Naringenin (4,5,7-trihydroxyflavonoid) is a naturally occurring bioflavonoid found in citrus fruits, which plays an important role in metabolic syndrome, neurological disorders, and cardiovascular diseases. However, the pharmacological mechanism and biological function of naringenin on anti-angiogenesis and anti-tumor immunity have not yet been elucidated. Our study firstly demonstrates that naringenin inhibits the growth of hepatocellular carcinoma (HCC) cells both in vivo and in vitro. Naringenin diminishes the ability of HCC cells to induce tube formation and migration of human umbilical vein endothelial cells (HUVECs) and suppresses neovascularization in chicken chorioallantoic membrane (CAM) assays. Meanwhile, in vivo results demonstrate that naringenin can significantly upregulate level of CD8⁺ T cells, subsequently increasing the level of immune-related cytokines in the tumor immune microenvironment. Mechanistically, we found that naringenin facilitate the K48-linked ubiquitination and subsequent protein degradation of vascular endothelial growth factor A (VEGFA) and mesenchymal-epithelial transition factor (c-Met), which reduces the expression of programmed death ligand 1 (PD-L1). Importantly, combination therapy naringenin with PD-L1 antibody or bevacizumab provided better therapeutic effects in liver cancer. Our study reveals that naringenin can effectively inhibit angiogenesis and anti-tumor immunity in liver cancer by degradation of VEGFA and c-Met in a K48-linked ubiquitination manner. This work enlightens the potential effect of naringenin as a promising therapeutic strategy against anti-angiogenesis and anti-tumor immunity in HCC.

Objective::To analyze fetal renal abnormality genetic features and the prenatal characteristics of the 17q12 microdeletion syndrome.Methods::This prospective cohort study examined prenatal ultrasound findings of renal abnormalities in pregnant women who underwent single nucleotide polymorphism (SNP) array or copy number variation sequencing (CNV-seq) testing on amniotic fluid or fetal tissue at Tianjin Central Obstetrics and Gynecology Hospital between January 2016 and August 2022. The study cohort comprised women with advanced maternal age, fetal ultrasound anomalies, high-risk non-invasive prenatal testing results, or suspected 17q12 microdeletion syndrome. Comprehensive clinical data, including maternal age, detailed ultrasound findings, and pregnancy outcomes, were systematically collected. SNP-array analysis was conducted using an Affymetrix CytoScan 750 K Array Chip to identify CNVs and loss of heterozygosity, while CNV-seq was performed on the Illumina HiSeq 2000 platform. Detected variants were classified according to the American College of Medical Genetics and Genomics guidelines. Statistical analyses were performed using SPSS version 27.0.Results::Abnormal renal development was identified in 141 patients, among whom 26 exhibited hyperechogenic kidneys (HCK). Of these, 12 cases were associated with 17q12 microdeletion syndrome, while the remaining 14 were linked to other chromosomal abnormalities. When excluding patients with HCK, those diagnosed with polycystic kidney disease demonstrated a higher prevalence of chromosomal abnormalities compared to those with multicystic dysplastic kidney and renal dysplasia. Although isolated conditions such as horseshoe kidney, hydronephrosis, ectopic kidney, and unilateral kidney typically presented with normal chromosomal findings, the incidence of chromosomal abnormalities increased when these conditions coexisted with other anomalies. A detailed analysis of the correlation between 17q12 microdeletion syndrome and HCK revealed that 12 out of the 14 patients diagnosed with 17q12 microdeletion syndrome exhibited HCK. Genetic testing confirmed the syndrome in seven patients, with five cases attributed to novel mutations and two cases resulting from inherited mutations.Conclusion::Fetal HCK was closely associated with the 17q12 microdeletion syndrome, and polycystic kidney disease showed a higher rate of chromosomal abnormalities. Chromosome test results were mostly normal in patients with other renal abnormalities, such as kidney dysplasia, horseshoe kidneys, hydronephrosis, kidney deficiency, and ectopic kidneys. Prenatal diagnosis is recommended, especially in cases of non-isolated fetal renal abnormalities. This study provides strong evidence supporting a link between fetal renal abnormalities and genetic syndromes.

