BackgroundMany studies have shown that patients with depressive disorder show impairments in prospective memory， while it is deemed necessary for facilitating their social reintegration， and the current research findings are inconsistent regarding the factors affecting prospective memory of patients with depressive disorder. ObjectiveTo explore the impact of target characteristics （emotional valence and salience） on prospective memory in patients with depressive disorder， so as to provide references for the training and recovery of prospective memory function in these patients. MethodsFrom January to December 2022， 53 patients with depressive disorder were recruited from the outpatient department of Inner Mongolia Autonomous Mental Health Center. Meanwhile， 45 healthy individuals were concurrently recruited from surrounding communities as control group. An experiment with a 2 （participant type： depressive disorder， healthy control） ×2 （target salience： salient， non-salient） ×3 （emotional valence： positive， neutral， negative） factorial design was conducted. The positive/neutral/negative emotional pictures from Chinese Affective Picture System （CAPS） were used for emotional stimulation. A dual-task experimental paradigm was adopted， and the response time and accuracy in prospective memory task and ongoing task were recorded for participants with different target characteristics. Results①In the prospective memory task， the main effect of participant type was statistically significant， with the depressive group showing lower accuracy （F=14.892， P<0.01） and longer response time （F=10.642， P=0.002） compared with control group. ② The main effect of target emotional valence on accuracy （F=7.575， P=0.001） and response time （F=3.196， P=0.044） in the prospective memory task was statistically significant. Simple effect analysis revealed that depressive group yielded a shorter response time and higher accuracy rate under negative conditions compared with positive and neutral conditions （P<0.05 or 0.01）. ③ The main effect of target salience on accuracy （F=6.659， P=0.012） and response time （F=10.106， P=0.002） in the prospective memory task was also statistically significant， with higher accuracy and shorter response time for salient targets compared with non-salient targets. ConclusionPatients with depressive disorder demonstrate preferential attention to and processing of negative stimuli in prospective memory tasks， while increasing target salience may facilitate spontaneous processing of prospective memory task in patients with depressive disorder. ［Funded by Inner Mongolia Health Commission Medical Health Science and Technology Project （number， 202202104）］

