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

Widespread utilization of glyphosate has led to environmental residues,posing potential threats to ecological systems and human health.Traditional methods for detection of glyphosate are limited by specialized equipment and operational techniques,resulting in inefficient responses.Therefore,it is urgent to develop a convenient,sensitive and accurate detection method for detection of glyphosate.Herein,a new naphthalenesulfonamide-based"Turn-on"fluorescent probe was synthesized using 2-chloroaniline and dansyl chloride as raw materials through a one-step process,which showed a good linear relationship between the glyphosate concentration in concentration range of 0.003-70 μmol/L and the fluorescence intensity(R2=0.995),with a detection limit of 2.73 nmol/L(S/N=3).Analytical techniques such as nuclear magnetic resonance(NMR)spectroscopy and high-resolution mass spectrometry(HRMS)were used to investigate the interaction mechanism between the fluorescent probe and glyphosate.The results indicated that a nucleophilic substitution reaction occurred between the probe and the secondary amine(—NH—)of glyphosate,inducing a photoinduced electron transfer(PET)effect which enhanced the fluorescence intensity by 11.2 times.The probe showed good anti-interference ability towards coexisting metal ions,anions and pesticides in water.When applied to determination of glyphosate in the samples such as tap water,river water(Xiangxi River Reservoir),soil,soybeans,and corn,the spiking recoveries ranged from 94.7％to 109.9％,demonstrating the high accuracy and broad applicability of this detection method.A portable test strip based on this fluorescent probe was developed for rapid semi-quantitative analysis of glyphosate.The developed method was rapid,sensitive,and portable,providing theoretical and technical support for on-site measurement of environmental contaminants.

Objective To investigate plasma protein expression changes in heatstroke patients using proteomics technology and to identify reliable prognostic biomarkers.Methods A retrospective analysis was conducted on 20 heatstroke patients hospitalized at the 908th Hospital of the Chinese PLA Joint Logistics Support Force from July 2022 to February 2024.Patients were divided into survival(n=16)and death groups(n=4)based on 28-day outcomes.Fasting venous blood samples were collected from both groups for proteomic analysis.Liquid chromatography-mass spectrometry(LC-MS/MS)was used to identify and screen differentially expressed proteins.Kyoto Encyclopedia of Genes and Genomes(KEGG)and Gene Ontology(GO)enrichment analyses were performed.LASSO regression was applied to screen key prognostic biomarkers,and receiver operating characteristic(ROC)curve analysis was used to evaluate their predictive value.Results A total of 27 upregulated and 90 downregulated proteins were identified between the two groups.These proteins were primarily involved in biological processes such as immune responses,complement activation,and metabolic processes.LASSO regression analysis indicated that actin-related protein 2(ARP2),cysteine-rich scavenger receptor type 1 M130(CD163),and catalase(CAT)could serve as effective biomarkers for evaluating heatstroke prognosis.ROC curve analysis demonstrated that ARP2 had higher diagnostic efficacy(AUC=0.98,sensitivity=0.80,specificity=1.00)compared to CD163(AUC=0.94,sensitivity=0.76,specificity=1.00),CAT(AUC=0.96,sensitivity=0.67,specificity=1.00),and acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)score(AUC=0.79,sensitivity=0.44,specificity=1.00).Conclusion Elevated plasma ARP2 level has good clinical value for predicting poor prognosis in heatstroke patients.

[Objective]To explore the diagnostic value of conventional ultrasound combined with shear wave elastography (SWE) for sarcopenia in patients with chronic kidney disease (CKD).[Methods]The study included 94 CKD patients (34 with sarcopenia and 60 without). All patient underwent the Simplified Assessment Rating Questionnaire (SARC-CalF),Mini Nutritional Assessment (MNA),Short Physical Performance Battery (SPPB),grip strength test,bioelectrical impedance analysis (BIA),conventional muscle ultrasound and SWE of the thighs. We then compared the differences in indicators between the sarcopenia group and non-sarcopenia group,used Spearman correlation analysis to assess the relationship between the two examinations (conventional ultrasound and SWE) and other clinical indicators,identified the diagnostic markers for sarcopenia,created receiver operating characteristic (ROC) curves,calculated the area under the curve (AUC) and determined the diagnostic performance of conventional ultrasound,SWE and their combination. Binary logistic regression was used to analyze the influencing factors of sarcopenia in CKD patients and a combined diagnosis model was established.[Results]The sarcopenia group showed lower upper arm circumference,calf circumference,6-meter walking speed and handgrip strength than non-sarcopenia group,and the differences were statistically significant (P<0.05). The sarcopenia group exhibited lower SARC-CalF and SPBB scores,as well as more compromised nutritional status. Statistically significant differences were observed in the ultrasound parameters between the two groups,including thickness of the subcutaneous fat and rectus femoris,combined thickness of the rectus femoris and vastus intermedius,rectus femoris cross-sectional area,elastic modulus of the rectus femoris and vastus medialis (all P<0.05). The muscle mass index had a moderate positive correlation with muscle thickness and cross-sectional area of the rectus femoris (0.3

【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.