With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

OBJECTIVE: To explore and quantify the association of hot night exposure during the sperm development period (0-90 lag days) with semen quality. METHODS: A total of 6,640 male sperm donors from 6 human sperm banks in China during 2014-2020 were recruited in this multicenter study. Two indices (i.e., hot night excess [HNE] and hot night duration [HND]) were used to estimate the heat intensity and duration during nighttime. Linear mixed models were used to examine the association between hot nights and semen quality parameters. RESULTS: The exposure-response relationship revealed that HNE and HND during 0-90 days before semen collection had a significantly inverse association with sperm motility. Specifically, a 1 °C increase in HNE was associated with decreased sperm progressive motility of 0.0090 (95% confidence interval [ CI]: -0.0147, -0.0033) and decreased total motility of 0.0094 (95% CI: -0.0160, -0.0029). HND was significantly associated with reduced sperm progressive motility and total motility of 0.0021 (95% CI: -0.0040, -0.0003) and 0.0023 (95% CI: -0.0043, -0.0002), respectively. Consistent results were observed at different temperature thresholds on hot nights. CONCLUSION Our findings highlight the need to mitigate nocturnal heat exposure during spermatogenesis to maintain optimal semen quality.
Humans ; Male ; Semen Analysis ; Adult ; Sperm Motility ; Hot Temperature/adverse effects* ; China ; Middle Aged ; Spermatozoa/physiology* ; Young Adult

Humans ; Adolescent ; Acne Vulgaris/therapy* ; Skin Care ; Surveys and Questionnaires ; Cognition ; China ; Skin

Objective:To report the results of national surveillance on the distribution and antimicrobial resistance profile of clinical Gram-negative bacteria isolates from bloodstream infections in China in 2022.Methods:The clinical isolates of Gram-negative bacteria from blood cultures in member hospitals of national bloodstream infection Bacterial Resistant Investigation Collaborative System（BRICS）were collected during January 2022 to December 2022. 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 software were used to analyze the data.Results:During the study period，9 035 strains of Gram-negative bacteria were collected from 51 hospitals，of which 7 895（87.4%）were Enterobacteriaceae and 1 140（12.6%）were non-fermenting bacteria. The top 5 bacterial species were Escherichia coli（ n=4 510，49.9%）， Klebsiella pneumoniae（ n=2 340，25.9%）， Pseudomonas aeruginosa（ n=534，5.9%）， Acinetobacter baumannii complex（ n=405，4.5%）and Enterobacter cloacae（ n=327，3.6%）. The ESBLs-producing rates in Escherichia coli， Klebsiella pneumoniae and Proteus spp. were 47.1%（2 095/4 452），21.0%（427/2 033）and 41.1%（58/141），respectively. The prevalence of carbapenem-resistant Escherichia coli（CREC）and carbapenem-resistant Klebsiella pneumoniae（CRKP）were 1.3%（58/4 510）and 13.1%（307/2 340）；62.1%（36/58）and 9.8%（30/307）of CREC and CRKP were resistant to ceftazidime/avibactam combination，respectively. The prevalence of carbapenem-resistant Acinetobacter baumannii（CRAB）complex was 59.5%（241/405），while less than 5% of Acinetobacter baumannii complex was resistant to tigecycline and polymyxin B. The prevalence of carbapenem-resistant Pseudomonas aeruginosa（CRPA）was 18.4%（98/534）. There were differences in the composition ratio of Gram-negative bacteria in bloodstream infections and the prevalence of main Gram-negative bacteria resistance among different regions，with statistically significant differences in the prevalence of CRKP and CRPA（ χ2=20.489 and 20.252， P<0.001）. The prevalence of CREC，CRKP，CRPA，CRAB，ESBLs-producing Escherichia coli and Klebsiella pneumoniae were higher in provinicial hospitals than those in municipal hospitals（ χ2=11.953，81.183，10.404，5.915，12.415 and 6.459， P<0.01 or <0.05），while the prevalence of CRPA was higher in economically developed regions（per capita GDP ≥ 92 059 Yuan）than that in economically less-developed regions（per capita GDP <92 059 Yuan）（ χ2=6.240， P=0.012）. Conclusions:The proportion of Gram-negative bacteria in bloodstream infections shows an increasing trend，and Escherichia coli is ranked in the top，while the trend of CRKP decreases continuously with time. Decreasing trends are noted in ESBLs-producing Escherichia coli and Klebsiella pneumoniae. Low prevalence of carbapenem resistance in Escherichia coli and high prevalence in CRAB complex have been observed. The composition ratio and antibacterial spectrum of bloodstream infections in different regions of China are slightly different，and the proportion of main drug resistant bacteria in provincial hospitals is higher than those in municipal hospitals.

