Objective: To explore a simple, effective, and safe method for excluding false positives and identifying infections by comprehensively evaluating blood donors with reactive HIV screening results, thereby providing a basis for developing management strategies for such donors. Methods: HIV testing data of blood donors from our laboratory from January 2022 to December 2024 were collected. The results of ELISA and nucleic acid testing (NAT) were combined with confirmatory results from the CDC and analyzed. Results: A total of 605 929 samples were tested for HIV over the three-year period, with 682 reactive samples (reactive rate: 11.25 per 10 000). All were sent to the CDC for Western blot (WB) confirmation, resulting in 53 confirmed positives ((confirmed positive rate: 7.77%). Among these, 619 samples showed isolated HIV Ag&Ab reactivity with non-reactive NAT (HIV Ag&Ab+-&HIV RNA or NAT NR), with a confirmed infection rate of 0%; 9 samples showed dual HIV Ag&Ab reactivity with non-reactive NAT (HIV Ag&Ab++&HIV RNA NR or NAT NR), also with 0% confirmed infection; 52 samples showed dual HIV Ag&Ab reactivity and reactive NAT (HIV Ag&Ab++&HIV RNA R or NAT R), all confirmed as positive (100% infection rate); and 2 HIV Ag&Ab dual-reactive samples without NAT detection were also confirmed infected (100%). For all four HIV Ag&Ab assays, the S/CO values in the true positive group with dual reactivity were significantly higher than those in the false-positive groups (P<0.05). The S/CO distributions for both single-reactive false positives and dual-reactive false positives were narrow, with the upper box (Q3, 75th percentile) below optimal cutoff values in all cases (The optimal cutoff values for the four reagents were 5.00, 11.67, 8.50, and 20.90, respectively). Conclusion: Blood donors with positive NAT results in HIV blood screening are permanently deferred. Donors with dual positive HIV Ag&Ab but negative NAT results are classified and managed based on the S/CO values of HIV Ag&Ab and the optimal screening thresholds. Donors with single positive HIV Ag&Ab but negative NAT results are placed under evaluation status and retain their eligibility to donate blood. Optimizing the management measures for blood donors and establishing a scientific stratified management and assessment mechanism can effectively maintain the stability of the blood donor team.

With the rising incidence of breast cancer among women, neoadjuvant chemotherapy (NAC) is becoming increasingly crucial as a preoperative treatment modality, enabling tumor downstaging and volume reduction. However, its efficacy varies significantly among patients, underscoring the importance of predicting pathological complete response (pCR) following NAC. Early research relied on statistical methods to integrate clinical data for predicting treatment outcomes. With the advent of artificial intelligence (AI), traditional machine learning approaches were subsequently employed for efficacy prediction. Deep learning emerged to dominate this field, and demonstrated the capability to automatically extract imaging features and integrate multimodal data for pCR prediction. This review comprehensively examined the applications and limitations of these three methodologies in predicting breast cancer pCR. Future efforts must prioritize the development of superior predictive models to achieve precise predictions, integrate them into clinical workflows, enhance patient care, and ultimately improve therapeutic outcomes and quality of life.
Humans ; Breast Neoplasms/pathology* ; Neoadjuvant Therapy ; Artificial Intelligence ; Female ; Machine Learning ; Deep Learning ; Chemotherapy, Adjuvant ; Treatment Outcome

