Objective: To explore the mechanism of cistanche deserticola(meat cistanche) in treating Alzheimer′s disease(AD) through network pharmacology, molecular docking, and animal experiments. Methods : Effective components of meat cistanche were mined from the TCMSP database, and AD-related targets were filtered using the SwissTargetPrediction, DisGeNET, and GeneCards databases. The intersection of these targets was analyzed using protein-protein interaction(PPI) networks. Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses were conducted via the Metascape database. Molecular docking of meat cistanche′s active components with core targets was performed using AutoDock Vina. Based on network pharmacology predictions, an AD model was established using 8-month-old SAMP8 mice, with Morris water maze tests assessing learning and cognitive functions, Nissl staining observing hippocampal neuron morphology, and enzyme-linked immunosorbent assays and Western blotting detecting the expression levels of cAMP signaling pathway-related proteins in hippocampal tissues. Results : Network pharmacology analysis predicted that meat cistanche might act on 74 AD targets through 8 active components. Molecular docking showed high affinity of active components like acteoside with core targets such as ESR1, BDNF, MAPK1, and APP. KEGG analysis indicated involvement in pathways related to cancer, cAMP signaling, and AD. Animal experiments demonstrated that meat cistanche effectively improved learning and cognitive impairments in AD mice and alleviated hippocampal neuron damage. ELISA and Western blotting results indicated that meat cistanche significantly increased the expression levels of cAMP, PKA, P-CREB in the cAMP pathway and promoted the expression of downstream neurotrophic factor BDNF. Conclusion Meat cistanche can improve learning and cognitive disorders in AD model mice and may exert therapeutic effects on AD by up-regulating the cAMP signaling pathway and the expression of downstream BDNF protein targets, thereby improving hippocampal neuron injury.

Objective:To analyze the epidemiological and clinical features of infectious diseases of the central nervous system (CNS).Methods:A cross-sectional study and analysis were conducted to summarize the epidemiological and clinical characteristics of 9 918 patients with CNS infectious diseases, who were diagnosed and treated at 29 hospitals across China from January 1, 2001 to December 31, 2020. Data collected included demographic data, clinical manifestations, health economic indicators, and prognostic outcomes.Results:Among the 9 918 collected cases of CNS infectious diseases, 5 559 were male (56.0%) and 4 359 were female (44.0%), with an onset age of 38 (25, 53) years. Education level: slightly more junior high school education (2 651 cases, 26.7%), and less elementary school education and below (2 181 cases, 22.0%) were found. Occupational distribution: farmers were found predominant (3 215 cases, 32.4%), followed by workers (1 826 cases, 18.4%) and students (1 633 cases, 16.5%). Clinical manifestations: headache (6 074 cases, 61.2%), fever (5 869 cases, 59.2%) and positive meningeal irritation signs (2 273 cases, 22.9%) were the 3 most common clinical manifestations, followed by nausea and (or) vomiting (2 095 cases, 21.1%), impaired consciousness (2 077 cases, 20.9%), psychiatric symptom (1 866 cases, 18.8%) and epilepsy (1 627 cases, 16.4%), etc., and cranial nerve involvement was found in 669 cases (6.7%). Major pathogens included viruses in 6 814 cases (68.7%), Mycobacterium tuberculosis in 1 677 cases (16.9%), common bacteria in 864 cases (8.7%), fungi in 254 cases (2.6%), spirochetes of syphilis in 183 cases (1.8%), parasites in 121 cases (1.2%), and rickettsiae in 5 cases (0.1%). Urban-rural distribution: slightly more cases were found in the countryside (5 418 cases, 54.6%) than in the towns (4 500 cases, 45.4%). Distribution of onset by season: 2 412 cases (24.3%) fell ill in spring, 2 835 cases (28.6%) in summer, 2 187 cases (22.1%) in fall, and 2 484 cases (25.0%) in winter. Health economics: the duration of hospitalization was 15 (8, 27) days, and the cost of hospitalization was 1.53 (0.91, 3.02)×10 000 yuan. Prognosis: 9 531 cases (96.1%) were cured or improved, and 92 cases (0.9%) died. Conclusions:The pathogens responsible for CNS infectious diseases are predominantly viruses. Although the incidence is slightly higher during the summer months, the overall seasonal pattern is not particularly pronounced. These infections are more commonly observed in young and middle-aged males and present with a diverse range of clinical manifestations, contributing to a significant disease burden.