【Objective】 To objectively evaluate the quality control level of blood testing process in blood banks through quantitative monitoring and trend analysis, and to promote the homogenization level and standardized management of blood testing laboratories in blood banks. 【Methods】 A quality monitoring indicator system covering the whole process of blood collection and supply, including blood donation service, blood component preparation, blood testing, blood supply and quality control was established. The questionnaire Quality Monitoring Indicators for Blood Collection and Supply Process with clear definition of indicators and calculation formulas was distributed to 17 blood banks in Shandong province. Quality monitoring indicators of each blood bank from January to December 2022 were collected, and 31 indicators in terms of blood testing were analyzed using SPSS25.0 software. 【Results】 The proportion of unqualified serological tests in 17 blood bank laboratories was 55.84% for ALT, 13.63% for HBsAg, 5.08% for anti HCV, 5.62% for anti HIV, 18.18% for anti TP, and 1.65% for other factors (mainly sample quality). The detection unqualified rate and median were (1.23±0.57)% and 1.11%, respectively. The ALT unqualified rate and median were (0.74±0.53)% and 0.60%, respectively. The detection unqualified rate was positively correlated with ALT unqualified rate (r=0.974, P<0.05). The unqualified rate of HBsAg, anti HCV, anti HIV and anti TP was (0.15±0.09)%, (0.05±0.04)%, (0.06±0.03)% and (0.20±0.05)% respectively. The average unqualified rate, average hemolysis rate, average insufficient volume rate and the abnormal hematocrit rate of samples in 17 blood bank laboratories was 0.21‰, 0.08‰, 0.01‰ and 0.02‰ respectively. There were differences in the retest concordance rates of four HBsAg, anti HCV and anti HIV reagents, and three anti TP reagents among 17 blood bank laboratories (P<0.05). The usage rate of ELISA reagents was (114.56±3.30)%, the outage rate of ELISA was (10.23±7.05) ‰, and the out of range rate of ELISA was (0.90±1.17) ‰. There was no correlation between the out of range rate, outrage rate and usage rate (all P>0.05), while the outrage rate was positively correlated with the usage rate (r=0.592, P<0.05). A total of 443 HBV DNA positive samples were detected in all blood banks, with an unqualified rate of 3.78/10 000; 15 HCV RNA positive samples were detected, with an unqualified rate of 0.13/10 000; 5 HIV RNA positive samples were detected, with an unqualified rate of 0.04/10 000. The unqualified rate of NAT was (0.72±0.04)‰, the single NAT reaction rate [(0.39±0.02)‰] was positively correlated with the single HBV DNA reaction rate [ (0.36±0.02) ‰] (r=0.886, P<0.05). There was a difference in the discriminated reactive rate by individual NAT among three blood bank laboratories (C, F, H) (P<0.05). The median resolution rate of 17 blood station laboratories by minipool test was 36.36%, the median rate of invalid batch of NAT was 0.67%, and the median rate of invalid result of NAT was 0.07‰. The consistency rate of ELISA dual reagent detection results was (99.63±0.24)%, and the median length of equipment failure was 14 days. The error rate of blood type testing in blood collection department was 0.14‰. 【Conclusion】 The quality monitoring indicator system for blood testing process in Shandong can monitor potential risks before, during and after the experiment, and has good applicability, feasibility, and effectiveness, and can facilitate the continuous improvement of laboratory quality control level. The application of blood testing quality monitoring indicators will promote the homogenization and standardization of blood quality management in Shandong, and lay the foundation for future comprehensive evaluations of blood banks.

