Myocardial fibrosis (MF) is a common pathological hallmark of cardiovascular diseases, reflecting shared mechanisms in their progression. However, the lack of reliable MF models that accurately mimic its pathogenesis has hindered drug discovery, highlighting the urgent need for more effective therapeutic agents. Herein, a novel contractile three-dimensional (3D) myocardial tissue model integrating cardiomyocytes, cardiac-fibroblasts, and bone marrow-derived macrophages in collagen hydrogel was developed to simulate the fibrotic changes of cardiovascular disease, and facilitate the screening of anti-MF compounds. The 3D myocardial tissue model exhibited precise, visualizable, and quantifiable contractile characteristics under hypoxia and drug interventions. 76 compounds extracted from the resins of Toxicodendron vernicifluum, a traditional Chinese medicine with clear clinical benefits for fibrotic diseases, were screened for anti-fibrotic activity. Using an in vitro 3D oxygen-glucose deprivation (OGD)-treated myocardial tissue model instead of a two-dimensional transforming growth factor-β treated cardiac-fibroblasts model, two candidates including LQ-40 and SQ-3 exert impressive anti-MF activity, which was further validated in left anterior descending coronary artery ligation-induced MF mouse model. The current results demonstrate the feasibility and advantage of the novel contractile 3D tissue model with multi-cell types in discovering candidates for MF, further stressing the great potential of regulating macrophages in the treatment of MF.

ObjectiveTo investigate the potential targets and mechanisms of Draconis Sanguis (DS), a valuable traditional Chinese medicine derived from the resin of the palm tree Daemonorops draco Bl (D. Sanguis, Xue Jie), in the treatment of myocardial infarction (MI).MethodsWe explored the potential mechanisms of DS in the treatment of MI using network pharmacology, bioinformatic techniques, and transcriptomic analysis, followed by validation through in vivo and in vitro experiments.ResultsNetwork pharmacology and bioinformatic analyses identified five genes (Fpr1, Glul, Mme, Mmp9, and Pla2g7) as potential targets for MI treatment. Moreover, DS significantly ameliorated cardiac function, inflammatory responses, and MI-induced myocardial fibrosis in vivo. Transcriptomic and bioinformatic analyses identified Pla2g7 as the most critical target in the DS treatment of MI. Molecular docking revealed that the key active ingredient in DS has a strong affinity for this gene. Furthermore, DS reduced the expression of Pla2g7 (P = .0009), NLRP3 (P = .003), interleukin-18 (P .001), and interleukin-1β (P = .004) mRNAs in vivo.ConclusionsThe results indicate that DS can downregulate the expression of Pla2g7 and reduce the inflammatory response. This demonstrates the potential therapeutic target of DS and the mechanism underlying its cardioprotective effects.

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.

In recent years, the application of minimal residual disease (MRD) in solid tumors has gained widespread attention. MRD typically refers to the presence of residual cancer cells that remain undetectable by imaging after curative treatments, such as surgical resection. The presence of MRD post-surgery is significantly associated with an increased risk of tumor recurrence. In colorectal cancer, circulating tumor DNA (ctDNA) serves as an effective marker for assessing MRD, particularly in non-metastatic (stages I-III) colorectal cancer. As a real-time, accurate, and convenient biomarker, ctDNA can effectively predict tumor recurrence, guide postoperative adjuvant chemotherapy decisions, and provide crucial information for recurrence monitoring. The application prospects of ctDNA detection technology are vast, promising more precise and individualized treatment plans for colorectal cancer patients. This article comprehensively analyzes the progress in the application of ctDNA for detecting MRD in non-metastatic colorectal cancer patients, elaborates on its guiding role in clinical treatment decisions, and envisions the future development directions in this field.

In recent years, the application of minimal residual disease (MRD) in solid tumors has gained widespread attention. MRD typically refers to the presence of residual cancer cells that remain undetectable by imaging after curative treatments, such as surgical resection. The presence of MRD post-surgery is significantly associated with an increased risk of tumor recurrence. In colorectal cancer, circulating tumor DNA (ctDNA) serves as an effective marker for assessing MRD, particularly in non-metastatic (stages I-III) colorectal cancer. As a real-time, accurate, and convenient biomarker, ctDNA can effectively predict tumor recurrence, guide postoperative adjuvant chemotherapy decisions, and provide crucial information for recurrence monitoring. The application prospects of ctDNA detection technology are vast, promising more precise and individualized treatment plans for colorectal cancer patients. This article comprehensively analyzes the progress in the application of ctDNA for detecting MRD in non-metastatic colorectal cancer patients, elaborates on its guiding role in clinical treatment decisions, and envisions the future development directions in this field.

Objective:To summarize the distributional characteristics of postoperative occurrence of multi-drug resistant organism (MDRO) infections and their risk factors in simultaneous pancreas-kidney transplantation (SPK) recipients and examine the impact of MDRO infections on the survival of SPK recipients.Method:From January 2016 to December 2022, the relevant clinical data were retrospectively reviewed for 218 SPK recipients. The source of donor-recipient specimens and the composition percentage of MDRO pathogens were examined. According to whether or not MDRO infection occurred post-transplantation, they were assigned into two groups of MDRO (98 cases) and non-MDRO (120 cases). The clinical data of two groups of donors and recipients were analyzed. And the risk factors for an onset of MDRO infection were examined by binary Logistic regression. The survival rate of two recipient groups was compared by Kaplan-Meier method.Result:A total of 98/218 recipients (45%) developed MDRO infections. And 46 (46.9%) of sputum and 34 (34.7%) of urine were cultured positively and 49 (50%) pathogens expressed extended spectrum beta-lactamase. There were pneumonia (46 cases, 46.9%), urinary tract infections (34 cases, 34.7%), abdominal infections (16 cases, 16.3%) and bloodstream infections (2 cases, 2.0%). Univariate regression analysis revealed that length of renal failure ( P=0.037), length of hospitalization ( P<0.001), length of antibiotic use ( P<0.001), novel antibiotics ( P=0.014), albumin ( P<0.001) and leukocyte count ( P<0.001) were risk factors for an onset of MDRO infections. The results of multifactorial regression indicated that low albumin ( OR=0.855, 95% CI: 0.790~0.925, P<0.001) and leukopenia ( OR=0.656, 95% CI: 0.550~0.783, P<0.001) were independent risk factors for an onset of MDRO infections. The survival rates of recipients in MDRO group at Year 1/3 post-operation were 92.9% (91/98) and 89.8% (88/98). And the survival rate of recipients in non-MDRO group was 96.7% (116/120) at Year 1/3 post-operation. Inter-group difference was not statistically significant in 1-year survival rate of two recipient groups ( P=0.201); statistically significant inter-group difference in 3-year survival rate between two recipient groups ( P=0.041) . Conclusion:Low albumin and leukopenia are risk factors for MDRO infection. Infection with MDRO has some impact on the survival of recipients.

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.