1.Characteristics of Gut Microbiota Changes and Their Relationship with Infectious Complications During Induction Chemotherapy in AML Patients.
Quan-Lei ZHANG ; Li-Li DONG ; Lin-Lin ZHANG ; Yu-Juan WU ; Meng LI ; Jian BO ; Li-Li WANG ; Yu JING ; Li-Ping DOU ; Dai-Hong LIU ; Zhen-Yang GU ; Chun-Ji GAO
Journal of Experimental Hematology 2025;33(3):738-744
OBJECTIVE:
To investigate the characteristics of gut microbiota changes in patients with acute myeloid leukemia (AML) undergoing induction chemotherapy and to explore the relationship between infectious complications and gut microbiota.
METHODS:
Fecal samples were collected from 37 newly diagnosed AML patients at four time points: before induction chemotherapy, during chemotherapy, during the neutropenic phase, and during the recovery phase. Metagenomic sequencing was used to analyze the dynamic changes in gut microbiota. Correlation analyses were conducted to assess the relationship between changes in gut microbiota and the occurrence of infectious complications.
RESULTS:
During chemotherapy, the gut microbiota α-diversity (Shannon index) of AML patients exhibited significant fluctuations. Specifically, the diversity decreased significantly during induction chemotherapy, further declined during the neutropenic phase (P < 0.05, compared to baseline), and gradually recovered during the recovery phase, though not fully returning to baseline levels.The abundances of beneficial bacteria, such as Firmicutes and Bacteroidetes, gradually decreased during chemotherapy, whereas the abundances of opportunistic pathogens, including Enterococcus, Klebsiella, and Escherichia coli, progressively increased.Analysis of the dynamic changes in gut microbiota of seven patients with bloodstream infections revealed that the bloodstream infection pathogens could be detected in the gut microbiota of the corresponding patients, with their abundance gradually increasing during the course of infection. This finding suggests that bloodstream infections may be associated with opportunistic pathogens originating from the gut microbiota.Compared to non-infected patients, the baseline samples of infected patients showed a significantly lower relative abundance of Bacteroidetes (P < 0.05). Regression analysis indicated that Bacteroidetes abundance is an independent predictive factor for infectious complications (P < 0.05, OR =13.143).
CONCLUSION
During induction chemotherapy in AML patients, gut microbiota α-diversity fluctuates significantly, and the abundance of opportunistic pathogens increase, which may be associated with bloodstream infections. Patients with lower baseline Bacteroidetes abundance are more prone to infections, and its abundance can serve as an independent predictor of infectious complications.
Humans
;
Gastrointestinal Microbiome
;
Leukemia, Myeloid, Acute/microbiology*
;
Induction Chemotherapy
;
Feces/microbiology*
;
Male
;
Female
;
Middle Aged
2.Research Progress of Vagal Nerve Regulation Mechanism in Acupuncture Treatment of Atrial Fibrillation.
Lu-Lu CAO ; Hui-Rong LIU ; Ya-Jie JI ; Yin-Tao ZHANG ; Bing-Quan WANG ; Xiao-Hong XUE ; Pei WANG ; Zhi-Hui LUO ; Huan-Gan WU
Chinese journal of integrative medicine 2025;31(3):281-288
Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. It has a high prevalence and poor prognosis. The application of antiarrhythmic drugs and even surgery cannot completely treat the disease, and there are many sequelae. AF can be classified into the category of "palpitation" in Chinese medicine according to its symptoms. Acupuncture has a significant effect on AF. The authors find that an important mechanism of acupuncture in AF treatment is to regulate the cardiac vagus nerve. Therefore, this article intends to review the distribution and function of vagus nerve in the heart, the application and the regulatroy effect for the treatment of AF.
Atrial Fibrillation/physiopathology*
;
Humans
;
Acupuncture Therapy
;
Vagus Nerve/physiology*
;
Animals
3.Risk factors for cutout failure in geriatric intertrochanteric fracture patients after cephalomedullary nail fixation.
