Objective:To evaluate the effects and underlying mechanisms of metabolites derived from the kidney-reinforcing, blood circulation-activating, and collateral dredging decoction on the proliferation of multiple myeloma (MM) KM3 cells.Methods:MM KM3 cells in the logarithmic growth phase were treated with 3%, 6%, 9%, or 12% metabolites of kidney-reinforcing, blood circulation-activating, and collateral dredging decoction. Cell viability was assessed using the CCK-8 assay. Apoptosis and necrosis were evaluated using flow cytometry and TUNEL staining. Mitochondrial and cellular ultrastructural changes were examined using transmission electron microscopy. mRNA and protein expression levels of dynamin-related protein 1 (Drp1), mitochondrial fission protein 1 (Fis1), mitochondrial fission factor (MFF), PTEN-induced kinase 1 (Pink1), and E3 ubiquitin ligase (Parkin) were determined through quantitative real-time PCR and western blotting. High-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) combined with network pharmacology, was utilized for reverse verification of the pharmacodynamic mechanisms and therapeutic targets underlying the anti-MM activity of this decoction.Results:The metabolites of the kidney-reinforcing, blood circulation-activating, and collateral dredging decoction inhibited KM3 cell proliferation and induced apoptosis in a dose-dependent manner. Transmission electron microscopy revealed increased mitochondrial fission and autophagic structures, with effects intensifying at higher metabolite concentrations. mRNA and protein expression of Drp1, Fis1, MFF, Pink1, and Parkin were significantly upregulated in treatment groups compared to controls ( P<0.05), with the most pronounced effects observed in the 12% metabolite group ( P<0.01). HPLC-MS/MS identified 121 bioactive compounds in BHTF, which shared 474 overlapping targets with MM. Enrichment analysis suggested that BHTF exerts antitumor effects primarily through apigenin, palmatine, and other key components by modulating TNF, NF-κB, and mitophagy pathways. Conclusion:The kidney-reinforcing and blood circulation-activating and collateral dredging decoction suppresses the proliferation of MM KM3 cells, potentially through mechanisms involving the regulation of mitochondrial dynamics and induction of autophagy.

Objective:To explore the value of deep learning models based on ultrafast dynamic contrast-enhanced MRI (UF-DCE MRI) in predicting malignant breast lesions.Methods:The study was a cross-sectional study. Clinical and imaging data of 347 patients with breast lesions who received treatment at Tianjin Medical University Cancer Institute and Hospital from March 2023 to January 2024 were analyzed retrospectively. A total of 347 lesions were observed in the 347 patients, including 75 benign and 272 malignant lesions. The random number method was used to divide into the training set with 243 cases and the validation set with 104 cases in a ratio of 7∶3. All patients underwent breast UF-DCE MRI and conventional dynamic-enhanced MRI (DCE-MRI). A 27-channel model (27-phase enhancement images of input UF-DCE MRI), a 3-channel model (3-phase enhancement images of input DCE-MRI), and a 1-channel model (1st-phase enhancement images of DCE-MRI) were built based on the pre-trained ResNet18 deep learning model on ImageNet. The efficacy of each model in predicting breast malignant lesions was analyzed using receiver operating characteristic curves and area under the curve (AUC). The differences of AUC were compared using DeLong test.Results:In the training and validation sets, the 27-channel model had the highest AUC for diagnosing malignant breast lesions, which were 0.848 (95% CI 0.818-0.877) and 0.784 (95% CI 0.752-0.817), respectively. DeLong test showed no statistically significant difference in the AUC values of the three models in the validation set for the diagnosis of malignant lesions of the breast in a two-by-two comparison ( P>0.05). UF-DCE MRI scans were 27 phases totaling 81 s with a temporal resolution of 3 s/phase; DCE-MRI scans were 3 phases totaling 270 s with a temporal resolution of 90 s/phase. Conclusions:The model combining UF-DCE MRI with deep learning demonstrates comparable efficacy to DCE-MRI deep learning model in diagnosing breast malignant lesions. However the UF-DCE MRI has the advantages of high temporal resolution and short scanning time, which makes this model valuable for precise diagnosis and treatment of breast cancer.

