bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effectiveness and safety of ginkgo biloba preparations in the treatment of Alzheimer's disease: A systematic review and meta-analysis
Authors: Li Dawei , Ma Jinlong , Wei Baojian , Gao Shuang , Lang Yanmei , Wan Xueying 
Published: March 2023

ObjectiveTo conduct a meta-analysis of the effectiveness and safety of ginkgo biloba preparations combined with donepezil hydrochloride vs. donepezil for the treatment of Alzheimer's disease (AD).MethodsThree English databases (Cochrane Library, PubMed, EMBASE), and four Chinese databases [the China National Knowledge Infrastructure (CKNI), the Chinese Biomedical Literature database (CBM), the Chongqing VIP database, and WANFANG DATA)] were manually searched for literature published from the respective dates of inception of the databases to December 2022. The randomized controlled trials (RCTs) of ginkgo biloba preparations with donepezil hydrochloride vs. donepezil for the treatment of AD were included. Relevant literature was screened, and the data in the included studies were extracted for quality assessment according to the Risk of bias tool. The RevMan 5.3 software was used for meta-analysis.ResultsA total of 1,642 participants were enrolled in the 18 RCTs. Of these, 842 were in the experimental group (ginkgo biloba preparations combined with donepezil hydrochloride) and 800 were in the control group (donepezil). The overall methodological quality of the included RCTs is poor due to the high risks of blindness and allocation concealment. The meta-analysis results showed statistically significant differences in several outcomes including Risk Ratio (RR) in change for clinical effectiveness rate (1.23, 95% CI 1.13, 1.34, P &lt; 0.00001), mean difference (MD) in change for Mini-Mental State Examination score (3.02, 95% CI 2.14, 3.89, P &lt; 0.00001), Activity of Daily Living Scale score (−4.56, 95% CI −5.09, −4.03, P &lt; 0.00001), Hasegawa Dementia Scale score (2.04, 95% CI 1.74, 2.34, P &lt; 0.00001), Montreal Cognitive Assessment score (2.38, 95% CI 0.72, 4.06, P = 0.005), between the experimental and control groups. But there is no statistically significant difference in change for adverse reaction (0.91, 95% CI 0.58, 1.42, P = 0.69).ConclusionGinkgo biloba preparations plus donepezil can improve clinical effectiveness rate and vocabulary memory outcomes. However, more relevant high-quality RCTs are needed in the future to validate these results.Systematic review registrationIdentifier CRD42022378970.
Risk factors for and outcomes of poststroke pneumonia in patients with acute ischemic stroke treated with mechanical thrombectomy
Authors: Zhang Ping , Chen Lei , Jiang Yi , Yuan Hui , Zhu Xuan , Zhang Minmin , Wu Tao , Deng Benqiang , Yang Pengfei , Zhang Yongwei , Liu Jianmin 
Published: March 2023

ObjectiveThe purpose of the study was to assess the risk factors for poststroke pneumonia (PSP) and its association with the outcomes in patients with acute ischemic stroke (AIS) due to large artery occlusion treated with mechanical thrombectomy (MT).MethodsConsecutive patients with AIS who underwent MT from January 2019 to December 2019 in the stroke center of Changhai Hospital were identified retrospectively. All of the patients were evaluated for the occurrence of PSP while in the hospital, and their modified Rankin scale (mRS) scores were assessed 90 days after having a stroke. Logistic regression analysis was conducted to determine the independent predictors of PSP, and the associations between PSP and clinical outcomes were analyzed.ResultsA total of 248 patients were enrolled, of whom 33.47% (83) developed PSP. Logistic regression analysis revealed that body mass index (BMI) [unadjusted odds ratio (OR) 1.200, 95% confidence interval (CI) 1.038–1.387; p = 0.014], systemic immune-inflammation index (SII) (OR 1.001, 95% CI 1.000–1.002; p = 0.003), dysphagia (OR 9.498, 95% CI 3.217–28.041; p &lt; 0.001), and intubation after MT (OR 4.262, 95% CI 1.166–15.581; p = 0.028) were independent risk factors for PSP. PSP was a strong predictor of clinical outcomes: it was associated with functional independence (mRS score ≤ 2) (OR 0.104, 95% CI 0.041–0.260; p &lt; 0.001) and mortality at 90 days (OR 3.010, 95% CI 1.068–8.489; p = 0.037).ConclusionMore than one in three patients with AIS treated with MT developed PSP. Dysphagia, intubation, higher BMI, and SII were associated with PSP in these patients. Patients with AIS who develop PSP are more likely to experience negative outcomes. The prevention and identification of PSP are necessary to reduce mortality and improve clinical outcomes.
Causes, factors, and complications associated with hospital admissions among patients with Parkinson's disease
Authors: Shaibdat Navena Sharma , Ahmad Norfazilah , Azmin Shahrul , Ibrahim Norlinah Mohamed 
Published: March 2023