OBJECTIVE: To explore the potential effects and mechanisms of Liang-Ge-San (LGS) for the treatment of acute respiratory distress syndrome (ARDS) through network pharmacology analysis and to verify LGS activity through biological experiments. METHODS: The key ingredients of LGS and related targets were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. ARDS-related targets were selected from GeneCards and DisGeNET databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using the Metascape Database. Molecular docking analysis was used to confirm the binding affinity of the core compounds with key therapeutic targets. Finally, the effects of LGS on key signaling pathways and biological processes were determined by in vitro and in vivo experiments. RESULTS: A total of LGS-related targets and 496 ARDS-related targets were obtained from the databases. Network pharmacological analysis suggested that LGS could treat ARDS based on the following information: LGS ingredients luteolin, wogonin, and baicalein may be potential candidate agents. Mitogen-activated protein kinase 14 (MAPK14), recombinant V-Rel reticuloendotheliosis viral oncogene homolog A (RELA), and tumor necrosis factor alpha (TNF-α) may be potential therapeutic targets. Reactive oxygen species metabolic process and the apoptotic signaling pathway were the main biological processes. The p38MAPK/NF-κ B signaling pathway might be the key signaling pathway activated by LGS against ARDS. Moreover, molecular docking demonstrated that luteolin, wogonin, and baicalein had a good binding affinity with MAPK14, RELA, and TNF α. In vitro experiments, LGS inhibited the expression and entry of p38 and p65 into the nucleation in human bronchial epithelial cells (HBE) cells induced by LPS, inhibited the inflammatory response and oxidative stress response, and inhibited HBE cell apoptosis (P<0.05 or P<0.01). In vivo experiments, LGS improved lung injury caused by ligation and puncture, reduced inflammatory responses, and inhibited the activation of p38MAPK and p65 (P<0.05 or P<0.01). CONCLUSION LGS could reduce reactive oxygen species and inflammatory cytokine production by inhibiting p38MAPK/NF-κ B signaling pathway, thus reducing apoptosis and attenuating ARDS.
Drugs, Chinese Herbal/pharmacology* ; Respiratory Distress Syndrome/enzymology* ; p38 Mitogen-Activated Protein Kinases/metabolism* ; NF-kappa B/metabolism* ; Animals ; Signal Transduction/drug effects* ; Molecular Docking Simulation ; Humans ; Male ; Network Pharmacology ; Apoptosis/drug effects* ; Mice

Objective To evaluate clinical effect of remote ischemic conditioning(RIC)in patients with spontaneous intracerebral hemorrhage(sICH).Methods Sixty-four patients with sICH who were diagnosed and admitted to the Department of Neurosurgery,the Sixth Medical Center of PLA General Hospital from January 2023 to May 2024 were selected as research subjects.Following the principle of matching baseline characteristics between groups,a computerized random grouping program was used to divide them into an observation group of 32 cases and a control group of 32 cases.Patients in the control group received standard basic medication according to the'Chinese Clinical Management Guidelines for Cerebrovascular Diseases',while the observation group received RIC treatment in addition to the control group's treatment,with a course of 14 d.The changes in National Institutes of Health Stroke Scale(NIHSS)scores and Barthel index(BI,daily living ability score table)on the day of admission and after 14 d of treatment,the changes in hematoma volume on computed tomography(CT)imaging,and the incidence of adverse events were compared between the two groups.Results At admission,the difference is not statistically significant in NIHSS scores,BI,and CT imaging hematoma volume between the two groups(P>0.05).After 14 d of treatment,the NIHSS scores of both groups were reduced compared to before treatment,and the scores of the observation group were lower than those of the control group(P<0.05);the BI of both groups was increased compared to before,and the scores of the observation group were higher than those of the control group(P<0.05);there were no significant differences in CT imaging hematoma volume and the incidence of adverse events between the two groups(P>0.05).Conclusion Continuous RIC treatment for 14 d is safe and well-tolerated in patients with spontaneous intracerebral hemorrhage and can effectively improve the neurological function of patients.