The occurrence and development of type 2 diabetes mellitus(T2DM)are closely as-sociated with defects in insulin secretion.Synaptosomal-associated protein 25(SNAP25),as a core component of the soluble N-ethylmaleimide-sensitive factor attachment protein receptor(SNARE)complex,directly mediates the fusion of insulin secretion granules with the cell membrane and regu-lates the dynamic balance of insulin secretion stimulated by glucose.The expression defect of SNAP25 in patients with T2DM and model animals directly leads to vesicle fusion disorder,constituting the core pathological link of insulin secretion disorder and exacerbating the deterioration of β-cell func-tion.SNAP25 may serve as a key hub for multi-target synergistic intervention,and its genetic poly-morphism and the plasticity of its regulatory network offer novel strategies for precision therapy.This article innovatively integrated multidimensional regulatory mechanisms,including calcium channel ac-tivity,G-protein-coupled signaling and epigenetic modifications,to systematically analyze the spatio-temporal-specific regulatory network of SNAP25 in insulin secretion,providing a theoretical basis for T2DM therapeutic strategies targeting vesicle fusion.

Objective:To evaluate the effects and underlying mechanisms of metabolites derived from the kidney-reinforcing, blood circulation-activating, and collateral dredging decoction on the proliferation of multiple myeloma (MM) KM3 cells.Methods:MM KM3 cells in the logarithmic growth phase were treated with 3%, 6%, 9%, or 12% metabolites of kidney-reinforcing, blood circulation-activating, and collateral dredging decoction. Cell viability was assessed using the CCK-8 assay. Apoptosis and necrosis were evaluated using flow cytometry and TUNEL staining. Mitochondrial and cellular ultrastructural changes were examined using transmission electron microscopy. mRNA and protein expression levels of dynamin-related protein 1 (Drp1), mitochondrial fission protein 1 (Fis1), mitochondrial fission factor (MFF), PTEN-induced kinase 1 (Pink1), and E3 ubiquitin ligase (Parkin) were determined through quantitative real-time PCR and western blotting. High-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) combined with network pharmacology, was utilized for reverse verification of the pharmacodynamic mechanisms and therapeutic targets underlying the anti-MM activity of this decoction.Results:The metabolites of the kidney-reinforcing, blood circulation-activating, and collateral dredging decoction inhibited KM3 cell proliferation and induced apoptosis in a dose-dependent manner. Transmission electron microscopy revealed increased mitochondrial fission and autophagic structures, with effects intensifying at higher metabolite concentrations. mRNA and protein expression of Drp1, Fis1, MFF, Pink1, and Parkin were significantly upregulated in treatment groups compared to controls ( P<0.05), with the most pronounced effects observed in the 12% metabolite group ( P<0.01). HPLC-MS/MS identified 121 bioactive compounds in BHTF, which shared 474 overlapping targets with MM. Enrichment analysis suggested that BHTF exerts antitumor effects primarily through apigenin, palmatine, and other key components by modulating TNF, NF-κB, and mitophagy pathways. Conclusion:The kidney-reinforcing and blood circulation-activating and collateral dredging decoction suppresses the proliferation of MM KM3 cells, potentially through mechanisms involving the regulation of mitochondrial dynamics and induction of autophagy.

Objective:To explore the value of deep learning models based on ultrafast dynamic contrast-enhanced MRI (UF-DCE MRI) in predicting malignant breast lesions.Methods:The study was a cross-sectional study. Clinical and imaging data of 347 patients with breast lesions who received treatment at Tianjin Medical University Cancer Institute and Hospital from March 2023 to January 2024 were analyzed retrospectively. A total of 347 lesions were observed in the 347 patients, including 75 benign and 272 malignant lesions. The random number method was used to divide into the training set with 243 cases and the validation set with 104 cases in a ratio of 7∶3. All patients underwent breast UF-DCE MRI and conventional dynamic-enhanced MRI (DCE-MRI). A 27-channel model (27-phase enhancement images of input UF-DCE MRI), a 3-channel model (3-phase enhancement images of input DCE-MRI), and a 1-channel model (1st-phase enhancement images of DCE-MRI) were built based on the pre-trained ResNet18 deep learning model on ImageNet. The efficacy of each model in predicting breast malignant lesions was analyzed using receiver operating characteristic curves and area under the curve (AUC). The differences of AUC were compared using DeLong test.Results:In the training and validation sets, the 27-channel model had the highest AUC for diagnosing malignant breast lesions, which were 0.848 (95% CI 0.818-0.877) and 0.784 (95% CI 0.752-0.817), respectively. DeLong test showed no statistically significant difference in the AUC values of the three models in the validation set for the diagnosis of malignant lesions of the breast in a two-by-two comparison ( P>0.05). UF-DCE MRI scans were 27 phases totaling 81 s with a temporal resolution of 3 s/phase; DCE-MRI scans were 3 phases totaling 270 s with a temporal resolution of 90 s/phase. Conclusions:The model combining UF-DCE MRI with deep learning demonstrates comparable efficacy to DCE-MRI deep learning model in diagnosing breast malignant lesions. However the UF-DCE MRI has the advantages of high temporal resolution and short scanning time, which makes this model valuable for precise diagnosis and treatment of breast cancer.