Objectives:To explore the antitumor effects of redox-responsive nanoparticles containing platinum(Ⅳ)—NP@Pt(Ⅳ) in ovarian cancer.Methods:Redox-responsive polymer carriers were synthesized. Polymer carriers and platinum(Ⅳ)—Pt(Ⅳ) can self-assemble into NP@Pt(Ⅳ). Inductively coupled plasma mass spectrometry was performed to detect the platinum release from NP@Pt(Ⅳ) in reducing environment and the platinum content in ovarian cancer cells ES2 treated with cisplatin, Pt(Ⅳ) and NP@Pt(Ⅳ). The proliferation ability of the ovarian cancer cells were detected by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Cellular apoptosis was assessed by flow cytometry. Collection of primary ovarian cancer tissues from patients with primary high-grade serous ovarian cancer who were surgically treated at the Cancer Hospital of the Chinese Academy of Medical Sciences from October to December 2022. The high-grade serous ovarian cancer patient-derived xenograft (PDX) mice were intravenously injected with Cy7.5 labeled NP@Pt(Ⅳ) followed by in vivo imaging system. Mice were treated with PBS, cisplatin and NP@Pt(Ⅳ). Tumor volume and weight were measured in each group. Necrosis, apoptosis and cell proliferation of tumor tissues were detected by hematoxylin-eosin (HE) staining, TUNEL fluorescence staining and Ki-67 immunohistochemistry staining. Body weight and HE staining of heart, liver, spleen, lung and kidney of mice in each group were measured.Results:The platinum release of NP@Pt(Ⅳ) after 48 hours in reducing environment was 76.29%, which was significantly higher than that of 26.82% in non-reducing environment ( P<0.001). The platinum content in ES2 cells after 4 hours and 7 hours of treatment with NP@Pt(Ⅳ) (308.59, 553.15 ng/million cells) were significantly higher than those of Pt(Ⅳ) (100.21, 180.31 ng/million cells) and cisplatin (43.36, 50.36 ng/million cells, P＜0.05). The half inhibitory concentrations of NP@Pt(Ⅳ) in ovarian cancer cells ES2, A2780, A2780DDP were 1.39, 1.42 and 4.62 μmol/L, respectively, which were lower than those of Pt(IV) (2.89, 7.27, and 16.74 μmol/L) and cisplatin (5.21, 11.85, and 71.98 μmol/L). The apoptosis rate of ES2 cells treated with NP@Pt(Ⅳ) was (33.91±3.80)%, which was significantly higher than that of Pt(Ⅳ) [(16.28±2.41)%] and cisplatin [(15.01±1.17)%, P＜0.05]. In high-grade serous ovarian cancer PDX model, targeted accumulation of Cy7.5 labeled NP@Pt(Ⅳ) at tumor tissue could be observed. After the treatment, the tumor volume of mice in NP@Pt(IV) group was (130±98) mm 3, which was significantly lower than those in control group [(1 349±161) mm 3, P<0.001] and cisplatin group [(715±293) mm 3, P=0.026]. The tumor weight of mice in NP@Pt(IV) group was (0.17±0.09)g, which was significantly lower than those in control group [(1.55±0.11)g, P<0.001] and cisplatin group [(0.82±0.38)g, P=0.029]. The areas of tumor necrosis and apoptosis in mice treated with NP@Pt(Ⅳ) were higher than those in mice treated with cisplatin. Immunohistochemical staining revealed that there were low expressions of Ki-67 at tumor tissues of mice treated with NP@Pt(Ⅳ) compared with cisplatin. The change in body weight of mice in NP@Pt(Ⅳ) group was not significantly different from that of the control group [(18.56±2.04)g vs.(20.87±0.79)g, P=0.063]. Moreover, the major organs of the heart, liver, spleen, lung, and kidney were also normal by HE staining. Conclusion:Redox-responsive NP@Pt(Ⅳ), produced in this study can enhance the accumulation of cisplatin in ovarian cancer cells and improve the efficacy of ovarian cancer chemotherapy.

Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development, such as lead discovery, drug repurposing and elucidation of potential drug side effects. Therefore, a variety of machine learning-based models have been developed to predict these interactions. In this study, a model called auxiliary multi-task graph isomorphism network with uncertainty weighting (AMGU) was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network (MT-GIN) with the auxiliary learning and uncertainty weighting strategy. The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural networks (GNN) models on the internal test set. Furthermore, it also exhibited much better performance on two external test sets, suggesting that the AMGU model has enhanced generalizability due to its great transfer learning capacity. Then, a naïve model-agnostic interpretable method for GNN called edges masking was devised to explain the underlying predictive mechanisms, and the consistency of the interpretability results for 5 typical epidermal growth factor receptor (EGFR) inhibitors with their structure‒activity relationships could be observed. Finally, a free online web server called KIP was developed to predict the kinome-wide polypharmacology effects of small molecules (http://cadd.zju.edu.cn/kip).

Acid-base dissociation constant (pK_a) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pK_a prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pK_a (multi-fidelity modeling with subgraph pooling for pK_a prediction), a novel pK_a prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledge-aware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pK_a prediction. To overcome the scarcity of accurate pK_a data, low-fidelity data (computational pK_a) was used to fit the high-fidelity data (experimental pK_a) through transfer learning. The final MF-SuP-pK_a model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pK_a achieves superior performances to the state-of-the-art pK_a prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pK_a achieves 23.83% and 20.12% improvement in terms of mean absolute error (MAE) on the acidic and basic sets, respectively.

