Objective: To investigate the incidence, characteristics, and influencing factors of resolution discrepancies within the minipool (MP) testing model across Chinese blood station laboratories in 2024. Methods: A nationwide, multicenter, cross-sectional study was conducted, including 334 blood station laboratories that reported nucleic acid reactive data among enzyme immunoassay non-reactive samples. Of these, 296 laboratories adopted the pool resolution model, with a total of 12 536 273 samples tested. Systematic analysis was performed on resolution data, focusing on the MP-NAT reactivity rate, the pool resolution concordance rate, and the resolution discrepancy rate. Subgroup analyses were conducted based on reagent types, viral targets, and Ct values. Potential causes were further explored through laboratory surveys and re-examination of raw amplification curves. Results: In 2024, the national average MP-NAT reactivity rate was 0.15%. The overall pool resolution concordance rate was 57.86%, which showed a gradual decline as Ct values increased across all reagents. The national average resolution discrepancy rate was 0.081‱(102/12 536 273), with 17.91%(53/296) of laboratories reporting at least one discrepancy. Nine reagent types were associated with these events, exhibiting reagent-specific patterns. For Reagent A2, the predominant discrepancy was HBV reactive pools resolving as HIV (36.36%); for Reagent D1, HBV pools frequently resolved as HCV (38.89%); and for Reagent E, the most common pattern was HIV pools resolving as HBV (48.00%). These resolution discrepancies were strongly associated with high Ct values: the median pool Ct for HBV exceeded 38, while those for HCV and HIV both exceeded 40. Investigations across 16 laboratories revealed that most discrepant samples exhibited “tailing” amplification curves, with some cases linked to cross-contamination or reagent batch-specific issues. Conclusion: While the incidence of resolution discrepancies in the MP-NAT model remains low in China, variations exist across different reagents and laboratories. These discrepancies are closely associated with low viral load, reagent performance, and laboratory operational practices.

Objective:To evaluate the utility of the Rapid Axial Roll Test （RART）, Supine Roll Test （SRT）, and Lying-Down Test （LDT） in determining the affected semicircular canal in cases of horizontal semicircular canal benign paroxysmal positional vertigo （HSC-BPPV）. Methods:A total of 330 patients diagnosed with HSCBPPV from September 2022 to September 2023 were collected and divided into three groups based on the different positional tests received: ①SRT Group, ②LDT+SRT Group, ③RART+SRT Group. The trial was divided into two stages: LDT/RART for patients in the first stage, and SRT for patients in the second stage. The elicitation rate of nystagmus among the three groups was compared to evaluate the accuracy in determining the affected semicircular canal in HSCBPPV. Results:Nystagmus was elicited in 84.55% （279/330） of the patients by positional tests. The elicitation rate of nystagmus in the RART+SRT/LDT group was 94.55% （104/110）, in the LDT+SRT group it was 84.11% （90/107）, and in the SRT group it was 69.91% （79/113）. The differences among the three groups were statistically significant （χ²= 23.88, P<0.001）. In the ② and ③ groups, there was a statistically significant difference in the elicitation rate of nystagmus between stage Ⅰ （patients with LDT or RART） （χ²=43.842, P<0.001）. SRT was performed in the stage Ⅱ, and there was a statistically significant difference in nystagmus extraction rate between the two groups （χ² =4.690, P=0.030）. The difference in the proportion of agreement between stage Ⅰ（LDT or RART） and stageⅡ （SRT） in determining the affected side of the semicircular canal was also statistically significant （χ² =40.502, P<0.001）. For patients with a consistent diagnosis of the affected semicircular canal, the difference in cure rate was not significant （P=0.149）. The Kappa statistic indicated substantial agreement between RART and SRT in terms of eliciting nystagmus （agreement 96.36%, Kappa = 0.730, P<0.001）. Conclusion:RART and SRT show a high degree of agreement regarding the elicitation rate of nystagmus. RART is simple and safe, and it can effectively induce the characteristic nystagmus of HSC-BPPV, accurately identify the responsible semicircular canal and provide a more optimized examination protocol for clinical practice in HSCBPPV.