IntroductionPatients with Parkinson's disease (PD) are at a higher risk of hospital admissions compared to the general population. We studied the causes and factors associated with admissions among patients with PD over 6 years.MethodsWe included all PD admissions between 1 January 2016 and 31 December 2021. Other causes of parkinsonism were excluded. Causes of admissions were divided into PD-related (direct or indirect) or non-PD-related. The type of admission was categorized into emergency or elective.ResultsWe identified 605 hospital admissions (259 patients with PD); 345 (57.0%) were PD-related and 260 (43%) were non-PD-related. Emergency PD admissions contributed to 50.4% of all admissions, most commonly from respiratory infection (23%). PD admissions in comparison to non-PD admissions were associated with worse disease severity (HY ≥ 3; p &lt; 0.001), longer disease duration [8.71 (SD 6.23) vs. 6.60 (SD 5.39) years; p &lt; 0.001], and longer hospital stay [7.70 (SD 5.89) vs. 6.42 (SD 7.63) days; p = 0.020]. Non-PD admissions were associated with more comorbidities (97.3%; p = 0.013). There were 124 (20.5%) complications and 31 deaths (5.1%). A total of 29 deaths were due to respiratory infection and 3 deaths were due to COVID-19 pneumonia. Emergency admission (PD- and non-PD-related; p = 0.001) and respiratory-related causes (p &lt; 0.001) were predictors of unfavorable hospital admission outcomes (death and complications).ConclusionRespiratory infection was the leading cause of hospital admission and a significant independent predictor of unfavorable hospital admission outcomes (death and complications). PD-related admissions were associated with disease severity and led to more complications and longer hospital stays. Non-PD-related admissions were associated with comorbidities.
Alpha peak activity in resting-state EEG is associated with depressive score
Authors: Zhou Peng , Wu Qian , Zhan Liying , Guo Zhihan , Wang Chaolun , Wang Shanze , Yang Qing , Lin Jiating , Zhang Fangyuan , Liu Lu , Lin Dehui , Fu Wenbin , Wu Xiang 
Published: March 2023

IntroductionDepression is a serious psychiatric disorder characterized by prolonged sadness, loss of interest or pleasure. The dominant alpha peak activity in resting-state EEG is suggested to be an intrinsic neural marker for diagnosis of mental disorders.MethodsTo investigate an association between alpha peak activity and depression severity, the present study recorded resting-state EEG (EGI 128 channels, off-line average reference, source reconstruction by a distributed inverse method with the sLORETA normalization, parcellation of 68 Desikan–Killiany regions) from 155 patients with depression (42 males, mean age 35 years) and acquired patients’ scores of Self-Rating Depression Scales. We measured both the alpha peak amplitude that is more related to synchronous neural discharging and the alpha peak frequency that is more associated with brain metabolism.ResultsThe results showed that over widely distributed brain regions, individual patients’ alpha peak amplitudes were negatively correlated with their depressive scores, and individual patients’ alpha peak frequencies were positively correlated with their depressive scores.DiscussionThese results reveal that alpha peak amplitude and frequency are associated with self-rating depressive score in different manners, and the finding suggests the potential of alpha peak activity in resting-state EEG acting as an important neural factor in evaluation of depression severity in supplement to diagnosis.
Geriatric nutritional risk index predicts all-cause mortality in the oldest-old patients with acute coronary syndrome: A 10-year cohort study
Authors: Li Ying , Shen Jian , Hou Xiaoling , Su Yongkang , Jiao Yang , Wang Jihang , Liu Henan , Fu Zhenhong 
Published: March 2023