The vagina contains at least a billion microbial cells,dominated by lactobacilli.Here we perform metagenomic shotgun sequencing on cervical and fecal samples from a cohort of 516 Chinese women of reproductive age,as well as cervical,fecal,and salivary samples from a second cohort of 632 women.Factors such as pregnancy history,delivery history,cesarean section,and breastfeeding were all more important than menstrual cycle in shaping the microbiome,and such information would be necessary before trying to interpret differences between vagino-cervical micro-biome data.Greater proportion of Bifidobacterium breve was seen with older age at sexual debut.The relative abundance of lactobacilli especially Lactobacillus crispatus was negatively associated with pregnancy history.Potential markers for lack of menstrual regularity,heavy flow,dysmenor-rhea,and contraceptives were also identified.Lactobacilli were rare during breastfeeding or post-menopause.Other features such as mood fluctuations and facial speckles could potentially be predicted from the vagino-cervical microbiome.Gut and salivary microbiomes,plasma vitamins,metals,amino acids,and hormones showed associations with the vagino-cervical microbiome.Our results offer an unprecedented glimpse into the microbiota of the female reproductive tract and call for international collaborations to better understand its long-term health impact other than in the settings of infection or pre-term birth.

Rituximab is the main monoclonal antibody for targeted therapy currently. With more rituximab biosimilars appearing and clinical evaluation need increasing, it is crucial to develop rapid and effective quantitative methods to determine the rituximab blood concentration in biological matrices for drug metabolism and pharmacokinetics (DMPK) analysis. This article reviewed the application of ligand binding method (LBA), liquid chromatography-tandem mass spectrometry (LC-MS/MS) and emerging quantitative technology to detect the blood concentration of Rituximab, which may provide valuable information for the analysts and testers when developing quantitative methods for rituximab and its biosimilars.

Objective:To investigate the expressions of leptin receptor (OBR) and phosphorylated protein kinase B (p-AKT) in patients with diffuse large B-cell lymphoma (DLBCL) and its significance.Methods:Immunohistochemistry was applied to detect the expressions of OBR and p-AKT in tissues from 90 patients with DLBCL and 20 patients with reactive lymphoid hyperplasia (RLH) between 2010 and 2015 in Shandong Provincial Hospital Affiliated to Shandong First Medical University. Cell proliferation assay was used to detect the effect of leptin on the proliferation of SUDHL4 and SUDHL5 in DLBCL cell lines, and the expression of p-AKT in SUDHL4 and SUDHL5 after the cultured leptin was detected by using Western blot.Results:The high expression rate of OBR and p-AKT of DLBCL was 47.7% (43/90) and 27.7% (25/90), respectively, and low expression was found in 20 cases of RLH. There were statistically differences in the expressions of OBR and p-AKT in DLBCL and RLH ( P < 0.01, P = 0.027). The expressions of OBR and p-AKT were not correlated with age, gender, extranodal infiltrations, clinical staging, lactic dehydrogenase (LDH) level, B-symptom and international prognostic index (IPI) score (all P > 0.05). The expression of OBR was positively related with that of p-AKT in DLBCL patients ( r = 0.532, P < 0.05). Leptin could increase the proliferation of SUDHL4 and SUDHL5 cells and promote the expression of p-AKT. Conclusion:Leptin and OBR can promote the proliferation of DLBCL cells and may be involved in the occurrence and development of DLBCL by activating PI3K-AKT pathway.

Objective:To evaluate the protective efficacy and safety of Brucella 104M against aerosol challenge in BALB/c mice and characterize its immunological effects. Methods:Female mice of 6-8 weeks old were immunized with Brucella abortus strain 104M by intratracheal aerosol delivery or intranasal instillation or subcutaneous injection route. Six mice of each group were sacrificed at 4, 8, 16, 24 weeks after immunization. At each time point, the clinical manifestations of mice were investigated, the serum, spleen and lung samples of mice were collected, body weight, spleen weight, bacteria loads in spleens, the anti- Brucella antibodies titers in serum and the cytokines concentrations of IFN-γ, IL-18 in serum or lung homogenate of the mice were detected. Twenty two weeks after immunization, all the mice were challenged with Brucella A19 through intratracheal aerosol delivery. Results:Compared with the control group, neither abnormal clinical symptoms nor significant changes in body weight were found in 104M immunization groups, at each time point when immunized through either nose dropping route, subcutaneous injection or aerosol routes; and the spleen weight of immunization groups were lower than control group after challenge ( P<0.05): *M1 (0.26±0.16)g