Objective:To explore the efficacy and safety of immunoneoadjuvant therapy with pembrolizumab combined with chemotherapy in locally advanced resectable hypopharyngeal squamous cell carcinoma patients.Methods:This study was a prospective, single arm, single center clinical study that was opened for enrollment in April 2021. Patients who met the inclusion criteria at the Cancer Hospital of the Chinese Academy of Medical Sciences were treated with neoadjuvant therapy of pembrolizumab combined with cisplatin and paclitaxel, and after treatments, received surgery and postoperative adjuvant therapy. The main endpoint of this study was postoperative pathological complete response (pCR), and other observations included adverse reactions and long-term prognoses of patients after neoadjuvant therapy.Results:By September 2023, a total of 23 patients who underwent neoadjuvant therapy and surgery were enrolled in the study and all patients were males aged 49-74 years. All patients were locally advanced stage, including 3 patients in stage Ⅲ and 20 patients in stage Ⅳ. There were 12 cases of primary lesions with posterior ring involvement accompanied by fixation of one vocal cord and 20 cases of regional lymph node metastases classified as N2. Eighteen cases received a two cycle regimen and 5 cases received a three cycle regimen for neoadjuvant therapy. The postoperative pCR rate was 26.1% (6/23), with no surgical delay caused by adverse drug reactions. The laryngeal preservation rate was 87.0% (20/23). Pharyngeal fistula was the main surgical complication, with an incidence of 21.7% (5/23). The median follow-up time was 15 months, and 3 patients experienced local recurrence.Conclusions:The immunoneoadjuvant therapy of pembrolizumab combined with chemotherapy has a high pCR rate in locally advanced resectable hypopharyngeal squamous cell carcinoma, with increased laryngeal preservation rate and no significant impact on surgical safety.

【Objective】 To analyze the genetic variation characteristics and clinical phenotypes of a family with primary microcephaly (MCPH) caused by RTTN gene variation, and to provide reference for genetic counseling and prenatal diagnosis. 【Methods】 Clinical data of the three patients (including 2 fetuses and 2-year-old proband,and one fetus with clinical diagnosis) and their parents were collected and analyzed. Two of the children and their parents were tested by trio whole exome sequencing (trio-WES), sanger sequencing validation sites, and the hazard of their compound heterozygous variants was predicted. Literature review was conducted through domestic and international databases to collect reported RTTN gene mutation cases. 【Results】 Three patients in this family had anomalies of the septum pellucidum, hypoplasia of the corpus callosum and other brain malformations during fetal period. The proband (G2) and fetus (G3) showed intrauterine growth retardation and MCPH in late pregnancy; besides, G2 was born with global developmental delay. Trio-WES detected a c.2101(exon16)C>T(p.Arg701Ter,1526) nonsense and a c.2863(exon22)G>A(p.Glu955Lys)missense in the RTTN gene of G2 and G3, which were inherited from their father and mother, forming a compound heterozygous variant. According to the American College of Medical Genetics and Genomics (ACMG) variant classification guidelines, two variants were likely to be pathogenic (LP) and uncertain significance (VUS). Among them, c.2863(exon22)G>A was a newly discovered missense, which was predicted by the software to be harmful to the gene product. 【Conclusions】 Complex heterozygous variations of RTTN gene (c.2101C>T and c.2863G>A) are the genetic cause of MCPH in this family. This report has enriched the variation spectrum of RTTN gene, provided guidance for prenatal diagnosis and reproduction of this family, as well as material and reference for further understanding of the diseases caused by this gene mutation.

This study established a school-supervisor-postgraduate "three-dimensional interaction" mode of academic norms and ethics education for medical postgraduates based on their training characteristics. The mode was evaluated and improved in practice. The results showed that after the implementation of the mode, graduate students significantly improved their awareness of how to standardize the implementation of experiments [(79.86% vs. 89.47%); P=0.021] and how to write formal experimental records [(84.72% vs. 92.76%); P=0.028]. In addition, there were significant increases in the pass rate of pre defense (85.42% vs. 94.08%; P=0.014) and blind review (84.03% vs. 93.42%; P=0.010). The "three-dimensional interaction" mode truly helps to achieve a win-win-win situation between postgraduate supervisors and the school, and is worth further practice and promotion.