Objective:To analyze the association between healthcare workers mental health,institu-tional supplies and facilities,inter-organizational coordination during infectious disease outbreaks,and the healthcare institution resilience.Methods:An online questionnaire survey was conducted among the healthcare workforce from 146 institutions in Beijing from January 13,2023 to February 9,2023,and a total of 1 434 eligible respondents were included.The sample comprised 408 responses from tertiary hos-pitals,117 from secondary hospitals,and 909 from primary care institutions.The resilience indicator for healthcare institutions was defined as the degree to which medical services met patient demands,with in-fluencing factors including physical factors,such as material shortages and facility space adaptation or ex-pansion,organizational factors such as information sharing and patient referral,and psychological factors were evaluated using job satisfaction(extrinsic satisfaction,intrinsic satisfaction),burnout(emotional exhaustion,depersonalization,reduced personal accomplishment),and depression status.Ordered mul-ticlassification Logistic regression was used to examine the impact of various factors on the degree to which healthcare services met patient needs;additionally,demographic factors that might influence institutional resilience were controlled.Results:During the emergency response phase,93％of hospitals maintained the capacity to meet patient needs,though tertiary hospitals demonstrated significantly higher rates of service inadequacy(21.05％).Material shortages were reported across all institutions,with tertiary hos-pitals experiencing more frequent multi-item shortages.Inter-institutional collaboration patterns revealed substantial variation:87.50％of primary care facilities,42.86％of secondary hospitals,and 31.58％of tertiary hospitals.Healthcare workers across all levels reported mild depressive symptoms and moderate-to-severe burnout levels.Regression analysis showed high satisfaction(overall satisfaction β=0.04,ex-trinsic satisfaction β=0.06,and intrinsic satisfaction β=0.08),low degree of job burnout(emotional exhaustion β=-0.04,depersonalization β=-0.07 and reduced personal accomplishment β=0.01),low degree of depression(β=-0.06)were significantly associated with higher healthcare institution re-silience.In addition,material shortages were significantly associated with lower resilience,and renova-tion and expansion of treatment spaces,and information sharing,were all associated with higher resilience.Demographic factors(age,gender,marital status,educational background,etc.)had no sig-nificant impact on resilience.Conclusion:Mental health status significantly influences healthcare institu-tion resilience.As human resources constitute the core asset of healthcare institutions,strategic optimiza-tion of workforce allocation and psychological support interventions can effectively strengthen resilience.Moreover,healthcare institution resilience is positively impacted by orderly material supply chains,timely resource distribution,and adaptive reconfiguration of clinical spaces.Finally,facilitating information sharing also enhances institutional resilience.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:This study aims to explore the job preferences of county-level Centers for Disease Control and Prevention(CDC)personnel and to provide a basis for the development of effective incentive mechanisms.Methods:This study used a combination of stratified sampling and latent class sampling to investigate 1 809 respondents from 56 county-level CDCs in Shandong Province,Hubei Province,and Guizhou Province.Data were analysed using a mixed logit model and a latent class model,and willingness to pay was calculated.Results:The results of the mixed logit model showed that,all attributes and their levels had a significant influence(P<0.05),with establishment being the most important motivating factor(β=2.249).In the latent class model,respondents were divided into three categories.The main differences between the three classes were the choice of exit options and differences in preferences for job attributes.Conclusion:County-level CDC personnel preferred jobs with higher incomes,very good benefit levels,establishment,low workload,better recognition and respect from the public,more opportunities for career advancement,and abundant training opportunities.It is recommended that the total number of establishment should be rationally controlled and dynamically adjusted to balance the differences between working conditions within and outside the establishment;that a comprehensive approach should be adopted to improve both hygiene and motivation factors;and that different incentives should be adopted for different categories of CDC staff. Those who are willing to make a change should be provided with more opportunities for training and career advancement.

Objective To investigate the acquisition of the"standard for intubation and maintenance of nasointestinal tube in adult patients"of Chinese Nursing Association,and its influencing factors,so as to provide a basis for targeted training programs.Methods A multi-centered,cross-sectional study was performed in 31 provinces from September to November 2023,and nurses from different departments which use nasointestinal tubes like intensive care units,gastroenterology,neurology,geriatrics were included by a convenient sampling method.The tool was a self-designed questionnaire based on the group standard and the survey was conducted.Multiple linear regression analysis was used to explore the influencing factors of nurses'knowledge of nasointestinal tubes intubation and maintenance.Results 1 350 valid questionnaires were collected.Only 61.63％of the respondents knew about the publishing of the standard.The score of knowledge of tube intubation and maintenance was(61.09±13.56).The results of multiple linear regression analysis showed the influencing factors of the score of knowledge of intubation and maintenance were as follows:education level,professional title,job position,intubation experience within half a year,and corresponding achievements(P＜0.05).Conclusion The acqui-sition level of nurses for the standard calls for continuous promotion.Nursing managers should establish targeted training programs based on the related influencing factors,so as to advance the implementation of the group standard.