You-Liang HAO ; Fang ZHOU ; Hong-Quan JI ; Yun TIAN ; Zhi-Shan ZHANG ; Yan GUO ; Yang LYU ; Zhong-Wei YANG ; Guo-Jin HOU
China Journal of Orthopaedics and Traumatology 2025;38(2):141-147
OBJECTIVE:
To determine risk factors for cutout failure in geriatric intertrochanteric fracture patients after cephalomedullary nail fixation.
METHODS:
A retrospective review of 518 elderly patients who underwent cephalomedullary nail fixation for intertrochanteric fractures between January 2008 and August 2018 was conducted, including 167 males and 351 females, age from 65 to 97 years old. All patients were followed up for at least one year after surgery and divided into a healed group and a cutout group based on whether the hip screw cutout occurred. Among all patients, 10 cases experienced hip screw cutout. The general information, surgical data, and radiological data of the two groups were compared, and risk factors influencing hip screw cutout were analyzed. Propensity score matching was then performed on the cutout group based on gender, age, body mass index(BMI), and American Society of Anesthesiologists(ASA), and 40 patients from the healed group were matched at a ratio of 1∶4. Key risk factors affecting hip screw cutout were further analyzed. Multivariable logistic regression analysis was conducted to evaluate associations between variables and cutout failure.
RESULTS:
There were no statistically significant differences between the healed group and the cutout group in terms of age, gender, BMI, ASA, and AO classification. However, statistically significant differences were observed between the two groups in terms of reduction quality(P=0.003) and tip-apex distance(TAD), P<0.001. Multivariate analysis identified poor reduction quality OR=23.138, 95%CI(2.163, 247.551), P=0.009 and TAD≥25 mm OR=30.538, 95%CI(2.935, 317.770), P=0.004 as independent risk factors for cutout failure.
CONCLUSION
The present study identified poor reduction quality and TAD≥25 mm as factors for cutout failure in geriatric intertrochanteric fractures treated with cephalomedullary nails. Further studies are needed to calculate the optimal TAD for cephalomedullary nails.
Humans
;
Male
;
Female
;
Hip Fractures/surgery*
;
Aged, 80 and over
;
Aged
;
Risk Factors
;
Retrospective Studies
;
Fracture Fixation, Intramedullary/adverse effects*
;
Bone Nails
;
Bone Screws
4.National bloodstream infection bacterial resistance surveillance report 2023: Gram-positive bacteria
Chaoqun YING ; Jinru JI ; Zhiying LIU ; Qing YANG ; Haishen KONG ; Jiangqin SONG ; Hui DING ; Yanyan LI ; Yuanyuan DAI ; Haifeng MAO ; Pengpeng TIAN ; Lu WANG ; Yongyun LIU ; Yizheng ZHOU ; Jiliang WANG ; Yan JIN ; Donghong HUANG ; Hongyun XU ; Peng ZHANG ; Xinhua QIANG ; Hong HE ; Lin ZHENG ; Junmin CAO ; Zhou LIU ; Ying HUANG ; Yan GENG ; Haiquan KANG ; Dan LIU ; Guolin LIAO ; Lixia ZHANG ; Fenghong CHEN ; Yanhong LI ; Baohua ZHANG ; Haixin DONG ; Xiaoyan LI ; Donghua LIU ; Qiuying ZHANG ; Xuefei HU ; Liang GUO ; Sijin MAN ; Dijing SONG ; Rong XU ; Youdong YIN ; Kunpeng LIANG ; Aiyun LI ; Zhuo LI ; Hongxia HU ; Guoping LU ; Jinhua LIANG ; Qiang LIU ; Yinqiao DONG ; Jilu SHEN ; Shuyan HU ; Liang LUAN ; Jian LI ; Ling MENG ; Dengyan QIAO ; Xiusan XIA ; Bo QUAN ; Dahong WANG ; Chunhua HAN ; Xiaoping YAN ; Fei LI ; Shifu WANG ; Ping SHEN ; Yunbo CHEN ; Yonghong XIAO
Chinese Journal of Clinical Infectious Diseases 2025;18(2):118-132