Objective::To analyze fetal renal abnormality genetic features and the prenatal characteristics of the 17q12 microdeletion syndrome.Methods::This prospective cohort study examined prenatal ultrasound findings of renal abnormalities in pregnant women who underwent single nucleotide polymorphism (SNP) array or copy number variation sequencing (CNV-seq) testing on amniotic fluid or fetal tissue at Tianjin Central Obstetrics and Gynecology Hospital between January 2016 and August 2022. The study cohort comprised women with advanced maternal age, fetal ultrasound anomalies, high-risk non-invasive prenatal testing results, or suspected 17q12 microdeletion syndrome. Comprehensive clinical data, including maternal age, detailed ultrasound findings, and pregnancy outcomes, were systematically collected. SNP-array analysis was conducted using an Affymetrix CytoScan 750 K Array Chip to identify CNVs and loss of heterozygosity, while CNV-seq was performed on the Illumina HiSeq 2000 platform. Detected variants were classified according to the American College of Medical Genetics and Genomics guidelines. Statistical analyses were performed using SPSS version 27.0.Results::Abnormal renal development was identified in 141 patients, among whom 26 exhibited hyperechogenic kidneys (HCK). Of these, 12 cases were associated with 17q12 microdeletion syndrome, while the remaining 14 were linked to other chromosomal abnormalities. When excluding patients with HCK, those diagnosed with polycystic kidney disease demonstrated a higher prevalence of chromosomal abnormalities compared to those with multicystic dysplastic kidney and renal dysplasia. Although isolated conditions such as horseshoe kidney, hydronephrosis, ectopic kidney, and unilateral kidney typically presented with normal chromosomal findings, the incidence of chromosomal abnormalities increased when these conditions coexisted with other anomalies. A detailed analysis of the correlation between 17q12 microdeletion syndrome and HCK revealed that 12 out of the 14 patients diagnosed with 17q12 microdeletion syndrome exhibited HCK. Genetic testing confirmed the syndrome in seven patients, with five cases attributed to novel mutations and two cases resulting from inherited mutations.Conclusion::Fetal HCK was closely associated with the 17q12 microdeletion syndrome, and polycystic kidney disease showed a higher rate of chromosomal abnormalities. Chromosome test results were mostly normal in patients with other renal abnormalities, such as kidney dysplasia, horseshoe kidneys, hydronephrosis, kidney deficiency, and ectopic kidneys. Prenatal diagnosis is recommended, especially in cases of non-isolated fetal renal abnormalities. This study provides strong evidence supporting a link between fetal renal abnormalities and genetic syndromes.

Depression has a high prevalence rate and often leads to severe social impairment. The dilemma of clinical diagnosis and treatment of major depressive disorder, especially in adolescents, is still a major challenge in psychiatric clinical practice. The affective symptoms serve as the primary symptom criteria for the diagnosis of major depressive disorder, but an increasing number of studies have proven that cognitive dysfunction is also one of the core symptoms of major depressive disorder. Both affective symptoms and cognitive impairment play an equally important role in the clinical manifestations and the progression. Moreover, there is a close and complex association between affective symptoms and cognitive impairment. The comprehension of the intricate interplay between cognitive dysfunction and emotional symptoms among patients with major depressive disorder is crucial for guiding clinical assessment, intervention, and overall prognosis improvement.

Objective:To further improve the understanding of paroxysmal nocturnal hemoglobinuria (PNH), we retrospectively analyzed and summarized the clinical characteristics, treatment status, and survival status of patients with PNH in Zhejiang Province.Methods:This study included 289 patients with PNH who visited 20 hospitals in Zhejiang Province. Their clinical characteristics, comorbidity, laboratory test results, and medications were analyzed and summarized.Results:Among the 289 patients with PNH, 148 males and 141 females, with a median onset age of 45 (16-87) years and a peak onset age of 20-49 years (57.8% ). The median lactic dehydrogenase (LDH) level was 1 142 (604-1 925) U/L. Classified by type, 70.9% (166/234) were classical, 24.4% (57/234) were PNH/bone marrow failure (BMF), and 4.7% (11/234) were subclinical. The main clinical manifestations included fatigue or weakness (80.8%, 235/289), dizziness (73.4%, 212/289), darkened urine color (66.2%, 179/272), and jaundice (46.2%, 126/270). Common comorbidities were hemoglobinuria (58.7% ), renal dysfunction (17.6% ), and thrombosis (15.0% ). Moreover, 82.3% of the patients received glucocorticoid therapy, 70.9% required blood transfusion, 30.7% used immunosuppressive agents, 13.8% received anticoagulant therapy, and 6.3% received allogeneic hematopoietic stem cell transplantation. The 10-year overall survival (OS) rate was 84.4% (95% CI 78.0% -91.3% ) . Conclusion:Patients with PNH are more common in young and middle-aged people, with a similar incidence rate between men and women. Common clinical manifestations include fatigue, hemoglobinuria, jaundice, renal dysfunction, and recurrent thrombosis. The 10-year OS of this group is similar to reports from other centers in China.