Background and objectiveNutritional status assessment in acute coronary syndrome (ACS) patients has been neglected for a long time. The geriatric nutritional risk index (GNRI) is a sensitive indicator for assessing the nutritional status of the elderly. This study aims to explore the association between GNRI and all-cause mortality in the oldest-old patients with ACS.MethodsThe patients who met the inclusion criteria were consecutively enrolled from January 2006 to December 2012. Clinical data were collected on admission, and all subjects were followed after being discharged. The nutritional status was evaluated using GNRI. The relationship between GNRI and all-cause mortality was assessed by using different analyses.ResultsA total of 662 patients with a mean age of 81.87 ± 2.14 years old were included in our study, and followed (median: 63 months, IQR 51–71). Patients whose GNRI ≤ 98 were reported as at risk of malnutrition (31.11%, n = 206). In multivariable analysis, we found that for each SD increase in GNRI, the risk of all-cause mortality lowered by 23%, and the HR for GNRI ≤ 98 was 1.39 (95% CI 1.04–1.86). After stratifying patients into three groups by tertiles of GNRI, we found that the HRs for tertile 2 and tertile 3 were 1.49 (95% CI 1.02–2.19) and 1.74 (95% CI 1.22–2.50), respectively. The trend test revealed a dose–response relationship between GNRI and all-cause mortality in the oldest-old with ACS. Lastly, in subgroup analyses, we found a reliable association between GNRI and all-cause mortality.ConclusionMalnutrition is common in the oldest-old patients with ACS, and GNRI could predict their long-term all-cause mortality in a dose-dependent manner. GNRI may be a prospective index for risk-stratification and secondary-prevention in the oldest-old patients with ACS.
Effect of vitamin D supplementation on COVID-19 patients: A systematic review and meta-analysis
Authors: Zhang Ying , Li Jing , Yang Min , Wang Qin 
Published: March 2023

ObjectiveTo systematically evaluate the impact of vitamin D supplementation on mortality, ICU admission, and the rates of mechanical ventilation or intubation among COVID-19 patients.Data sources and study selectionThe PubMed, Embase, Cochrane Library, CBM, CNKI, VIP, and WanFang databases were searched from 1 December 2019 to 31 December 2022. The authors sought to identify randomized controlled trials and cohort studies that examined the relationship between vitamin D supplementation and mortality, ICU admission, and mechanical ventilation or intubation rates among COVID-19 patients.Data extraction and synthesisTwo investigators independently searched the literature, extracted the data, and assessed the quality of the included studies. The Grading of Recommendation, Assessment, Development, and Evaluation approach was used to evaluate the quality of the evidence. Meta-analysis was conducted using RevMan 5.3, STATA 15.1, and R 4.1.3 software.ResultsEight randomized controlled trials (RCTs) and eight cohort studies were included, involving 3359 COVID-19 patients. The pooled analysis of randomized controlled trials showed that vitamin D supplementation did not have a significant effect on reducing mortality (Relative Risk, RR = 0.94, 95% CI 0.69–1.29, P = 0.7), while the results of cohort studies indicated that vitamin D supplementation had a positive impact on reducing mortality among COVID-19 patients (RR = 0.33, 95% CI 0.23–0.47, P &lt; 0.001). There was no statistically significant difference in the rates of ICU admission (RCTs: RR = 0.64, 95%CI 0.38–1.08, P = 0.10; cohort studies: RR = 0.32, 95% CI 0.08–1.29, P = 0.109) or rates of mechanical ventilation or intubation (RCTs: RR = 0.77, 95% CI 0.58–1.02, P = 0.07; cohort studies: RR = 0.93, 95% CI 0.55–1.58, P = 0.789).ConclusionThe results of this systematic review and meta-analysis suggest that vitamin D supplementation does not have a significant impact on reducing mortality, ICU admission, and the rates of mechanical ventilation or intubation among COVID-19 patients. However, due to the limited number and quality of the studies included, further high-quality studies are needed to confirm these findings.Systematic review registrationwww.crd.york.ac.uk, identifier CRD42021299521.
BMI, socioeconomic status, and bone mineral density in U.S. adults: Mediation analysis in the NHANES
Authors: Zhang Yun , Tan Caixia , Tan Wenfu 
Published: March 2023