Objective:To report the nationwide surveillance results of pathogenic profiles and antimicrobial resistance patterns of Gram-positive bloodstream infections in China in 2023.Methods:The clinical isolates of Gram-posttive bacteria from blood cultures were collected in member hospitals of National Bloodstream Infection Bacterial Resistant Investigation Collaborative System(BRICS)during January to December 2023. Antimicrobial susceptibility testing was performed using the dilution method recommended by the Clinical and Laboratory Standards Institute(CLSI). Statistical analyses were conducted using WHONET 5.6 and SPSS 25.0 software.Results:A total of 4 385 Gram-positive bacterial isolates were obtained from 60 participating center. The top five pathogens were Staphylococcus aureus( n=1 544,35.2%),coagulase-negative Staphylococci( n=1 441,32.9%), Enterococcus faecium( n=574,13.1%), Enterococcus faecalis( n=385,8.8%),and α-hemolytic Streptococci( n=187,4.3%). The prevalence of methicillin-resistant Staphylococcus aureus(MRSA)and methicillin-resistant coagulase-negative Staphylococci(MRCNS)was 26.2%(405/1 544)and 69.8%(1 006/1 441),respectively. Notably,all Staphylococci remained susceptible to glycopeptide or daptomycin. Staphylococcus aureus demonstrated excellent susceptibility(>97.0%)to cephalobiol,rifampicin,trimethoprim-sulfamethoxazole,linezolid,minocycline,tigecycline,and eravacycline. No Enterococcus exhibiting resistance to linezolid were detected. Glycopeptide resistance was uncommon but more frequent in Enterococcus faecium(resistance to vancomycin and teicoplanin:both 1.7%)compared to Enterococcus faecalis(both 0.3%). The detection rates of MRSA and MRCNS exhibited significant regional variations across the country( χ2=17.674 and 148.650,respectively,both P<0.001). No vancomycin-resistant Enterococci were detected in central China. Institutional comparison demonstrated higher prevalence of MRSA( χ2=14.111, P<0.001)and MRCNS( χ2=4.828, P=0.028)in provincial hospitals than that in municipal hospitals. Socioeconomic analysis identified elevated detection rates of both MRSA( χ2=18.986, P<0.001)and MRCNS( χ2=4.477, P=0.034)in less developed regions(per capita GDP
5.National bloodstream infection bacterial resistance surveillance report (2023) : Gram-negative bacteria
Jinru JI ; Zhiying LIU ; Chaoqun YING ; Qing YANG ; Haishen KONG ; Jiangqin SONG ; Hui DING ; Yanyan LI ; Yuanyuan DAI ; Haifeng MAO ; Pengpeng TIAN ; Lu WANG ; Yongyun LIU ; Yizheng ZHOU ; Jiliang WANG ; Yan JIN ; Donghong HUANG ; Hongyun XU ; Peng ZHANG ; Xinhua QIANG ; Hong HE ; Lin ZHENG ; Junmin CAO ; Zhou LIU ; Ying HUANG ; Yan GENG ; Haiquan KANG ; Dan LIU ; Guolin LIAO ; Lixia ZHANG ; Fenghong CHEN ; Yanhong LI ; Baohua ZHANG ; Haixin DONG ; Xiaoyan LI ; Donghua LIU ; Qiuying ZHANG ; Xuefei HU ; Liang GUO ; Sijin MAN ; Dijing SONG ; Rong XU ; Youdong YIN ; Kunpeng LIANG ; Aiyun LI ; Zhuo LI ; Hongxia HU ; Guoping LU ; Jinhua LIANG ; Qiang LIU ; Yinqiao DONG ; Jilu SHEN ; Shuyan HU ; Liang LUAN ; Jian LI ; Ling MENG ; Dengyan QIAO ; Xiusan XIA ; Bo QUAN ; Dahong WANG ; Chunhua HAN ; Xiaoping YAN ; Fei LI ; Shifu WANG ; Ping SHEN ; Yunbo CHEN ; Yonghong XIAO
Chinese Journal of Clinical Infectious Diseases 2025;18(1):47-62