Objective:To investigate the value of a cross-combination machine learning approach in constructing a PET radiomics score (RadScore) for predicting early treatment response and prognosis in patients with primary gastrointestinal diffuse large B-cell lymphoma (PGI-DLBCL).Methods:This retrospective cohort study was conducted on 108 patients (59 males and 49 females, age (55.6±12.1) years) diagnosed with PGI-DLBCL between November 2016 and December 2021 at Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University ( n=85) and West China Hospital, Sichuan University ( n=23). Patients were divided into a training set ( n=86) and a validation set ( n=22) with the ratio of 8∶2 using stratified random sampling method. Seven machine learning models were employed to generate 49 feature selection-classification candidates, and the optimal candidate was selected to construct the RadScore, with five-fold cross-validation applied to determine the best-performing model. Logistic regression analysis was performed to identify risk factors for early treatment response, and a radiomics nomogram was developed by integrating RadScore with clinical predictors. Survival results between different groups of RadScore was compared by log-rank test. Results:Nineteen predictive features were selected from 111 radiomic features to construct the RadScore. In the training set, lactate dehydrogenase (LDH) (odds ratio ( OR)=3.53, 95% CI: 1.21-10.31, P=0.021), intestinal involvement ( OR=3.04, 95% CI: 1.04-8.88, P=0.042), total lesion glycolysis (TLG; OR=6.73, 95% CI: 2.23-20.29, P<0.001) and RadScore ( OR=15.11, 95% CI: 3.95-57.80, P<0.001) were identified as independent risk factors for predicting early treatment response. The combined model integrating RadScore, LDH, intestinal involvement, and TLG demonstrated good discriminatory ability for early treatment response (AUC=0.860 in the training set; AUC=0.902 in the validation set). Significant differences were observed in progression-free survival (PFS) and overall survival (OS) between different RadScore groups ( χ2 values: 13.92 and 8.56, both P<0.01). Conclusions:The machine learning-based RadScore may effectively predict survival outcomes in patients with PGI-DLBCL. The combined model integrating RadScore, clinical factors, and metabolic indicators can predict early treatment response in PGI-DLBCL patients.

Objective:This study aimed to analyze the genetic subtypes and drug resistance transmission characteristics of HIV-1 among the preoperative population in Ningxia from 2018 to 2023, to provide a scientific basis for the prevention and control of the AIDS epidemic.Methods:Plasma samples and demographic information of HIV/AIDS patients receiving antiviral treatment in Ningxia from 2018 to 2023 were collected. Blood samples with a viral loads >200 copies/ml from preoperative testing were amplified, sequenced, and subjected to genotypic resistance testing to analyze their genetic subtypes and drug resistance characteristics. The TN93 model in MEGA11 software was used to calculate the genetic distance between each pair of all sequences, and a molecular transmission network was constructed in Cytoscape 3.10.0 with 1.9% as the genetic threshold.Results:Among 101 preoperative HIV/AIDS patients, CRF07_BC and CRF01_AE were the predominant subtypes. The majority were male (85.15%, 86/101), aged 41-60 years (45.54%, 46/101), residing in Yinchuan city (61.39%, 62/101), and infected via heterosexual transmission (71.29%, 72/101), with most cases being late-detected. Of 39 drug-resistant sequences, resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs) alone (18.81%, 19/101) and dual resistance to nucleoside reverse transcriptase inhibitors (NRTIs)-NNRTIs (13.86%, 14/101) were most common. Among 44 sequences forming 13 transmission clusters, nine clusters harbored drug-resistant mutations. Four subtypes entered the molecular network, primarily involving heterosexual transmission, individuals with junior high school education or below, and men aged≥50 years.Conclusions:From 2018 to 2023, the preoperative HIV/AIDS patients had diversified genetic subtypes, with higher rates of overall drug resistance and late detection, stronger drug resistance and higher mortality rate. Strengthening molecular epidemiological research and developing targeted screening strategies are critical to improve early detection and reduce transmission risks.