Humans ; Semicircular Canals/physiopathology* ; Benign Paroxysmal Positional Vertigo/diagnosis* ; Female ; Male ; Middle Aged ; Nystagmus, Pathologic/diagnosis* ; Vestibular Function Tests/methods* ; Aged ; Vertigo/diagnosis* ; Adult

[Objective] To obtain accurate data on alanine aminotransferase (ALT) levels among blood donors in China and to explore the necessity of adjusting the qualification criteria for ALT. [Methods] A collaborative study was conducted involving 26 blood centers and 7 central blood stations with an annual testing volume exceeding 100 000 samples. Between December 1 and 15, 2024, pre-donation ALT testing was suspended for 1-2 days for all whole blood donations. ALT levels were measured only post-donation using standard laboratory equipment and reagents. All transfusion-transmitted infectious disease-related serological and nucleic acid testing, including hepatitis E virus (HEV) RNA testing, were performed. Within one week of testing completion, anonymized data on basic donor information, routine test results, and HEV RNA results were collected and statistically analyzed. [Results] A total of 21 345 blood donors were included in the study, with an ALT disqualification rate of 7.6% (1 623/21 345). The disqualification rate was 9.6% (1 453/15 205) for males and 2.8% (170/6 140) for females. There were significant regional variations in both the disqualification rates and levels of ALT, with Shaanxi Province exhibiting the highest disqualification rate (12.3%, 87/710) and Yunnan Province the lowest (2.9%, 19/652). Among the provinces (autonomous regions and municipalities), Beijing recorded the lowest ALT levels. ALT levels varied across different age groups and genders. Among all samples tested by HEV RNA, the HEV RNA positive rate was 0.29‰ (6/21 003). HCV infection was found to directly affect ALT levels, while HBV, HIV, syphilis, and HEV infections did not significantly impact ALT disqualification rates. It is recommended to adjust the ALT qualification criteria to twice the upper limit of the clinical reference range, which would increase the number of eligible blood donations by 6.61% (1 293/19 550). [Conclusion] In China, the ALT levels of blood donors are correlated with gender, age, geographical region, and HCV infection status. Appropriately raising the ALT eligibility criteria to ≤100 U/L for male donors and ≤80 U/L for female donors could expand the pool of eligible donors and reduce the blood discard rate while ensuring blood safety.

Houpo Sanwutang， included in the Catalogue of Ancient Classical Prescriptions （Second Batch）， was first recorded in the Synopsis of Golden Chamber written by ZHANG Zhongjing from the Eastern Han dynasty and was modified by successive generations of medical experts. A total of 37 pieces of effective data involving 37 ancient Chinese medical books were retrieved from different databases. Through literature mining， statistical analysis， and data processing， combined with modern articles， this study employed bibliometrics to investigate the historical origin， composition， decoction methods， clinical application， and other key information. The results showed that the medicinal origin of Houpo Sanwutang was clearly documented in classic books. Based on the conversion of the measurements from the Han Dynasty， it is recommended that 110.4 g Magnolia Officinalis Cortex， 55.2 g Rhei Radix et Rhizoma， and 72 g Aurantii Fructus Immaturus should be taken. Magnolia Officinalis Cortex and Aurantii Fructus Immaturus should be decocted with 2 400 mL water first， and 1 000 mL should be taken from the decocted liquid. Following this， Rhei Radix et Rhizoma should be added for further decoction， and then 600 mL should be taken from the decocted liquid. A single dose of administration is 200 mL， and the medication can be stopped when patients restore smooth bowel movement. Houpo Sanwutang has the effect of moving Qi， relieving stuffiness and fullness， removing food stagnation， and regulating bowels. It can be used in treating abdominal distending pain， guarding， constipation， and other diseases with the pathogenesis of stagnated heat and stagnated Qi in the stomach. The above results provide reference for the future development and research of Houpo Sanwutang.