IntroductionThe mechanism by which socioeconomic status (SES) affects bone mineral density (BMD) remains unknown, and body mass index (BMI) may be a potential mediator. The purpose of this study was to investigate whether BMI mediates the relationship between SES [education level and poverty income ratio (PIR)] and lumbar BMD and the proportion it mediates.MethodsThis study included a total of 11,075 adults from the National Health and Nutrition Examination Survey (NHANES). Lumbar BMD was measured at the lumbar spine by dual-energy X-ray absorptiometry (DXA). Multivariate linear regression and smoothing curve fitting were used to investigate the relationship between SES and lumbar BMD. Mediator analysis was used to investigate the proportion of BMI mediating the association between SES and BMD.ResultsIn the fully adjusted model, there was a positive correlation between SES and BMD (education level: β = 0.025, 95% CI: 0.005, 0.045; PIR: β = 0.007, 95% CI: 0.002, 0.011). Mediation analysis showed that BMI mediated the relationship between PIR, education level, and lumbar BMD with a range of mediation proportions from 13.33 to 18.20%.ConclusionBMI partially mediated the positive association between SES and BMD, and this association may be largely mediated by factors other than BMI.
Amelioration of alcohol-induced acute liver injury in C57BL/6 mice by a mixture of TCM phytochemicals and probiotics with antioxidative and anti-inflammatory effects
Authors: Li Zhiguo , Fang Xuexun , Hu Xin , Li Congcong , Wan Youzhong , Yu Dahai 
Published: March 2023

BackgroundThere are many causes of acute liver injury (ALI), such as alcohol, drugs, infection, and toxic materials, which have caused major health problems around the world. Among these causes, alcohol consumption induced liver injury is a common alcoholic liver disease, which can further lead to liver failure even liver cancer. A number of traditional Chinese medicine (TCM) and TCM derived compounds have been used in treating the liver-associated diseases and combination use of probiotics with TCM phytochemicals has attracted interests for enhanced biological effects.MethodsThis study investigated the hepatoprotective effect of TCM-probiotics complex (TCMPC) and its underlying mechanism for the treatment of ALI in mice. The TCMPC is composed of TCM phytochemicals puerarin, curcumin, ginsenosides, and 5 lactobacteria strains. We first established a mouse model of alcohol-induced ALI, then the therapeutic effects of TCMPC on alcohol-induced ALI were monitored. A series of measurements have been performed on antioxidation, anti-inflammation, and lipid metabolism regulation.ResultsThe results showed that TCMPC can reduce the level of liver injury biomarkers and regulate oxidative stress. Histopathological results indicated that TCMPC could ameliorate ALI in mice. In addition, it can also significantly reduce the production of inflammatory cytokines caused by ALI.ConclusionOur research has proved the therapeutic effect of TCMPC on alcohol-induced ALI. The potential mechanism of hepatoprotective effects of TCMPC may be related to its antioxidative and anti-inflammatory effects. Our research might provide a new way for liver disease treatment.
A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules
Authors: Chen Chengyu , Geng Qun , Song Gesheng , Zhang Qian , Wang Youruo , Sun Dongfeng , Zeng Qingshi , Dai Zhengjun , Wang Gongchao 
Published: March 2023

ObjectiveTo establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).Materials and methodsRetrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.ResultsPulmonary malignant nodules were significantly associated with vascular alterations in both the training (p &lt; 0.001) and external validation (p &lt; 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.ConclusionPredictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.
An ultrasound-based radiomics model to distinguish between sclerosing adenosis and invasive ductal carcinoma
Authors: Huang Qun , Nong Wanxian , Tang Xiaozhen , Gao Yong 
Published: March 2023

