a specificity of 78.8% (95% CI: 67.5% - 86.9%). The combination of qPCR and culture resulted in a sensitivity of 93% and a specificity of 100% for the detection of Mycobacterium marinum. Conclusion:In this study, a highly sensitive qPCR assay was developed for the detection of Mycobacterium marinum, and its combination with culture could further improve the detection sensitivity.

The chronic use of morphine and other opioids is associated with opioid-induced hypersensitivity (OIH) and analgesic tolerance. Among the different forms of OIH and tolerance, the opioid receptors and cell types mediating opioid-induced mechanical allodynia and anti-allodynic tolerance remain unresolved. Here we demonstrated that the loss of peripheral μ-opioid receptors (MORs) or MOR-expressing neurons attenuated thermal tolerance, but did not affect the expression and maintenance of morphine-induced mechanical allodynia and anti-allodynic tolerance. To confirm this result, we made dorsal root ganglia-dorsal roots-sagittal spinal cord slice preparations and recorded low-threshold Aβ-fiber stimulation-evoked inputs and outputs in superficial dorsal horn neurons. Consistent with the behavioral results, peripheral MOR loss did not prevent the opening of Aβ mechanical allodynia pathways in the spinal dorsal horn. Therefore, the peripheral MOR signaling pathway may not be an optimal target for preventing mechanical OIH and analgesic tolerance. Future studies should focus more on central mechanisms.
Humans ; Morphine/pharmacology* ; Hyperalgesia/metabolism* ; Analgesics, Opioid/pharmacology* ; Neurons/metabolism* ; Signal Transduction