This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

Anxiety/epidemiology* ; Depression/epidemiology* ; Humans ; Sleep ; Sleep Quality ; Students ; Surveys and Questionnaires ; Universities

Pruning branches and leaves is the measure to stimulate the growth of Lonicera japonica flower buds, and consequently, the resources of pruned leaves are inevitably and seriously wasted in production. High-performance liquid chromatography(HPLC) was applied for content determination of seven active ingredients(chlorogenic acid, galuteolin, isochlorogenic acids A, B, and C, secologanic acid, and secoxyloganin) in L. japonica leaves from March to November. The results showed that the tillering removed from the trunk of L. japonica in March, the leaves pruned from May to July, and the leaves after the first frost date in November were rich in active ingredients, which deserved further exploitation and utilization. The total content(TC) of active ingredients in pruned L. japonica leaves in early March was the highest. The content of active ingredients in L. japonica leaves increased significantly after the first frost date, which was close to that in the bud tillers pruned in early and middle March. After the first frost date, L. japonica leaves are incapable of photosynthesis, and the harvesting of L. japonica leaves does not affect the physiological activities of the tree. In addition to huge resources, the content of active ingredients is high during this period, which is the best harvesting period of L. japonica leaves.
Chromatography, High Pressure Liquid/methods* ; Flowers ; Lonicera ; Plant Leaves

Objective: To establish a survival prediction model based on the independent prognostic factors of long-term prognosis after laparoscopic liver resection(LLR) for intrahepatic cholangiocarcinoma(ICC). Methods: The clinical and pathological data of 351 consecutive patients with ICC who received radical LLR in 13 Chinese medical centers from August 2010 to May 2021 were collected retrospectively. There were 190 males and 161 females,aged(M(IQR)) 61(14)years(range:23 to 93 years). The total cohort was randomly divided into a training dataset(264 cases) and a validation dataset(87 cases). The patients were followed up by outpatient service or telephone,and the deadline for follow-up was October 2021. Based on the training dataset,the multivariate Cox proportional hazards regression model was used to screen the independent influencing factors of long-term prognosis to construct a Nomogram model. The Nomogram model's discrimination,calibration,and clinical benefit were evaluated through internal and external validation,and an assessment of the overall value of two groups was made through the use of a receiver operating characteristic(ROC) curve. Results: There was no significant difference in clinical and pathological characteristics and long-term survival results between the training and validation datasets(all P>0.05). The multivariate Cox analysis showed that CA19-9,CA125,conversion to laparotomy during laparoscopic surgery,and lymph node metastasis were independent prognostic factors for ICC patients after LLR(all P<0.05). The survival Nomogram was established based on the independent prognostic factors obtained from the above screening. The ROC curve showed that the area under the curve of 1, 3 and 5-year overall survival rates of patients in the training dataset were 0.794(95%CI:0.721 to 0.867),0.728(95%CI:0.618 to 0.839) and 0.799(95%CI:0.670 to 0.928),and those in the validation dataset were 0.787(95%CI:0.660 to 0.915),0.831(95%CI:0.678 to 0.983) and 0.810(95%CI:0.639 to 0.982). Internal and external validation proved that the model exhibited a certain discrimination,calibration,and clinical applicability. Conclusion: The survival Nomogram model based on the independent influencing factors of long-term prognosis after LLR for ICC(including CA19-9,CA125,conversion to laparotomy during laparoscopic surgery,and lymph node metastasis) exhibites a certain differentiation,calibration,and clinical practicability.
Bile Duct Neoplasms/surgery* ; Bile Ducts, Intrahepatic/pathology* ; CA-19-9 Antigen ; Cholangiocarcinoma/diagnosis* ; Female ; Humans ; Laparoscopy ; Lymphatic Metastasis ; Male ; Nomograms ; Prognosis ; Retrospective Studies

Objective: To investigate the effects of long non-coding RNA RP1-90L14.1 on the proliferation, migration and invasion of prostate cancer LNCaP cells and the expressions of GRIN2A and BACE2. METHODS: Using RT-PCR, we detected the expression of RP1-90L14.1 in LNCaP and LNCaP-AI cells, transiently transfected the RP1-90L14.1 overexpression plasmid (the RP1-90L14.1 group) and vector plasmid (the LNCaP-NC group) into the LNCaP cells, and cultured the two groups of cells with ordinary medium and phenol red-free activated carbon adsorption medium (PRF-ACA). Then we examined the proliferation, migration and invasiveness of the cells by CCK-8 and Transwell, and determined the mRNA and protein expressions of GRIN2A and BACE2 by RT-PCR and Western blot. RESULTS: The expression of RP1-90L14.1 was significantly higher in the LNCaP-AI than in the LNCaP cells (8.49 ± 0.43 vs 2.53 ± 0.95, P < 0.05), and so was that of LNCaP-RP1-90L14.1 in the RP1-90L14.1 than in the LNCaP-NC group after transfection (0.71 ± 0.22 vs 0.02 ± 0.01, P < 0.05). The optical densities (OD) of the cells were 51.95% and 50.69% higher in the RP1-90L14.1 than in the LNCaP-NC group after 72 hours of culture with ordinary medium and phenol red-free ACA (1.22 ± 0.08 vs 0.08 ± 0.05, P < 0.05; 0.79 ± 0.02 vs 0.53 ± 0.05, P < 0.05), and 51.72% and 60.23% higher in the former than in the latter after 96 hours (1.72 ± 0.07 vs 1.13 ± 0.05, P < 0.05; 1.18 ± 0.05 vs 0.73 ± 0.08, P < 0.05). The numbers of the migrating cells cultured with common medium and PRF-ACA were markedly higher in the RP1-90L14.1 than in the LNCaP-NC group after transfection (682.0 ± 42.7 vs 422.0 ± 37.1, P < 0.05; 419.0 ± 42.9 vs 251.0 ± 25.9, P < 0.05), and so were those of the invading cells (507.0 ± 22.2 vs 274.0 ± 19.6, P < 0.05; 352.0 ± 14.1 vs 216.0 ± 14.3, P < 0.05). Statistically significant differences were observed between the RP1-90L14.1 and LNCaP-NC groups in the mRNA and protein expressions of GRIN2A (5.13 ± 0.89 vs 2.09 ± 0.54, P < 0.05; 5.88 ± 0.29 vs 2.03 ± 0.22, P < 0.05) and BACE2 (5.82 ± 0.50 vs 2.53 ± 0.30, P < 0.05; 4.89 ± 0.19 vs 3.37 ± 0.13, P < 0.05). CONCLUSIONS 　lncRNA RP1-90L14.1 may play important roles in the proliferation, migration and invasiveness of prostate cancer cells. RP1-90L14.1 can promote the expressions of GRIN2A and BACE2 and may have an endogenous competitive relation with GRIN2A and BACE2.

