Lymphoma is a highly heterogeneous malignancy characterized by complex molecular regulatory mechanisms that result in significant differences in aggressiveness and prognosis across its subtypes.Gene copy number alteration(CNA)analysis,an emerging technology,has become a pivotal tool in the precision re-search and management of lymphoma.By detecting DNA deletions,amplifications,and chromosomal copy number changes,CNA analysis addresses the limitations of traditional cytogenetic techniques,enhances the ac-curacy of subtype classification,and aids in evaluating tumor heterogeneity and disease progression.This re-view provides a comprehensive summary of CNA detection methods and their applications in lymphoma,with a focus on recent advancements in the field.It offers a comparative analysis of CNA detection techniques and discusses their role in precision diagnosis,subtype classification,monitoring disease progression,predicting therapeutic resistance,and assessing prognosis.Additionally,the review explores the potential applications of CNA analysis in uncovering molecular regulatory mechanisms,optimizing therapeutic strategies,and impro-ving patient survival outcomes.

Objective:Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions.Methods:Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis.Results:In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion:Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

The comorbidity of depression and cancer represents a significant global public health challenge,severely impacting patients' quality of life and clinical outcomes.This systematic review considers the epidemiological characteristics,clinical implications,and major challenges in current research regarding comorbid depression and cancer,focusing on the role of depression in promoting tumor progression and suppressing immune function via the neuroendocrine-immune regulatory network.We discuss the dynamic changes and interaction mechanisms of depression-related neurotransmitters(such as serotonin and norepinephrine)and stress hormones(such as cortisol)within the tumor microenvironment.We also reveal the molecular mechanisms by which depression regulates malignant biological behaviors such as tumor immune evasion,metastasis,and angiogenesis via activation of the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system.This review also evaluates the application value and limitations of existing animal models for studying the mechanisms underlying the comorbidity of depression and cancer,emphasizing the importance and urgency of developing more precise comorbidity models to uncover the mechanisms and explore management strategies.This review aims to raise awareness of risk prediction,clinical interventions,and basic research on the comorbidity of depression and cancer,to provide a theoretical foundation and new research directions for developing depression-cancer comorbidity models.

Objective To explore the value of time-dependent diffusion MRI(td-dMRI)parameters for differentiating invasive breast cancer(IBC)with ductal carcinoma in situ(DCIS)(IBC-DCIS)from simple IBC.Methods A total of 19 patients with IBC-DCIS(IBC-DCIS group)and 53 patients with simple IBC(IBC group)confirmed by surgery and postoperation pathology were retrospectively enrolled.Breast td-dMRI acquired with oscillating gradient spin-echo(OGSE)and pulsed gradient spin-echo(PGSE)sequences before operation were interpreted,and apparent diffusion coefficient(ADC)and microstructure parameters,including OGSE-ADC value,PGSE-ADC value,cellularity,cell diameter,intracellular volume fraction and extracellular diffusion coefficient were obtained and compared between groups.Receiver operating characteristic(ROC)curves of parameters being significantly different between groups were plotted,and the area under the curve(AUC)was calculated to evaluate the efficacy of these parameters for differentiating IBC-DCIS from IBC.Results Significant differences of OGSE-ADC value,PGSE-ADC value,cellularity,cell diameter,intracellular volume fraction and extracellular diffusion coefficient were found between groups(all P＜0.05).The AUC of the above parameters for differentiating IBC-DCIS from IBC was 0.81,0.79,0.78,0.68,0.77 and 0.81,respectively.Conclusion td-dMRI parameters could be used to noninvasively and effectively differentiate IBC-DCIS from simple IBC.

The comorbidity of depression and cancer represents a significant global public health challenge,severely impacting patients' quality of life and clinical outcomes.This systematic review considers the epidemiological characteristics,clinical implications,and major challenges in current research regarding comorbid depression and cancer,focusing on the role of depression in promoting tumor progression and suppressing immune function via the neuroendocrine-immune regulatory network.We discuss the dynamic changes and interaction mechanisms of depression-related neurotransmitters(such as serotonin and norepinephrine)and stress hormones(such as cortisol)within the tumor microenvironment.We also reveal the molecular mechanisms by which depression regulates malignant biological behaviors such as tumor immune evasion,metastasis,and angiogenesis via activation of the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system.This review also evaluates the application value and limitations of existing animal models for studying the mechanisms underlying the comorbidity of depression and cancer,emphasizing the importance and urgency of developing more precise comorbidity models to uncover the mechanisms and explore management strategies.This review aims to raise awareness of risk prediction,clinical interventions,and basic research on the comorbidity of depression and cancer,to provide a theoretical foundation and new research directions for developing depression-cancer comorbidity models.