ObjectivesWe aimed to develop an ultrasound-based radiomics model to distinguish between sclerosing adenosis (SA) and invasive ductal carcinoma (IDC) to avoid misdiagnosis and unnecessary biopsies.MethodsFrom January 2020 to March 2022, 345 cases of SA or IDC that were pathologically confirmed were included in the study. All participants underwent pre-surgical ultrasound (US), from which clinical information and ultrasound images were collected. The patients from the study population were randomly divided into a training cohort (n = 208) and a validation cohort (n = 137). The US images were imported into MaZda software (Version 4.2.6.0) to delineate the region of interest (ROI) and extract features. Intragroup correlation coefficient (ICC) was used to evaluate the consistency of the extracted features. The least absolute shrinkage and selection operator (LASSO) logistic regression and cross-validation were performed to obtain the radiomics score of the features. Based on univariate and multivariate logistic regression analyses, a model was developed. 56 cases from April 2022 to December 2022 were included for independent validation of the model. The diagnostic performance of the model and the radiomics scores were evaluated by performing the receiver operating characteristic (ROC) analysis. The calibration curve and decision curve analysis (DCA) were used for calibration and evaluation. Leave-One-Out Cross-Validation (LOOCV) was used for the stability of the model.ResultsThree predictors were selected to develop the model, including radiomics score, palpable mass and BI-RADS. In the training cohort, validation cohort and independent validation cohort, AUC of the model and radiomics score were 0.978 and 0.907, 0.946 and 0.886, 0.951 and 0.779, respectively. The model showed a statistically significant difference compared with the radiomics score (p&lt;0.05). The Kappa value of the model was 0.79 based on LOOCV. The Brier score, calibration curve, and DCA showed the model had a good calibration and clinical usefulness.ConclusionsThe model based on radiomics, ultrasonic features, and clinical manifestations can be used to distinguish SA from IDC, which showed good stability and diagnostic performance. The model can be considered a potential candidate diagnostic tool for breast lesions and can contribute to effective clinical diagnosis.
The innovative model based on artificial intelligence algorithms to predict recurrence risk of patients with postoperative breast cancer
Authors: Zeng Lixuan , Liu Lei , Chen Dongxin , Lu Henghui , Xue Yang , Bi Hongjie , Yang Weiwei 
Published: March 2023

PurposeThis study aimed to develop a machine learning model to retrospectively study and predict the recurrence risk of breast cancer patients after surgery by extracting the clinicopathological features of tumors from unstructured clinical electronic health record (EHR) data.MethodsThis retrospective cohort included 1,841 breast cancer patients who underwent surgical treatment. To extract the principal features associated with recurrence risk, the clinical notes and histopathology reports of patients were collected and feature engineering was used. Predictive models were next conducted based on this important information. All algorithms were implemented using Python software. The accuracy of prediction models was further verified in the test cohort. The area under the curve (AUC), precision, recall, and F1 score were adopted to evaluate the performance of each model.ResultsA training cohort with 1,289 patients and a test cohort with 552 patients were recruited. From 2011 to 2019, a total of 1,841 textual reports were included. For the prediction of recurrence risk, both LSTM, XGBoost, and SVM had favorable accuracies of 0.89, 0.86, and 0.78. The AUC values of the micro-average ROC curve corresponding to LSTM, XGBoost, and SVM were 0.98 ± 0.01, 0.97 ± 0.03, and 0.92 ± 0.06. Especially the LSTM model achieved superior execution than other models. The accuracy, F1 score, macro-avg F1 score (0.87), and weighted-avg F1 score (0.89) of the LSTM model produced higher values. All P values were statistically significant. Patients in the high-risk group predicted by our model performed more resistant to DNA damage and microtubule targeting drugs than those in the intermediate-risk group. The predicted low-risk patients were not statistically significant compared with intermediate- or high-risk patients due to the small sample size (188 low-risk patients were predicted via our model, and only two of them were administered chemotherapy alone after surgery). The prognosis of patients predicted by our model was consistent with the actual follow-up records.ConclusionsThe constructed model accurately predicted the recurrence risk of breast cancer patients from EHR data and certainly evaluated the chemoresistance and prognosis of patients. Therefore, our model can help clinicians to formulate the individualized management of breast cancer patients.