Objective:Peripheral nerve invasion (PNI) is associated with local recurrence and poor prognosis in patients with advanced gastric cancer. A risk-assessment model based on preoperative indicators for predicting PNI of gastric cancer may help to formulate a more reasonable and accurate individualized diagnosis and treatment plan.Methods:Inclusion criteria: (1) electronic gastroscopy and enhanced CT examination of the upper abdomen were performed before surgery; (2) radical gastric cancer surgery (D2 lymph node dissection, R0 resection) was performed; (3) no distant metastasis was confirmed before and during operation; (4) postoperative pathology showed an advanced gastric cancer (T2-4aN0-3M0), and the clinical data was complete. Those who had other malignant tumors at the same time or in the past, and received neoadjuvant radiochemotherapy or immunotherapy before surgery were excluded. In this retrospective case-control study, 550 patients with advanced gastric cancer who underwent curative gastrectomy between September 2017 and June 2019 were selected from the Affiliated Hospital of Qingdao University for modeling and internal verification, including 262 (47.6%) PNI positive and 288 (52.4%) PNI negative patients. According to the same standard, clinical data of 50 patients with advanced gastric cancer who underwent radical surgery from July to November 2019 in Qingdao Municipal Hospital were selected for external verification of the model. There were no statistically significant differences between the clinical data of internal verification and external verification (all P>0.05). Univariate analysis and multivariate logistic regression analysis were used to determine the independent risk factors for PNI in advanced gastric cancer, and the clinical indicators with statistically significant difference were used to establish a preoperative nomogram model through R software. The Bootstrap method was applied as internal verification to show the robustness of the model. The discrimination of the nomogram was determined by calculating the average consistency index (C-index). The calibration curve was used to evaluate the consistency of the predicted results with the actual results. The Hosmer-Lemeshow test was used to examine the goodness of fit of the discriminant model. During external verification, the corresponding C-index index was also calculated. The area under ROC curve (AUC) was used to evaluate the predictive ability of the nomogram in the internal verification and external verification groups. Results:A total of 550 patients were identified in this study, 262 (47.6%) of which had PNI. Multivariate logistic regression analysis revealed that carcinoembryonic antigen level ≥ 5 μg/L (OR=5.870, 95% CI: 3.281-10.502, P<0.001), tumor length ≥5 cm (OR=5.539,95% CI: 3.165-9.694, P<0.001), mixed Lauren classification (OR=2.611, 95%CI: 1.272-5.360, P=0.009), cT3 stage (OR=13.053, 95% CI: 5.612-30.361, P<0.001) and the presence of lymph node metastasis (OR=4.826, 95% CI: 2.729-8.533, P<0.001) were significant independent risk factors of PNI in advanced gastric cancer (all P<0.05). Based on these results, diffused Lauren classification and cT4 stage were included to establish a predictive nomogram model. CEA ≥ 5 μg/L was for 68 points, tumor length ≥ 5 cm was for 67 points, mixed Lauren classification was for 21 points, diffused Lauren classification was for 38 points, cT3 stage was for 75 points, cT4 stage was for 100 points, and lymph node metastasis was for 62 points. Adding the scores of all risk factors was total score, and the probability corresponding to the total score was the probability that the model predicted PNI in advanced gastric cancer before surgery. The internal verification result revealed that the AUC of nomogram was 0.935, which was superior than that of any single variable, such as CEA, Lauren classification, cT stage, tumor length and lymph node metastasis (AUC: 0.731, 0.595, 0.838, 0.757 and 0.802, respectively). The external verification result revealed the AUC of nomogram was 0.828. The C-ndex was 0.931 after internal verification. External verification showed a C-index of 0.828 from the model. The calibration curve showed that the predictive results were good in accordance with the actual results ( P=0.415). Conclusion:A nomogram model constructed by CEA, tumor length, Lauren classification (mixed, diffuse), cT stage, and lymph node metastasis can predict the PNI of advanced gastric cancer before surgery.