Objective To investigate the correlations of serum Apelin-13 and fatty acid binding protein 4 (FABP4) levels with metabolic and bone metabolic indicators in postmenopausal women with different bone mass. Methods A total of 145 postmenopausal women were selected as subjects and divided into three groups based on bone mineral density (BMD) test results: normal bone mass group(49 cases), osteopenia (ON) group(51 cases), and osteoporosis (OP) group(45 cases). Serum Apelin-13, FABP4 levels, bone metabolic indicators, and biochemical indicators were measured and compared among the three groups. Spearman correlation analysis was used to analyze the correlations of Apelin-13, FABP4, and other indicators with BMD. Multivariate Logistic regression analysis was performed to analyze the risk factors for OP, and the receiver operating characteristic (ROC) curve was plotted to analyze the predictive value of serum Apelin-13 for postmenopausal osteoporosis (PMOP). Results The serum Apelin-13 level in the OP group was lower than that in the ON group and the normal bone mass group (P < 0.05). No significant difference in serum FABP4 levels was found among the three groups (P>0.05). The levels of serum parathyroid hormone (PTH), alkaline phosphatase (ALP), type Ⅰ procollagen amino-terminal propeptide (PⅠNP), type Ⅰ collagen cross-linked C-terminal peptide (CTXⅠ), and bone-specific alkaline phosphatase (BALP) in the OP group were higher than those in the ON group and the normal bone mass group (P < 0.05). Lumbar post-menopause BMD was positively correlated with serum Apelin-13 levels (P < 0.05), but had no correlation with serum FABP4 levels (P>0.05). Lumbar BMD was negatively correlated with serum PTH, ALP, PⅠNP, CTXⅠ, BALP, and age (P < 0.05), but positively correlated with body weight, body mass index, T-score, fasting insulin, and insulin resistance index (P < 0.05). Multivariate Logistic regression analysis showed that serum Apelin-13, PTH, ALP, PⅠNP, CTXⅠ, and BALP levels were independent factors influencing the occurrence of OP in postmenopausal women (P < 0.05). ROC curve results showed that the optimal cut-off value of serum Apelin-13 for predicting PMOP was 18.51 pg/mL, with an area under the curve of 0.716, a sensitivity of 70.0%, and a specificity of 64.4%. Conclusion Apelin-13 is lowly expressed in the serum of PMOP patients, and its expression level is closely related to lumbar BMD, which may serve as an early screening indicator and potential therapeutic target for PMOP.

Objective::This study investigates the efficacy of analyzing fetal heart rate (FHR) signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through electronic fetal monitoring during labor.Methods::A total of 43,888 cardiotocograph(CTG) records of female patients in labor from January 2012 to December 2020 were collected from the NanFang Hospital of Southern Medical University. After filtering the data, 2341 FHR records were used for the study. The ObVue fetal monitoring system, manufactured by Lian-Med Technology Co. Ltd., was used to monitor the FHR signals for these pregnant women from the beginning of the first stage of labor to the end of delivery. Two obstetric experts together annotated the FHR signals in the system to determine the baseline as well as accelerations/decelerations of the FHR. Our cardiotocograph network (CTGNet) as well as traditional methods were then used to automatically analyze the baseline and acceleration/deceleration of the FHR signals. The results of calculations were compared with the annotations provided by the obstetric experts, and ten-fold cross-validation was applied to evaluate them. The root-mean-square difference (RMSD) between the baselines, acceleration F-measure (Acc.F-measure), deceleration F-measure (Dec.F-measure), coefficient of synthetic inconsistency (SI) and the morphological analysis discordance index (MADI) were used as evaluation metrics. The data were analyzed by using a paired t-test. Results::The proposed CTGNet was superior to the best traditional method, proposed by Mantel, in terms of the RMSD.BL (1.7935 ± 0.8099 vs. 2.0293 ± 0.9267, t=-3.55 , P=0.004), Acc.F-measure (86.8562 ± 10.9422 vs. 72.2367 ± 14.2096, t= 12.43, P <0.001), Dec.F-measure (72.1038 ± 33.2592 vs. 58.5040 ± 38.0276, t= 4.10, P <0.001), SI (34.8277±20.9595 vs. 54.8049 ± 25.0265, t=-9.39, P <0.001), and MADI (3.1741 ± 1.9901 vs. 3.7289 ± 2.7253, t= -2.74, P= 0.012). The proposed CTGNet thus had significant advantages over the best traditional method on all evaluation metrics. Conclusion::The proposed Artificial Intelligence-based method CTGNet delivers good performance in terms of the automatic analysis of FHR based on cardiotocograph data. It promises to be a key component of smart obstetrics systems of the future.