Objective To explore the differences in the radiological features,clinical,and laboratory indicators of frequent versus infrequent gout flares in the feet and ankles using dual-energy computed tomography(DECT).Methods A retrospective selection was made on 385 gout patients,who were divided into the frequent flare group(≥2 gout attacks per year,219 cases)and the infre-quent flare group(<2 gout attacks per year,166 cases).Clinical data,laboratory indicators,and DECT imaging findings were col-lected for statistical analysis.Binary logistic regression was used to analyze the independent risk factors for frequent gout flares and receiver operating characteristic(ROC)curve was plotted.Results Statistically significant differences were found between the fre-quent flare group and the infrequent flare group in terms of disease duration,body mass index(BMI),blood pressure,triglyceride(TG),serum uric acid(SUA),monosodium urate(MSU)crystal deposition,total volume of MSU crystals,maximum diameter of individ-ual tophi,number of affected joints,bone erosion,maximum depth of bone erosion,soft tissue swelling,bone proliferation and sclero-sis,and joint space narrowing(P<0.05).SUA levels,MSU crystal deposition,total volume of MSU crystals,and maximum depth of bone erosion were identified as independent risk factors for frequent gout(P<0.05).Both the combination of four factors model and the maximum depth of bone erosion model had better diagnostic efficacy.Conclusion Gout patients with high SUA levels,MSU crystal deposition,larger total volume of MSU crystals,and greater maximum depth of bone erosion are more likely to experience frequent gout attacks.Patients with bone erosion depth>3.200 mm are more likely identified early as having frequent gout.

Objective:To explore the factors that can predict the outcome of non-invasive ventilation (NIV) in elderly patients with moderate acute respiratory distress syndrome (ARDS) and analyze their clinical application value.Methods:A total of 87 elderly patients with moderate ARDS caused by community-acquired pneumonia admitted to the Emergency Department of Beijing Friendship Hospital from January 2023 to December 2023 were retrospectively selected. Taking the need for conversion to invasive ventilation during treatment as the study endpoint, the patients were divided into NIV success group and failure group. Clinical data of patients were collected every 2 hours starting from 2 hours after the initiation of NIV, and differences between the two groups were compared. The predictive efficacy of potential risk factors was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC).Results:Among the 87 patients, 38 cases failed NIV within 48 hours (overall failure rate 43.7%), including 18 cases failing within the first 12 hours and 20 new failures from 12 to 48 hours, while the remaining 49 cases succeeded. There were no statistically significant differences between the two groups in gender, age, prevalence of hypertension and diabetes, left ventricular ejection fraction, serum creatinine, total bilirubin, heart rate, N-terminal pro-brain natriuretic peptide (NT-proBNP), inspiratory positive airway pressure (IPAP), arterial blood pH, partial pressure of arterial carbon dioxide (PaCO 2), oxygenation index (PaO 2/FiO 2), mean arterial pressure, Sequential Organ Failure Assessment (SOFA) score, respiratory rate and other indicators (all P>0.05). The failure group had a significantly lower baseline VOX index [(SpO 2/FiO 2)/VT] than the success group. From 2 to 12 hours after the initiation of NIV (a total of 6 recording time points), the VOX index in the failure group was significantly lower than that in the success group (all P<0.001). ROC curve analysis showed that the VOX index had the best predictive efficacy at 4-6 hours after NIV initiation: at 4 hours, the AUC was 0.929, and the optimal cut-off value of 24.72 had the highest sensitivity (91.84%) for predicting failure; at 6 hours, the AUC was 0.870, and the optimal cut-off value of 24.58 had the highest specificity (90.91%) for predicting failure. Conclusions:The VOX index can effectively predict the failure outcome of NIV in elderly patients with moderate ARDS, and its predictive ability is the strongest at 4-6 hours after the initiation of NIV. Clinically, the 4-hour or 6-hour time window can be selected to evaluate the prognosis according to the patient′s specific conditions (such as respiratory drive intensity, weaning risk), providing a reference for timely adjustment of respiratory support plans.