Objective:To report the results of bacterial resistant investigation collaborative system(BRICS)on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2023,and provide reference for clinical tretment of bloodstream infections and prevention and control of bacterial resistance.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of BRICS were collected during January 2023 to December 2023. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute(CLSI). WHONET 5.6 and SPSS 25.0 were used to analyze the data.Results:During the study period,11 492 strains of Gram-negative bacteria were collected from 60 hospitals,of which 10 098(87.9%)were Enterobacterales and 1 394(12.1%)were non-fermentative bacteria. The top 5 bacterial species were Escherichia coli(50.0%), Klebsiella pneumoniae(26.1%), Pseudomonas aeruginosa(5.1%), Acinetobacter baumannii complex(5.0%)and Enterobacter cloacae complex(4.1%). The ESBL-producing rates in Escherichia coli, Klebsiella pneumoniae and Proteus mirablilis were 46.8%(2 685/5 741),18.3%(549/2 999)and 44.0%(77/175),respectively. The prevalence of carbapenem-resistant Escherichia coli(CREC)and carbapenem-resistant Klebsiella pneumoniae(CRKP)were 1.3%(76/5 741)and 15.0%(450/2 999);32.9%(25/76)and 78.0%(351/450)of CREC and CRKP were sensitive to ceftazidime/avibactam combination,respectively. 94.7%(72/76)and 90.2%(406/450)of CREC and CRKP were sensitive to aztreonam/avibactam combination. Furthermore,57.9%(44/76)and 79.1%(356/450)were sensitive to imipenem/relebactam combination. The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)complex was 64.6%(370/573),while more than 80.0% of CRAB complex was sensitive to tigecycline,eravacycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa(CRPA)was 17.0%(99/581). There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of important Gram-negative bacteria resistance among different regions in China,with statistically significant differences in the prevalence of CREC,CRKP,CRPA and CRAB complex( χ2=10.6,28.6,10.8 and 19.3, P<0.05). The prevalence of ESBL-producing Escherichia coli, CREC,CRAB complex and CRKP were higher in provincial hospitals than those in municipal hospitals( χ2=12.5,9.8,12.7 and 57.8,all P<0.01). Conclusions:Gram-negative bacteria are the main pathogens causing bloodstream infections in China,and Escherichia coli is ranked in the top,while the trend of Klebsiella pneumoniae increases continuously with time. CRKP infection shows a slow upward trend,CREC infecton maintains a low prevalence level,and CRAB complex infection continues to exhibit a high prevalence rate. The composition and resistance patterns of pathogens causing bloodstream infections vary to some extent across different regions and levels of hospitals in China.
6.Quality assurance of artificial intelligence models applied to case-specific radiotherapy
Xiaonan LIU ; Guodong JIN ; Wenyu WANG ; Ji ZHU ; Bining YANG ; Siqi YUAN ; Hong QUAN ; Kuo MEN ; Jianrong DAI
Chinese Journal of Radiation Oncology 2025;34(9):949-953
Artificial intelligence (AI) technologies are being widely applied in radiotherapy. However, the integration of AI into clinical workflows of radiotherapy faces a series of challenges, such as poor model interpretability, domain shifts between clinical application and training data, and the inherent model uncertainties. Therefore, case-specific quality assurance (QA) is essential before deploying AI models in clinical practice. This paper reviews and summarizes QA methodologies for the application of AI models in radiotherapy across four key areas: image registration, image generation, region of interest segmentation, and treatment planning.