Objective:To analyze risk factors for immune checkpoint inhibitor-related pneumonitis (CIP) in non-small cell lung cancer (NSCLC) patients based on clinical and radiological characteristics, and to develop and validate a nomogram model for predicting the risk of CIP.Methods:A retrospective case-controlled study was conducted. The clinical data of 159 patients diagnosed with NSCLC in Shanxi Province Cancer Hospital between January 2020 and December 2023 who received immune checkpoint inhibitor (ICI) therapy were retrospectively analyzed. Based on the development of CIP after immunotherapy, the patients were divided into the CIP group (30 cases) and the control group (129 cases). The clinical data of NSCLC patients, hematological indicators and the data of imaging characteristics before their first ICI treatment were collected. Quantitative assessments were performed on pretreatment chest CT images, including lung total tumor volume, number of involved lung segments, and pulmonary infection index. Logistic regression analysis was used to screen out the factors influencing the development of CIP. R 4.3.0 statistical software was used to construct a nomogram model for predicting CIP based on the statistically significant risk factors identified in the multivariate logistic regression analysis. The predictive performance of the model was evaluated by using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Calibration curves and decision curve analysis (DCA) were employed to assess the model's consistency and clinical benefit.Results:There were statistically significant differences in the proportions of patients with a history of chest radiotherapy and those receiving different immunotherapy regimens between the control group and the CIP group (both P < 0.001). The difference in the lactate dehydrogenase (LDH) [ M ( IQR)] between the both groups was statistically significant [211.00 U/L (57.00 U/L) vs. 276.00 U/L (136.00 U/L), Z = -3.41, P < 0.001]; additionally, the difference in lung status score between the 2 groups was statistically significant ( P < 0.001). Multivariate logistic regression analysis revealed that a history of chest radiotherapy (with vs. without: OR = 4.200, 95% CI: 1.466-12.036), the combination of immunotherapy (monotherapy vs. the combined therapy: OR = 0.106, 95% CI: 0.022-0.509), LDH ≥ 255.5 U/L (< 255.5 U/L vs. ≥ 255.5 U/L: OR = 0.988, 95% CI: 0.981-0.995), and severe lung status score(mild vs. moderate vs. severe: OR = 0.187, 95% CI: 0.059-0.593) were independent risk factors for CIP development in NSCLC patients after immunotherapy (all P < 0.05). A nomogram model for predicting CIP occurrence was constructed based on chest radiotherapy history, immunotherapy regimen, LDH, and lung status score. ROC curve analysis showed the AUC was 0.878 (95% CI: 0.813-0.942). The calibration curve demonstrated the good consistency between the predicted risk probability of CIP and the observed outcomes; DCA indicated that the model had favorable clinical benefits. Conclusions:The constructed nomogram prediction model shows a good predictive performance.

Objective:This study aimed to analyze the prognostic risk factors for hepatoid adenocarcinoma of the stomach (HAS) and construct two nomogram-based clinical prediction models to predict overall survival (OS) and recurrence-free survival (RFS) in patients with HAS.Methods:Data were retrospectively collected from 82 patients (64 males, 18 females; mean age 60.3 ± 9.4 years) who underwent radical gastrectomy and were pathologically diagnosed with gastric hepatoid adenocarcinoma at the First Medical Center of the PLA General Hospital between February 2006 and September 2023. Statistical analyses were conducted using SPSS 25.0 and R 4.3.2. Survival analyses were performed using the Kaplan-Meier method, and univariate analyses were used to identify clinical and pathological factors associated with prognosis. Variables with P<0.05 in the univariate analysis were included in multivariate Cox regression models to identify independent risk factors for OS and RFS. These factors were incorporated into the prediction models to construct nomograms. The discriminatory power of the models was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) analyses, while