Objective:Peripheral nerve invasion (PNI) is associated with local recurrence and poor prognosis in patients with advanced gastric cancer. A risk-assessment model based on preoperative indicators for predicting PNI of gastric cancer may help to formulate a more reasonable and accurate individualized diagnosis and treatment plan.Methods:Inclusion criteria: (1) electronic gastroscopy and enhanced CT examination of the upper abdomen were performed before surgery; (2) radical gastric cancer surgery (D2 lymph node dissection, R0 resection) was performed; (3) no distant metastasis was confirmed before and during operation; (4) postoperative pathology showed an advanced gastric cancer (T2-4aN0-3M0), and the clinical data was complete. Those who had other malignant tumors at the same time or in the past, and received neoadjuvant radiochemotherapy or immunotherapy before surgery were excluded. In this retrospective case-control study, 550 patients with advanced gastric cancer who underwent curative gastrectomy between September 2017 and June 2019 were selected from the Affiliated Hospital of Qingdao University for modeling and internal verification, including 262 (47.6%) PNI positive and 288 (52.4%) PNI negative patients. According to the same standard, clinical data of 50 patients with advanced gastric cancer who underwent radical surgery from July to November 2019 in Qingdao Municipal Hospital were selected for external verification of the model. There were no statistically significant differences between the clinical data of internal verification and external verification (all P>0.05). Univariate analysis and multivariate logistic regression analysis were used to determine the independent risk factors for PNI in advanced gastric cancer, and the clinical indicators with statistically significant difference were used to establish a preoperative nomogram model through R software. The Bootstrap method was applied as internal verification to show the robustness of the model. The discrimination of the nomogram was determined by calculating the average consistency index (C-index). The calibration curve was used to evaluate the consistency of the predicted results with the actual results. The Hosmer-Lemeshow test was used to examine the goodness of fit of the discriminant model. During external verification, the corresponding C-index index was also calculated. The area under ROC curve (AUC) was used to evaluate the predictive ability of the nomogram in the internal verification and external verification groups. Results:A total of 550 patients were identified in this study, 262 (47.6%) of which had PNI. Multivariate logistic regression analysis revealed that carcinoembryonic antigen level ≥ 5 μg/L (OR=5.870, 95% CI: 3.281-10.502, P<0.001), tumor length ≥5 cm (OR=5.539,95% CI: 3.165-9.694, P<0.001), mixed Lauren classification (OR=2.611, 95%CI: 1.272-5.360, P=0.009), cT3 stage (OR=13.053, 95% CI: 5.612-30.361, P<0.001) and the presence of lymph node metastasis (OR=4.826, 95% CI: 2.729-8.533, P<0.001) were significant independent risk factors of PNI in advanced gastric cancer (all P<0.05). Based on these results, diffused Lauren classification and cT4 stage were included to establish a predictive nomogram model. CEA ≥ 5 μg/L was for 68 points, tumor length ≥ 5 cm was for 67 points, mixed Lauren classification was for 21 points, diffused Lauren classification was for 38 points, cT3 stage was for 75 points, cT4 stage was for 100 points, and lymph node metastasis was for 62 points. Adding the scores of all risk factors was total score, and the probability corresponding to the total score was the probability that the model predicted PNI in advanced gastric cancer before surgery. The internal verification result revealed that the AUC of nomogram was 0.935, which was superior than that of any single variable, such as CEA, Lauren classification, cT stage, tumor length and lymph node metastasis (AUC: 0.731, 0.595, 0.838, 0.757 and 0.802, respectively). The external verification result revealed the AUC of nomogram was 0.828. The C-ndex was 0.931 after internal verification. External verification showed a C-index of 0.828 from the model. The calibration curve showed that the predictive results were good in accordance with the actual results ( P=0.415). Conclusion:A nomogram model constructed by CEA, tumor length, Lauren classification (mixed, diffuse), cT stage, and lymph node metastasis can predict the PNI of advanced gastric cancer before surgery.