7.National bloodstream infection bacterial resistance surveillance report 2023: Gram-positive bacteria
Chaoqun YING ; Jinru JI ; Zhiying LIU ; Qing YANG ; Haishen KONG ; Jiangqin SONG ; Hui DING ; Yanyan LI ; Yuanyuan DAI ; Haifeng MAO ; Pengpeng TIAN ; Lu WANG ; Yongyun LIU ; Yizheng ZHOU ; Jiliang WANG ; Yan JIN ; Donghong HUANG ; Hongyun XU ; Peng ZHANG ; Xinhua QIANG ; Hong HE ; Lin ZHENG ; Junmin CAO ; Zhou LIU ; Ying HUANG ; Yan GENG ; Haiquan KANG ; Dan LIU ; Guolin LIAO ; Lixia ZHANG ; Fenghong CHEN ; Yanhong LI ; Baohua ZHANG ; Haixin DONG ; Xiaoyan LI ; Donghua LIU ; Qiuying ZHANG ; Xuefei HU ; Liang GUO ; Sijin MAN ; Dijing SONG ; Rong XU ; Youdong YIN ; Kunpeng LIANG ; Aiyun LI ; Zhuo LI ; Hongxia HU ; Guoping LU ; Jinhua LIANG ; Qiang LIU ; Yinqiao DONG ; Jilu SHEN ; Shuyan HU ; Liang LUAN ; Jian LI ; Ling MENG ; Dengyan QIAO ; Xiusan XIA ; Bo QUAN ; Dahong WANG ; Chunhua HAN ; Xiaoping YAN ; Fei LI ; Shifu WANG ; Ping SHEN ; Yunbo CHEN ; Yonghong XIAO
Chinese Journal of Clinical Infectious Diseases 2025;18(2):118-132
Objective:To report the nationwide surveillance results of pathogenic profiles and antimicrobial resistance patterns of Gram-positive bloodstream infections in China in 2023.Methods:The clinical isolates of Gram-posttive bacteria from blood cultures were collected in member hospitals of National Bloodstream Infection Bacterial Resistant Investigation Collaborative System(BRICS)during January to December 2023. Antimicrobial susceptibility testing was performed using the dilution method recommended by the Clinical and Laboratory Standards Institute(CLSI). Statistical analyses were conducted using WHONET 5.6 and SPSS 25.0 software.Results:A total of 4 385 Gram-positive bacterial isolates were obtained from 60 participating center. The top five pathogens were Staphylococcus aureus( n=1 544,35.2%),coagulase-negative Staphylococci( n=1 441,32.9%), Enterococcus faecium( n=574,13.1%), Enterococcus faecalis( n=385,8.8%),and α-hemolytic Streptococci( n=187,4.3%). The prevalence of methicillin-resistant Staphylococcus aureus(MRSA)and methicillin-resistant coagulase-negative Staphylococci(MRCNS)was 26.2%(405/1 544)and 69.8%(1 006/1 441),respectively. Notably,all Staphylococci remained susceptible to glycopeptide or daptomycin. Staphylococcus aureus demonstrated excellent susceptibility(>97.0%)to cephalobiol,rifampicin,trimethoprim-sulfamethoxazole,linezolid,minocycline,tigecycline,and eravacycline. No Enterococcus exhibiting resistance to linezolid were detected. Glycopeptide resistance was uncommon but more frequent in Enterococcus faecium(resistance to vancomycin and teicoplanin:both 1.7%)compared to Enterococcus faecalis(both 0.3%). The detection rates of MRSA and MRCNS exhibited significant regional variations across the country( χ2=17.674 and 148.650,respectively,both P<0.001). No vancomycin-resistant Enterococci were detected in central China. Institutional comparison demonstrated higher prevalence of MRSA( χ2=14.111, P<0.001)and MRCNS( χ2=4.828, P=0.028)in provincial hospitals than that in municipal hospitals. Socioeconomic analysis identified elevated detection rates of both MRSA( χ2=18.986, P<0.001)and MRCNS( χ2=4.477, P=0.034)in less developed regions(per capita GDP
8.National bloodstream infection bacterial resistance surveillance report (2023) : Gram-negative bacteria