Objective:To evaluate the efficacy and safety of hypomethylating agents (HMA) in patients with myelodysplastic syndromes (MDS) .Methods:A total of 409 MDS patients from 45 hospitals in Zhejiang province who received at least four consecutive cycles of HMA monotherapy as initial therapy were enrolled to evaluate the efficacy and safety of HMA. Mann-Whitney U or Chi-square tests were used to compare the differences in the clinical data. Logistic regression and Cox regression were used to analyze the factors affecting efficacy and survival. Kaplan-Meier was used for survival analysis. Results:Patients received HMA treatment for a median of 6 cycles (range, 4-25 cycles) . The complete remission (CR) rate was 33.98% and the overall response rate (ORR) was 77.02%. Multivariate analysis revealed that complex karyotype ( P=0.02, OR=0.39, 95% CI 0.18-0.84) was an independent favorable factor for CR rate. TP53 mutation ( P=0.02, OR=0.22, 95% CI 0.06-0.77) was a predictive factor for a higher ORR. The median OS for the HMA-treated patients was 25.67 (95% CI 21.14-30.19) months. HMA response ( P=0.036, HR=0.47, 95% CI 0.23-0.95) was an independent favorable prognostic factor, whereas complex karyotype ( P=0.024, HR=2.14, 95% CI 1.10-4.15) , leukemia transformation ( P<0.001, HR=2.839, 95% CI 1.64-4.92) , and TP53 mutation ( P=0.012, HR=2.19, 95% CI 1.19-4.07) were independent adverse prognostic factors. There was no significant difference in efficacy and survival between the reduced and standard doses of HMA. The CR rate and ORR of MDS patients treated with decitabine and azacitidine were not significantly different. The median OS of patients treated with decitabine was longer compared with that of patients treated with azacitidine (29.53 months vs 20.17 months, P=0.007) . The incidence of bone marrow suppression and pneumonia in the decitabine group was higher compared with that in the azacitidine group. Conclusion:Continuous and regular use of appropriate doses of hypomethylating agents may benefit MDS patients to the greatest extent if it is tolerated.

Depression has a high prevalence rate and often leads to severe social impairment. The dilemma of clinical diagnosis and treatment of major depressive disorder, especially in adolescents, is still a major challenge in psychiatric clinical practice. The affective symptoms serve as the primary symptom criteria for the diagnosis of major depressive disorder, but an increasing number of studies have proven that cognitive dysfunction is also one of the core symptoms of major depressive disorder. Both affective symptoms and cognitive impairment play an equally important role in the clinical manifestations and the progression. Moreover, there is a close and complex association between affective symptoms and cognitive impairment. The comprehension of the intricate interplay between cognitive dysfunction and emotional symptoms among patients with major depressive disorder is crucial for guiding clinical assessment, intervention, and overall prognosis improvement.