calibration curves, decision curve analysis (DCA), and comparisons with the 8th edition of the TNM staging system of the American Joint Committee on Cancer (AJCC) were employed to evaluate model performance. Results:Among the 82 patients, 36 (43.9%) exhibited vascular infiltration, 61 (74.4%) had nerve infiltration, and lymph node metastasis was observed in 60 cases (73.2%). Pathological stages I, II, III, and IV were distributed as 11 (13.4%), 26 (31.7%), 44 (53.7%), and 1 (1.2%) cases, respectively. Inflammatory markers included neutrophil-to-lymphocyte ratio (NLR) ≥ 4.33 in 22 cases (26.8%), platelet-to-lymphocyte ratio (PLR) ≥ 142.2 in 50 cases (61.0%), monocyte-to-lymphocyte ratio (MLR) ≥ 0.411 in 22 cases (26.8%), α-fetoprotein (AFP) ≥ 2.48 μg/L in 64 cases (78.0%), and C-reactive protein (CRP) ≥ 7.506 mg/L in 12 cases (14.6%). Among the 82 patients, 3 cases (3.6%) were lost to follow-up. The median follow-up time was 52 (range: 8–147) months, with a median OS of 61(2–147) months. The 1-year and 3-year OS rates were 78.5% and 58.5%, respectively, while the 1-year and 3-year RFS rates were 77.3% and 60.3%, respectively. Multivariate analysis identified several independent risk factors influencing OS in patients with HAS: advanced pathological stage, MLR ≥ 0.411, AFP ≥ 2.545 μg/L, and CRP ≥ 7.51 mg/L. The hazard ratios (HRs) and 95% confidence intervals (CIs) were as follows: 5.218 (1.230–22.143), 2.610 (1.287–5.294), 2.950 (1.013–8.589), and 2.594 (1.145–5.877), respectively (all P < 0.05). For RFS, advanced pathological stage, PLR ≥ 152.0, and MLR ≥ 0.411 were independent risk factors, with HRs (95% CIs) of 4.735 (1.080–20.760), 3.759 (1.259–11.226), and 2.714 (1.218–6.048), respectively (all P < 0.05). The AUC values for OS prediction at 1 year, 3 years, and 5 years were 0.7765, 0.7525, and 0.7702, respectively. For RFS, the AUC values were 0.7304, 0.8137, and 0.8307 at 1 year, 3 years, and 5 years, respectively. The calibration curves demonstrated strong agreement between nomogram- predicted outcomes and observed survival data. DCA indicated that both TNM staging and the nomogram-based clinical prediction models provided a net positive benefit in predicting OS and RFS in HAS patients, with the nomogram model demonstrating superior performance. Conclusion:The nomogram-based clinical prediction models developed in this study demonstrated robust performance in predicting long-term OS and RFS in patients with HAS.

Objective:This study aimed to analyze the prognostic risk factors for hepatoid adenocarcinoma of the stomach (HAS) and construct two nomogram-based clinical prediction models to predict overall survival (OS) and recurrence-free survival (RFS) in patients with HAS.Methods:Data were retrospectively collected from 82 patients (64 males, 18 females; mean age 60.3 ± 9.4 years) who underwent radical gastrectomy and were pathologically diagnosed with gastric hepatoid adenocarcinoma at the First Medical Center of the PLA General Hospital between February 2006 and September 2023. Statistical analyses were conducted using SPSS 25.0 and R 4.3.2. Survival analyses were performed using the Kaplan-Meier method, and univariate analyses were used to identify clinical and pathological factors associated with prognosis. Variables with P<0.05 in the univariate analysis were included in multivariate Cox regression models to identify independent risk factors for OS and RFS. These factors were incorporated into the prediction models to construct nomograms. The discriminatory power of the models was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) analyses, while calibration curves, decision curve analysis (DCA), and comparisons with the 8th edition of the TNM staging system of the American Joint Committee on Cancer (AJCC) were employed to evaluate model performance. Results:Among the 82 patients, 36 (43.9%) exhibited vascular infiltration, 61 (74.4%) had nerve infiltration, and lymph node metastasis was observed in 60 cases (73.2%). Pathological stages I, II, III, and IV were distributed as 11 (13.4%), 26 (31.7%), 44 (53.7%), and 1 (1.2%) cases, respectively. Inflammatory markers included neutrophil-to-lymphocyte ratio (NLR) ≥ 4.33 in 22 