Numerous techniques for C2 screw fixation have been recently reported. However, concerns remain regarding the risk of spinal cord or vertebral artery injury and inadequate biomechanical stability. To our knowledge, the specific transforaminal “in-out-in” screw fixation technique has not been previously reported. This study aimed to investigate the feasibility and preliminary clinical outcomes of a transforaminal “in-out-in” multi-cortical purchase screw for posterior C2 screw fixation. Between October 2022 and March 2023, 10 patients underwent posterior atlantoaxial internal fixation. All patients had severe hypoplasia of the C2 pedicle on at least one side, precluding the use of standard C2 pedicle screws. A transforaminal “in-out-in” screw was used as an alternative. No spinal cord injury, vascular injury, or other major complications were observed. No implant failure was noted at the final follow-up. In conclusion, the transforaminal “in-out-in” screw may achieve rigid three-column fixation with multiple cortical purchases. It represents a safe and effective alternative for posterior C2 fixation in patients with severely narrow C2 pedicles where traditional pedicle screw placement is not feasible.

Objective: To systematically evaluate the application effectiveness of the full disinfection process in blood stations, identify key optimization links, and provide a scientific evidence for the formulation of specialized disinfection and hygiene standards for blood collection and supply institutions. Methods: On-site microbiological monitoring was conducted using techniques such as the plate settling method and cotton swab method. Stratified random sampling was performed on eight key control points: including evironmental air, hand hygiene, skin disinfection, blood transport boxes, pressure steam sterilization, sewage treatment, ultraviolet disinfection, and sterile swabs. Statistical analysis was performed using SPSS 20.0. Measurement data were expressed as mean ± standard deviation(x-±s), and inter-group comparisons were conducted using t-tests or analysis of variance (ANOVA). Count data were expressed as rates, and inter-group comparisons were conducted using the χ test. Trend analysis was performed using the Cochran-Armitage test, with a significance level of α=0.05. Results: The colony removal rate of plasma air disinfection machines (94.2%±0.8%) was significantly higher than that of the ultraviolet group (P<0.05). The qualification rate of quick-drying disinfectant hand was 100% within 30 days after opening the bottle, but decreased to 93.3% after 40 days (P<0.01). The qualification rate of disinfection for blood transport boxes using 500 mg/L chlorine-containing disinfectant (100%) was significant higher 75% alcohol (60%, P< 0.05). Skin disinfection with 2% alcoholic chlorhexidine gluconate balanced effectiveness and user experience (satisfaction score 4.8±0.3). The qualification rates for core procedures such as pressure steam sterilization and sewage treatment were both 100%. Conclusion: The current disinfection procedures in blood collection and supply institutions are reliable. The effective usage period of hand disinfectants, disinfection methods for transport boxes, and usage duration of sterile swabs are the main points for optimization. It is recommended that the opened hand disinfectants be used for ≤30 days and that chlorine-containing disinfectants be prioritized for wiping transport boxes, providing empirical support for the revision of protocols and the formulation of standards.

Objective To investigate and compare the epidemiological characteristics of common respiratory viruses among influenza-like illness(ILI)and severe acute respiratory infection(SARI)cases in Huzhou,Zhejiang Province before and after the COVID-19 pandemic,so as to provide a basis for formulating and adjusting the prevention and control strategies for viral respiratory infectious diseases.Methods ILI and SARI cases at two influenza surveillance sentinel hospitals in Huzhou and had throat swab samples collected during Nov 2017 to Feb 2020(pre-COVID-19 pandemic period)and Dec 2022 to Apr 2024(post-COVID-19 mitigation phase)were selected as the participants.Seven common viral respiratory pathogens were tested,including influenza A virus(H1N1 and H3N2 subtypes),influenza B virus(Victoria lineage,FluB),respiratory syncytial virus(RSV),rhinovirus(HRV),adenovirus(ADV),and severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).The positive rates of respiratory pathogens before and after the COVID-19 pandemic were compared across different age groups and different time.Results A total of 7 948 ILI samples and 2 294 SARI samples were included.The overall positive rate of ILI samples increased from 33.6%to 47.1%,primarily due to the increase in influenza and COVID-19 infections;the overall positive rate of SARI samples decreased from 31.4%to 24.8%,mainly due to the reduction in HRV and ADV infections.During the post-COVID-19 mitigation phase,SARS-CoV-2(22.1%),H3N2(12.7%),and FluB(6.0%)were the primary pathogens in ILI samples,while RSV(7.1%),H3N2(5.3%),and HRV(4.5%)dominated in SARI samples.During the post-COVID-19 mitigation phase,the influenza virus circulation period was shortened.Before the COVID-19 pandemic,RSV was mainly detected in autumn and winter,while during the post-COVID-19 mitigation phase,out-of-season RSV epidemics were observed in spring and summer.Co-infection rate in ILI cases increased significantly in the post-COVID-19 mitigation phase,predominantly consisting of co-infections of COVID-19 and influenza A virus,while co-infection rate in SARI cases showed a decline.Conclusion We found important epidemiological changes in respiratory viruses in Huzhou during the post-COVID-19 mitigation phase compared to pre-COVID-19 period,including increased positive rates of influenza and COVID-19,and disruptions to the seasonal patterns of influenza and RSV.The prevention and control strategies should be adjusted in a timely manner based on the monitoring data.

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.