Objective:To explore the speech features of female patients with anhedonic depression and their recognition of pleasure deficient symptoms.Methods:A total of 102 female depression patients who were hospitalized at Nanjing Brain Hospital from September 2020 to October 2021 were selected, including 62 anhedonic depression patients (anhedonic group) and 40 non-anhedonic depression patients (non-anhedonic group). A total of 50 female healthy controls were recruited during the same period.All participants were evaluated by the 17-item Hamilton depression scale (HAMD-17), Snaith-Hamilton pleasure scale (SHAPS), and the temporal experience of pleasure scale (TEPS), as well as voice acquisition.SPSS 23.0 software was used for data processing.Statistical analysis was conducted using one-way ANOVA, non-parametric tests, Logistic regression, and receiver operating characteristic curve.Results:Compared with the non-anhedonic group, the anhedonic group showed significant changes in 15 voice features(all P<0.05), including Mel-frequency cepstral coefficients, formant frequencies, intensity, and energy features.Among these features, Mel-frequency cepstral coefficients exhibited the highest accuracy in identifying anhedonic depression, with sensitivity of 47.5%, specificity of 91.9%, area under curve (AUC) of 0.751, 95% CI=0.686-0.866.Formant frequencies could identify female anhedonic depression, with a sensitivity of 90.0%, a specificity of 40.3%, an AUC of 0.647, and 95% CI=0.605-0.824.Energy features could identify anhedonic deficient depression, with a sensitivity of 60.0%, a specificity of 74.2%, an AUC of 0.679, and 95% CI=0.587-0.804.Intensity features could identify female anhedonic depression, with a sensitivity of 70.0%, a specificity of 58.1%, an AUC of 0.640, and 95% CI=0.554-0.769. Conclusion:Mel-frequency cepstral coefficients, formant frequencies, intensity features, and energy features may have specific changes in female patients with anhedonic depression.The Mel-frequency cepstral coefficients has the highest recognition accuracy for anhedonic symptoms in female depression patients, and is expected to become an objective evaluation index for female anhedonic depression.

Objective:Construction of deep learning classification models based on functional magnetic resonance imaging (fMRI) data assists the clinicians to achieve better diagnosis of bipolar disorder (BD), which can improve the recognition rate of BD by identifying the critical imaging features.Methods:A total of 146 patients who met the diagnosis criteria of BD according to DSM-Ⅳ and 234 healthy control (HC) were recruited for fMRI scans. Regional homogeneity (ReHo) and amplitude of low frequency fluctuation (ALFF) were used to analyze fMRI data. Based on ReHo and ALFF, the classification models were constructed by deeping neural network (DNN) and dual-channel convolution neural network (DCNN) respectively, and the best classification model was developed by comparing the accuracy and area under curve (AUC) of the two models. Based on each brain region divided by anatomical automatic labeling (AAL), the support vector machine (SVM) classification model was constructed using imaging index with a better performance, and the critical imaging features were identified by comparing the accuracy of each brain region.Results:The performances of the DCNN classification model (accuracy = 75.3%, and 72.6%, respectively, based on ReHo and ALFF) were significantly better than the DNN classification model (accuracy = 67.1%, and 65.1%, respectively). Meanwhile, the accuracy of classification model constructed using ReHo was higher than ALFF. Based on the SVM classification model, critical brain regions were identified above the accuracy of 65.0%, including the occipital lobe (middle occipital gyrus, superior occipital gyrus and lingual gyrus), hippocampus, and thalamus.Conclusion:The computational model based on DCNN using ReHo can help the clinicians to achieve better diagnosis of BD. Furthermore, occipital lobe, hippocampus and thalamus may be the critical imaging features for the auxiliary recognition of BD.

Objective:Construction of deep learning classification models based on functional magnetic resonance imaging (fMRI) data assists the clinicians to achieve better diagnosis of bipolar disorder (BD), which can improve the recognition rate of BD by identifying the critical imaging features.Methods:A total of 146 patients who met the diagnosis criteria of BD according to DSM-Ⅳ and 234 healthy control (HC) were recruited for fMRI scans. Regional homogeneity (ReHo) and amplitude of low frequency fluctuation (ALFF) were used to analyze fMRI data. Based on ReHo and ALFF, the classification models were constructed by deeping neural network (DNN) and dual-channel convolution neural network (DCNN) respectively, and the best classification model was developed by comparing the accuracy and area under curve (AUC) of the two models. Based on each brain region divided by anatomical automatic labeling (AAL), the support vector machine (SVM) classification model was constructed using imaging index with a better performance, and the critical imaging features were identified by comparing the accuracy of each brain region.Results:The performances of the DCNN classification model (accuracy = 75.3%, and 72.6%, respectively, based on ReHo and ALFF) were significantly better than the DNN classification model (accuracy = 67.1%, and 65.1%, respectively). Meanwhile, the accuracy of classification model constructed using ReHo was higher than ALFF. Based on the SVM classification model, critical brain regions were identified above the accuracy of 65.0%, including the occipital lobe (middle occipital gyrus, superior occipital gyrus and lingual gyrus), hippocampus, and thalamus.Conclusion:The computational model based on DCNN using ReHo can help the clinicians to achieve better diagnosis of BD. Furthermore, occipital lobe, hippocampus and thalamus may be the critical imaging features for the auxiliary recognition of BD.