Objective: To monitor the WT1 mRNA level and its dynamic changes in patients with myelodysplastic syndromes (MDS) after hypomethylating agents (HMA) , as well as to assess the significance of WT1 mRNA levels and its dynamic changes in evaluating the efficacy of HMA and distinguishing the disease status of heterogeneous patients with stable disease (SD) . Methods: Bone marrow or peripheral blood samples of 56 patients with MDS who underwent hypomethylating agents (≥4 cycles) from November 2009 to March 2018 were tested by real-time quantitative polymerase chain reaction (PCR) to detect the expression of WT1 mRNA, and to observe the correlation between the dynamic changes of WT1 mRNA expression and clinical efficacy and prognosis of patients. Results: WT1 mRNA expression levels of MDS patients decreased significantly after 3 cycles of hypomethylating agent treatment. Besides, the WT1 mRNA expression levels of patients increased significantly after diseases progression. According to the dynamic changes of WT1 mRNA expression levels during SD, 45 cases could be further divided into increased group and non-increased group. In those SD patients with increased WT1 mRNA expression level, the ratio of suffering disease progression or transformation to AML was 95.65% (22/23) , whereas the ratio turned to be 9.09% (2/22) for the non-increased group (χ(2)=33.852, P<0.001) . Compared with those SD patients reporting no increase in WT1 mRNA expression level, the overall survival[17 (95%CI 11-23) months vs not reached, P<0.001] and progression-free survival [13 (95%CI 8-18) months vs not reached, P<0.001] of those SD patients reporting increase in WT1 mRNA expression level were significantly shorter. Conclusion: WT1 mRNA expression level is a useful indicator to assess the efficacy of hypomethylating agents in MDS patients. Especially in patients with SD, detection of the changes in WT1 mRNA expression level is able to predict disease progression and help to make clinical decision.
Bone Marrow ; Humans ; Myelodysplastic Syndromes/genetics* ; Prognosis ; RNA, Messenger ; WT1 Proteins/genetics*

Carcinoma ; Humans ; Limbic Encephalitis ; diagnosis ; surgery ; Male ; Middle Aged ; Nasopharyngeal Neoplasms ; diagnosis ; surgery

To establish a method for the identification of five species and one variety of medicinal plants from Diospyros, their leaf veins, epidermis, anatomic and powder characters were observed and compared with macro-morphological and microscopic methods. The results indicated the differences of secondary and tertiary veins among those Diospyros species. The single cell non-glandular hair and glandular hair exist in most species' epidermis while stone cells were only found in the leaf powders of two species. Through the study, the main differences of leaf macro- and micro-morphology of these species were obtained and practical keys were also established, which can provide scientific base not only for identification of these species during their vegetative stages, but also for accuracy authentication of the source of Kaki Folium.

This study was aimed to investigate the c-kit mutation in acute myeloid leukemia (AML) patients with AML1-ETO and analyze its relation with clinical and laboratorial features and prognosis. PCR and sequencing methods were used to detect the c-kit 17 exon mutations in 31 AML patients with AML1-ETO. The relation of the c-kit mutation with clinical features, results of laboratorial examination and prognosis of disease were analyzed. The results showed that the c-kit mutation was found in 14 out of 31 AML patients and the mutation frequency was 45.16%. Male patients had a higher incidence of c-kit mutation than that of female patients (P = 0.020). The proportion of patients with newly diagnosed white blood cell>10×10(9)/L and with extramedullary infiltration in mutated group were higher than those in unmutated group respectively. No significant difference was observed at the age (P = 0.437) and the rate of bone marrow blasts(P = 0.510) between the above mentioned two groups. The difference in complete remission rate (64.29% vs 80%, P = 0.344)and relapse rate (58.33% vs 21.43%, P = 0.054) between c-kit mutated and c-kit unmutated groups were not significant. While the c-kit mutated group had a significant higher death rate as compared with c-kit unmutated group (57.14% vs 20%, P = 0.039). It is concluded that the c-kit mutation is frequent in AML patients with AML1-ETO and the c-kit mutated patients have a poor prognosis. It is important to detect c-kit mutation in routine clinical practice for patient's risk stratification, evaluation of prognosis and selection of effective treatment.
Adolescent ; Adult ; Aged ; Core Binding Factor Alpha 2 Subunit ; genetics ; DNA Mutational Analysis ; Female ; Humans ; Leukemia, Myeloid, Acute ; genetics ; pathology ; Male ; Middle Aged ; Mutation ; Oncogene Proteins, Fusion ; genetics ; Prognosis ; Proto-Oncogene Proteins c-kit ; genetics ; RUNX1 Translocation Partner 1 Protein ; Treatment Outcome ; Young Adult