【Objective】 To establish an effective quality indicator monitoring system, scientifically and objectively evaluate the quality management level of blood banks, and achieve continuous improvement of quality management in blood bank. 【Methods】 A quality monitoring indicator system that covers the whole process of blood collection and supply was established, the questionnaire of Quality Monitoring Indicators for Blood Collection and Supply Process with clear definition of indicators and calculation formulas was distributed to 17 blood banks in Shandong. Statistical analysis of 21 quality monitoring indicators in terms of blood donation service (10 indicators), blood component preparation (7 indicators ), and blood supply (4 indicators) from each blood bank from January to December 2022 were conducted using SPSS25.0 software The differences in quality monitoring indicators of blood banks of different scales were analyzed. 【Results】 The average values of quality monitoring indicators for blood donation service process of 17 blood banks were as follows: 44.66% (2 233/5 000) of regular donors proportion, 0.22% (11/50) of adverse reactions incidence, 0.46% (23/5 000) of non-standard whole blood collection rate, 0.052% (13/25 000) of missed HBsAg screening rate, 99.42% (4 971/5 000) of first, puncture successful rate, 86.49% (173/200) of double platelet collection rate, 66.50% (133/200) of 400 mL whole blood collection rate, 99.25% (397/400) of donor satisfaction rate, 82.68% (2 067/2 500) of use rate of whole blood collection bags with bypass system with sample tube, and 1 case of occupational exposure in blood collection.There was a strong positive correlation between the proportion of regular blood donors and the collection rate of 400 mL whole blood (P<0.05). The platelet collection rate, incidence of adverse reactions to blood donation, and non-standard whole blood collection rate in large blood banks were significantly lower than those in medium and small blood banks (P<0.05). The average quality monitoring indicators for blood component preparation process of 17 blood banks were as follows: the leakage rate of blood component preparation bags was 0.03% (3/10 000), the discarding rate of lipemic blood was 3.05% (61/2 000), the discarding rate of hemolysis blood was 0.13%(13/10 000). 0.06 case had labeling errors, 8 bags had blood catheter leaks, 2.76 bags had blood puncture/connection leaks, and 0.59 cases had non-conforming consumables. The discarding rate of hemolysis blood of large blood banks was significantly lower than that of medium and small blood banks (P<0.05), and the discarding rate of lipemic blood of large and medium blood banks was significantly lower than that of small blood banks (P<0.05). The average values of quality monitoring indicators for blood supply process of 17 blood banks were as follows: the discarding rate of expired blood was 0.023% (23/100 000), the leakage rate during storage and distribution was of 0.009%(9/100 000), the discarding rate of returned blood was 0.106% (53/50 000), the service satisfaction of hospitals was 99.16% (2 479/2 500). The leakage rate of blood components during storage and distribution was statistically different with that of blood component preparation bags between different blood banks (P<0.05). There were statistically significant differences in the proportion of regular blood donors, incidence of adverse reactions, non-standard whole blood collection rate, 400 mL whole blood collection rate, double platelet collection rate, the blood bag leakage rate during preparation process, the blood components leakage rate during storage and distribution as well as the discarding rate of lipemic blood, hemolysis blood, expired blood and returned blood among large, medium and small blood banks (all P<0.05). 【Conclusion】 The establishment of a quality monitoring indicator system for blood donation services, blood component preparation and blood supply processes in Shandong has good applicability, feasibility and effectiveness. It can objectively evaluate the quality management level, facilitate the continuous improvement of the quality management system, promote the homogenization of blood management in the province and lay the foundation for future comprehensive evaluation of blood banks.