cases (26.8%), platelet-to-lymphocyte ratio (PLR) ≥ 142.2 in 50 cases (61.0%), monocyte-to-lymphocyte ratio (MLR) ≥ 0.411 in 22 cases (26.8%), α-fetoprotein (AFP) ≥ 2.48 μg/L in 64 cases (78.0%), and C-reactive protein (CRP) ≥ 7.506 mg/L in 12 cases (14.6%). Among the 82 patients, 3 cases (3.6%) were lost to follow-up. The median follow-up time was 52 (range: 8–147) months, with a median OS of 61(2–147) months. The 1-year and 3-year OS rates were 78.5% and 58.5%, respectively, while the 1-year and 3-year RFS rates were 77.3% and 60.3%, respectively. Multivariate analysis identified several independent risk factors influencing OS in patients with HAS: advanced pathological stage, MLR ≥ 0.411, AFP ≥ 2.545 μg/L, and CRP ≥ 7.51 mg/L. The hazard ratios (HRs) and 95% confidence intervals (CIs) were as follows: 5.218 (1.230–22.143), 2.610 (1.287–5.294), 2.950 (1.013–8.589), and 2.594 (1.145–5.877), respectively (all P < 0.05). For RFS, advanced pathological stage, PLR ≥ 152.0, and MLR ≥ 0.411 were independent risk factors, with HRs (95% CIs) of 4.735 (1.080–20.760), 3.759 (1.259–11.226), and 2.714 (1.218–6.048), respectively (all P < 0.05). The AUC values for OS prediction at 1 year, 3 years, and 5 years were 0.7765, 0.7525, and 0.7702, respectively. For RFS, the AUC values were 0.7304, 0.8137, and 0.8307 at 1 year, 3 years, and 5 years, respectively. The calibration curves demonstrated strong agreement between nomogram- predicted outcomes and observed survival data. DCA indicated that both TNM staging and the nomogram-based clinical prediction models provided a net positive benefit in predicting OS and RFS in HAS patients, with the nomogram model demonstrating superior performance. Conclusion:The nomogram-based clinical prediction models developed in this study demonstrated robust performance in predicting long-term OS and RFS in patients with HAS.

Objective:To investigate the value of a cross-combination machine learning approach in constructing a PET radiomics score (RadScore) for predicting early treatment response and prognosis in patients with primary gastrointestinal diffuse large B-cell lymphoma (PGI-DLBCL).Methods:This retrospective cohort study was conducted on 108 patients (59 males and 49 females, age (55.6±12.1) years) diagnosed with PGI-DLBCL between November 2016 and December 2021 at Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University ( n=85) and West China Hospital, Sichuan University ( n=23). Patients were divided into a training set ( n=86) and a validation set ( n=22) with the ratio of 8∶2 using stratified random sampling method. Seven machine learning models were employed to generate 49 feature selection-classification candidates, and the optimal candidate was selected to construct the RadScore, with five-fold cross-validation applied to determine the best-performing model. Logistic regression analysis was performed to identify risk factors for early treatment response, and a radiomics nomogram was developed by integrating RadScore with clinical predictors. Survival results between different groups of RadScore was compared by log-rank test. Results:Nineteen predictive features were selected from 111 radiomic features to construct the RadScore. In the training set, lactate dehydrogenase (LDH) (odds ratio ( OR)=3.53, 95% CI: 1.21-10.31, P=0.021), intestinal involvement ( OR=3.04, 95% CI: 1.04-8.88, P=0.042), total lesion glycolysis (TLG; OR=6.73, 95% CI: 2.23-20.29, P<0.001) and RadScore ( OR=15.11, 95% CI: 3.95-57.80, P<0.001) were identified as independent risk factors for predicting early treatment response. The combined model integrating RadScore, LDH, intestinal involvement, and TLG demonstrated good discriminatory ability for early treatment response (AUC=0.860 in the training set; AUC=0.902 in the validation set). Significant differences were observed in progression-free survival (PFS) and overall survival (OS) between different RadScore groups ( χ2 values: 13.92 and 8.56, both P<0.01). Conclusions:The machine learning-based RadScore may effectively predict survival outcomes in patients with PGI-DLBCL. The combined model integrating RadScore, clinical factors, and metabolic indicators can predict early treatment response in PGI-DLBCL patients.