To understand the infection status of main intestinal protozoa in BALB/c mice and pro-vide a basis for further control of intestinal protozoa infection.Five hundred and forty BALB/c mice provided by four domestic suppliers of BALB/c mice were detected for intestinal protozoa,in which 140 from supplier A,130 from supplier B,135 from supplier C,and 135 from supplier D,re-spectively.Fresh faecal samples were collected from each mouse separately to extract the genome and amplified by nested PCR based on primers for the 18S rRNA gene sequences of Pent-atrichomonas hominis(P.hominis)and Cryptosporidium tyzzeri(C.tyzzeri),and the 16S-like rRNA gene sequence of Tritrichomonas muris(T.muris)and sequenced.The results showed that the total intestinal protozoan infection rate was 7.1％(10/140)in 140 mice faecal samples provided by supplier A.Among them,the positivity rate of T.muris was 7.1％(10/140),C.tyzzeri was 2.1％(3/140),and P.hominis was 7.1％(10/140),the co-infection rate of two intestinal protozoa was 7.1％(10 mice:T.muris+P.hominis),and three intestinal protozoa was 2.1％(3 mice:T.muris+P.hominis+C.tyzzeri).The total intestinal protozoan infection rate in 135 mice faecal samples provided by supplier C was 7.4％,in which,7.4％(10/135)was positive for T.muris.There are no intestinal protozoa to be detected in 130 mice faecal samples from supplier B and 135 mice faecal samples from supplier D.The homology analysis showed that the homology of ampli-fied sequence of T.muris,P.hominis and C.tyzzeri was 98.52％,98.27％and 99.87％compared with published sequence of GenBank No:AY886846.1,GenBank No:AF156964.1 and GenBank No:KJ000486.1,which was clustered as an independent branch by phylogenetic analysis respec-tively.In conclusion,there are intestinal protozoan infection in BALB/c mice in some animal sup-pliers.The co-infections of more than 3 parasites such as T.muris,P.hominis and C.tyzzeri has been found.It will provide a basis for control of intestinal protozoa infection in BALB/c mice in the future.

Objective To conduct a bibliometric analysis of relevant literature on liver failure caused by viral hepatitis from the past five years,and to help researchers understand the current status and hotspots in this field,and to provide insights into future research trends.Methods Based on the Science Citation Index Expanded(SCI-Expanded)data from Web of Science Core Collection,visualization analysis and mapping were conducted through VOSviewer and CiteSpace software to generate visual representations of international research collaboration networks,keyword co-occurrence clustering,and keyword bursts.Results From 2019 to 2023,a total of 873 relevant literature were included,with a total citation frequency of 7 364 and an average citation frequency of 8.44.Among them,China had the highest number of publications(458 articles,52.46%)and had the most cooperation with the United States.The research hotspots of viral hepatitis induced liver failure were mainly divided into three categories:basic and clinical research on liver failure caused by non-hepatitis B virus(HBV),the pathogenesis of HBV related liver failure,and treatment and prediction models of liver failure.The keyword time overlay map and burst map showed that the research hotspots had gradually shifted from the prevention and control of new infections to the treatment and prognosis assessment of patients with chronic infection.Conclusion China is a major international research entity in liver failure caused by viral hepatitis and actively participates in international scientific collaborations.The research hotspots on liver failure caused by viral hepatitis have gradually shifted from preventing viral hepatitis infections and expanding treatment options to the treatment of chronic infection patients and prognostic prediction.

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 To conduct a bibliometric analysis of relevant literature on liver failure caused by viral hepatitis from the past five years,and to help researchers understand the current status and hotspots in this field,and to provide insights into future research trends.Methods Based on the Science Citation Index Expanded(SCI-Expanded)data from Web of Science Core Collection,visualization analysis and mapping were conducted through VOSviewer and CiteSpace software to generate visual representations of international research collaboration networks,keyword co-occurrence clustering,and keyword bursts.Results From 2019 to 2023,a total of 873 relevant literature were included,with a total citation frequency of 7 364 and an average citation frequency of 8.44.Among them,China had the highest number of publications(458 articles,52.46%)and had the most cooperation with the United States.The research hotspots of viral hepatitis induced liver failure were mainly divided into three categories:basic and clinical research on liver failure caused by non-hepatitis B virus(HBV),the pathogenesis of HBV related liver failure,and treatment and prediction models of liver failure.The keyword time overlay map and burst map showed that the research hotspots had gradually shifted from the prevention and control of new infections to the treatment and prognosis assessment of patients with chronic infection.Conclusion China is a major international research entity in liver failure caused by viral hepatitis and actively participates in international scientific collaborations.The research hotspots on liver failure caused by viral hepatitis have gradually shifted from preventing viral hepatitis infections and expanding treatment options to the treatment of chronic infection patients and prognostic prediction.

The pathological mechanisms and related physiological indicators of acute mountain sickness(AMS)were introduced,and the present situation of wearable devices applied in AMS auxiliary detection and diagnosis were reviewed.The challenges and future development directions of wearable devices applied in auxiliary detection and diagnosis were pointed out.[Chinese Medical Equipment Journal,2025,46(9):100-107]

The pathological mechanisms and related physiological indicators of acute mountain sickness(AMS)were introduced,and the present situation of wearable devices applied in AMS auxiliary detection and diagnosis were reviewed.The challenges and future development directions of wearable devices applied in auxiliary detection and diagnosis were pointed out.[Chinese Medical Equipment Journal,2025,46(9):100-107]