Jinru JI ; Zhiying LIU ; Chaoqun YING ; Qing YANG ; Haishen KONG ; Jiangqin SONG ; Hui DING ; Yanyan LI ; Yuanyuan DAI ; Haifeng MAO ; Pengpeng TIAN ; Lu WANG ; Yongyun LIU ; Yizheng ZHOU ; Jiliang WANG ; Yan JIN ; Donghong HUANG ; Hongyun XU ; Peng ZHANG ; Xinhua QIANG ; Hong HE ; Lin ZHENG ; Junmin CAO ; Zhou LIU ; Ying HUANG ; Yan GENG ; Haiquan KANG ; Dan LIU ; Guolin LIAO ; Lixia ZHANG ; Fenghong CHEN ; Yanhong LI ; Baohua ZHANG ; Haixin DONG ; Xiaoyan LI ; Donghua LIU ; Qiuying ZHANG ; Xuefei HU ; Liang GUO ; Sijin MAN ; Dijing SONG ; Rong XU ; Youdong YIN ; Kunpeng LIANG ; Aiyun LI ; Zhuo LI ; Hongxia HU ; Guoping LU ; Jinhua LIANG ; Qiang LIU ; Yinqiao DONG ; Jilu SHEN ; Shuyan HU ; Liang LUAN ; Jian LI ; Ling MENG ; Dengyan QIAO ; Xiusan XIA ; Bo QUAN ; Dahong WANG ; Chunhua HAN ; Xiaoping YAN ; Fei LI ; Shifu WANG ; Ping SHEN ; Yunbo CHEN ; Yonghong XIAO
Chinese Journal of Clinical Infectious Diseases 2025;18(1):47-62
Objective:To report the results of bacterial resistant investigation collaborative system(BRICS)on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2023,and provide reference for clinical tretment of bloodstream infections and prevention and control of bacterial resistance.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of BRICS were collected during January 2023 to December 2023. Antibiotic susceptibility tests were conducted by agar dilution or broth dilution methods recommended by Clinical and Laboratory Standards Institute(CLSI). WHONET 5.6 and SPSS 25.0 were used to analyze the data.Results:During the study period,11 492 strains of Gram-negative bacteria were collected from 60 hospitals,of which 10 098(87.9%)were Enterobacterales and 1 394(12.1%)were non-fermentative bacteria. The top 5 bacterial species were Escherichia coli(50.0%), Klebsiella pneumoniae(26.1%), Pseudomonas aeruginosa(5.1%), Acinetobacter baumannii complex(5.0%)and Enterobacter cloacae complex(4.1%). The ESBL-producing rates in Escherichia coli, Klebsiella pneumoniae and Proteus mirablilis were 46.8%(2 685/5 741),18.3%(549/2 999)and 44.0%(77/175),respectively. The prevalence of carbapenem-resistant Escherichia coli(CREC)and carbapenem-resistant Klebsiella pneumoniae(CRKP)were 1.3%(76/5 741)and 15.0%(450/2 999);32.9%(25/76)and 78.0%(351/450)of CREC and CRKP were sensitive to ceftazidime/avibactam combination,respectively. 94.7%(72/76)and 90.2%(406/450)of CREC and CRKP were sensitive to aztreonam/avibactam combination. Furthermore,57.9%(44/76)and 79.1%(356/450)were sensitive to imipenem/relebactam combination. The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)complex was 64.6%(370/573),while more than 80.0% of CRAB complex was sensitive to tigecycline,eravacycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa(CRPA)was 17.0%(99/581). There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of important Gram-negative bacteria resistance among different regions in China,with statistically significant differences in the prevalence of CREC,CRKP,CRPA and CRAB complex( χ2=10.6,28.6,10.8 and 19.3, P<0.05). The prevalence of ESBL-producing Escherichia coli, CREC,CRAB complex and CRKP were higher in provincial hospitals than those in municipal hospitals( χ2=12.5,9.8,12.7 and 57.8,all P<0.01). Conclusions:Gram-negative bacteria are the main pathogens causing bloodstream infections in China,and Escherichia coli is ranked in the top,while the trend of Klebsiella pneumoniae increases continuously with time. CRKP infection shows a slow upward trend,CREC infecton maintains a low prevalence level,and CRAB complex infection continues to exhibit a high prevalence rate. The composition and resistance patterns of pathogens causing bloodstream infections vary to some extent across different regions and levels of hospitals in China.