Objective To explore the value of time-dependent diffusion MRI(td-dMRI)parameters for differentiating invasive breast cancer(IBC)with ductal carcinoma in situ(DCIS)(IBC-DCIS)from simple IBC.Methods A total of 19 patients with IBC-DCIS(IBC-DCIS group)and 53 patients with simple IBC(IBC group)confirmed by surgery and postoperation pathology were retrospectively enrolled.Breast td-dMRI acquired with oscillating gradient spin-echo(OGSE)and pulsed gradient spin-echo(PGSE)sequences before operation were interpreted,and apparent diffusion coefficient(ADC)and microstructure parameters,including OGSE-ADC value,PGSE-ADC value,cellularity,cell diameter,intracellular volume fraction and extracellular diffusion coefficient were obtained and compared between groups.Receiver operating characteristic(ROC)curves of parameters being significantly different between groups were plotted,and the area under the curve(AUC)was calculated to evaluate the efficacy of these parameters for differentiating IBC-DCIS from IBC.Results Significant differences of OGSE-ADC value,PGSE-ADC value,cellularity,cell diameter,intracellular volume fraction and extracellular diffusion coefficient were found between groups(all P＜0.05).The AUC of the above parameters for differentiating IBC-DCIS from IBC was 0.81,0.79,0.78,0.68,0.77 and 0.81,respectively.Conclusion td-dMRI parameters could be used to noninvasively and effectively differentiate IBC-DCIS from simple IBC.

Objective:Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions.Methods:Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis.Results:In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion:Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

Sedentary bad habits and unhealthy diets in modern lifestyles have led to an upward trend in the incidence of obesity, and a series of diseases related to obesity have also gradually received attention. Multiple sclerosis is a chronic inflammatory disease of the central nervous system, and obesity has a common inflammatory component with most chronic diseases. Therefore, this paper reviews the research progress on the relationship between obesity and multiple sclerosis in order to better understand the role of obesity in the management of multiple sclerosis.

Objective:To explore the clinical effectiveness of functional magnetic stimulation (FMS) of the sacral nerve root in the treatment of diabetic neurogenic bladder (DNB).Methods:A total of 134 DNB patients were divided into an observation group and a control group, each of 67. Both groups were given conventional drug therapy and bladder function training, while the observation group was additionally provided with sacral nerve root FMS for 4 weeks. The urine and urine flow dynamics of both groups were tested before and after the experiment.Results:After treatment the average daily volume of single urination had increased significantly in both groups and the average daily urinations and incidence of incontinence had decreased significantly. The observation group′s average single urination volume was then significantly greater than that of the control group. The observation group′s average daily urinations (8.87±1.42) and incontinence incidents (3.04±1.93) were then significantly fewer than the control group′s. After the treatment the detrusor pressure at maximum flow, bladder sense initial volition and residual urine volume of both groups had decreased significantly compared with before the treatment, while their average maximum flow rates had increased significantly. But the observation group′s averages were in all cases significantly better than those of the control group. The treatment′s total effectiveness rate among the observation group (88%) was significantly better than among the control group (72%).Conclusions:Supplementing routine bladder function training with FMS applied to the root of the sacral nerve can further promote the regaining of bladder function among DNB patients, improve their urinary flow dynamics and improve their quality of life. Such combination therapy is worthy of clinical promotion and application.

OBJECTIVE: To explore the clinical characteristics and genetic etiology of a child with Schaaf-Yang syndrome (SYS). METHODS: Peripheral blood samples of the child and his parents were collected and subjected to whole exome sequencing. Sanger sequencing was used for family constellation verification, and bioinformatic analysis was performed for the candidate variant. RESULTS: The child, a 1-year-and-9-month-old boy, had clinical manifestations of retarded growth, small penis, and unusual facies. Genetic testing revealed that the child has harbored a novel heterozygous variant of c.3078dupG (p.Leu1027Valfs*28) of the MAGEL2 gene. Sanger sequencing showed that neither parent of the child carried the same variant. The c.3078dupG(p.Leu1027Valfs*28) variant of the MAGEL2 gene has not been included in the databases of ESP, 1000 Genomes and ExAC. According to the Standards and Guidelines for the Interpretation of Sequence Variants of the American College of Medical Genetics and Genomics (ACMG), the variant was judged to be pathogenic. CONCLUSION The c.3078dupG (p.Leu1027Valfs*28) variant of the MAGEL2 gene probably underlay the SYS in this child, which has further expanded the spectrum of the MAGEL2 gene variants.
Child ; Humans ; Infant ; Male ; Exome Sequencing ; Genetic Testing ; Heterozygote ; Mutation ; Proteins/genetics* ; Developmental Disabilities/genetics*