Objective: To investigate the prevalence of Dengue virus (DENV) infection among voluntary blood donors in Xishuangbanna Dai Autonomous Prefecture, and to evaluate the necessity of implementing nucleic acid testing (NAT) for blood donors during the rainy season (May-October). Methods: Prior to initiating donor screening, the Xishuangbanna Central Blood Center conducted in-house validation of reagent performance and participated in external quality assessment (EQA) organized by the National Center for Clinical Laboratories (NCCL). During the surveillance period (August-October 2024), a total of 2 919 donor samples were screened using a 6-sample mini-pool NAT strategy. Daily internal quality controls were recorded. Samples that tested positive in pooled screening were deconvoluted and retested in duplicate; only those reactive in both replicate wells were sent to the NCCL for confirmatory testing. At NCCL, samples underwent re-testing using five domestic NAT reagents, as well as serological assays for NS1 antigen and DENV-specific IgG/IgM. Confirmed positive samples were further characterized by serotyping, envelope (E) gene sequencing, and phylogenetic analysis using the maximum likelihood method. Results: The DENV NAT reagent demonstrated consistent detection of 40 copies/mL controls in individual donor (ID)-NAT test (mean CT: 35.61±0.40). During the 63-day quality control monitoring, DENV detection remained stable (mean CT: 22.53±0.72). The center achieved full marks in EQA assessments for 2023 and 2024. Three reactive pools were identified in initial screening, and subsequent individual testing confirmed three DENV RNA-positive donors (sample numbers: 2401, 2402, and 2403). The confirmatory test results from NCCL were: all five NAT platforms consistently detected DENV RNA in the three samples; for serological tests, 2 samples (2402, 2403) were positive for NS1 antigen, while all three samples were negative for both IgG and IgM antibodies. DENV serotyping reagents identified DENV-2 in all cases, which were further confirmed as DENV-2 Genotype Ⅱ-Cosmopolitan by E gene sequencing. Phylogenetic analysis indicated that samples 2401 and 2402 clustered with Southeast Asian strains (Thailand/MZ636802.1, Laos/PQ775621.1), while sample 2403 closely matched a previously reported local Yunnan strain (PV544686.1). Conclusion: DENV-2 infection was detected among blood donors in Xishuangbanna during the rainy season, indicating concurrent risks of imported and local transmission. We recommend implementing pooled NAT screening for blood donors in high-risk areas during dengue epidemic seasons, along with strengthened laboratory quality control, to enhance blood safety.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Objective This study aims to develop an early in-hospital temperature management protocol for heat stroke patients and assess its effectiveness,providing guidance for rapid cooling and precise target temperature control.Methods The protocol was developed through a Delphi expert consultation combined with expert panel meetings.A multi-center,non-randomized,historical control study was conducted,utilizing convenience sampling to select heat stroke patients from the emergency departments of 7 tertiary hospitals in Zhejiang Province,China,between June and August 2024 as an experimental group.The protocol was implemented in this group,while the control group consisted of heat stroke patients treated between June and August 2022,prior to protocol implementation.Cooling rates,target temperature attainment rates,and clinical outcomes were compared between the 2 groups.Results The final protocol included 6 primary indicators,23 secondary indicators,and 56 tertiary indicators.After protocol implementation,the experimental group achieved a cooling rate of 0.08(0.05～0.09)℃/min within 0.5 hours,significantly higher than the control group,which had a rate of 0.04(0.02～0.06)℃/min(P＜0.001).The target temperature attainment rates at 0.5 hours and 2.0 hours were 55.93％and 98.31％,respectively,significantly higher than the rates of 15.87％and 61.11％in the control group(P＜0.001).The mechanical ventilation rate,hospitalization rate,ICU admission rate,and mortality rate in the experimental group were 25.42％,61.02％,44.07％,and 8.47％,respectively.Logistic regression analysis revealed that the early in-hospital temperature management protocol significantly reduced the risk of mechanical ventilation and hospitalization in heat stroke patients,with odds ratios(ORs)of 0.294 and 0.300,respectively(both P＜0.05).Conclusion The developed protocol for early in-hospital temperature management in heat stroke patients is scientific,systematic,and practical.It improves cooling rates and target temperature attainment,thereby enhancing the prognosis of heat stroke patients.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.