9.Quality assurance of artificial intelligence models applied to case-specific radiotherapy
Xiaonan LIU ; Guodong JIN ; Wenyu WANG ; Ji ZHU ; Bining YANG ; Siqi YUAN ; Hong QUAN ; Kuo MEN ; Jianrong DAI
Chinese Journal of Radiation Oncology 2025;34(9):949-953
Artificial intelligence (AI) technologies are being widely applied in radiotherapy. However, the integration of AI into clinical workflows of radiotherapy faces a series of challenges, such as poor model interpretability, domain shifts between clinical application and training data, and the inherent model uncertainties. Therefore, case-specific quality assurance (QA) is essential before deploying AI models in clinical practice. This paper reviews and summarizes QA methodologies for the application of AI models in radiotherapy across four key areas: image registration, image generation, region of interest segmentation, and treatment planning.
10.Establishment of SHERLOCK-HBA Detection Method and Its Application in Blood Identification
Qian-Wei YAO ; Hong-Xia HE ; Sheng HU ; Yi-Xia ZHAO ; Yu LUO ; An-Quan JI ; Qi-Fan SUN
Progress in Biochemistry and Biophysics 2024;51(8):1971-1982
ObjectiveRapid and accurate identification of body fluid traces at crime scenes is crucial for case investigation. Leveraging the speed and sensitivity of nucleic acid detection technology based on SHERLOCK, our research focuses on developing a peripheral blood SHERLOCK-HBA detection system to detect mRNA in forensic practice. MethodsShort crRNA fragments targeting the blood-specific mRNA gene HBA were designed and screened, alongside RPA primers. Optimal RPA primers were selected based on specificity and amplification efficiency, leading to the establishment of the RPA system. The most efficient crRNA was chosen based on relative fluorescence units (RFU) generated by the Cas protein reaction, and the Cas protein reaction system was constructed to establish the SHERLOCK-HBA detection method. The RPA and Cas protein reaction systems in the SHERLOCK detection system were then individually optimized. A total of 79 samples of five body fluids were tested to evaluate the method’s ability to identify blood, with further verification through species-specific tests, sensitivity tests, mixed spots detection, aged samples, UV-irradiated samples, and actual casework samples. ResultsThe SHERLOCK reaction system for the peripheral blood-specific marker HBA was successfully established and optimized, enabling detection within 30 min. The method demonstrated a detection limit of 0.001 ng total RNA, better than FOB strip method and comparable to RT-PCR capillary electrophoresis. The system could detect target body fluids in mixed samples and identify blood in samples stored at room temperature for three years and exposed to UV radiation for 32 h. Detection of 11 casework samples showed performance comparable to RT-PCR capillary electrophoresis. ConclusionThis study presents a CRISPR/Cas-based SHERLOCK-HBA detection system capable of accurately, sensitively, and rapidly identifying blood samples. Introducing CRISPR/Cas technology to forensic body fluid identification represents a significant advancement in applying cutting-edge molecular biology techniques to forensic science.The method’s simplicity, shorter detection time, and independence from specialized equipment make it promising for rapid blood sample identification in forensic cases.

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