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.

OBJECTIVES: To study clinical manifestations and gene mutation features of neonates with centronuclear myopathy. METHODS: A retrospective analysis was conducted on the medical data of 5 neonates with centronuclear myopathy diagnosed in the Neonatal Intensive Care Unit of Children's Hospital, Zhejiang University School of Medicine from January 2020 to August 2024. The data included gender, gestational age, birth weight, Apgar score, clinical manifestations, creatine kinase level, electromyography, genetic testing results and the outcomes of the infants. RESULTS: All 5 male neonates had a history of postpartum asphyxia and resuscitation. They all presented with hypotonia, myasthenia, and respiratory failure; two neonates also had swallowing dysfunction. Of the five neonates, three had normal creatine kinase levels, while two had slightly elevated levels. Electromyography was performed for three neonates, among whom two had myogenic damage. MTM1 gene mutations were identified by genetic testing in all five neonates, including two nonsense mutations and three missense mutations, among which one variant had not been previously reported. Four mutations were inherited from the mother, and the other one was a de novo mutation. The five neonates showed no clinical improvement following treatment, failed weaning from mechanical ventilation, and ultimately died after withdrawal of life-sustaining therapy. CONCLUSIONS Centronuclear myopathy caused by MTM1 gene mutation often has a severe phenotype and a poor prognosis, and it should be considered for neonates with hypotonia and myasthenia after birth. Genetic testing should be performed as soon as possible.
Humans ; Myopathies, Structural, Congenital/genetics* ; Male ; Infant, Newborn ; Retrospective Studies ; Mutation ; Female ; Protein Tyrosine Phosphatases, Non-Receptor/genetics*

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

OBJECTIVE: This study aimed to identify high-risk areas for type 2 diabetes mellitus (T2DM) mortality to provide relevant evidence for interventions in emerging economies. METHODS: Empirical Bayesian Kriging and a discrete Poisson space-time scan statistic were applied to identify the spatiotemporal clusters of T2DM mortality. The relationships between economic factors, air pollutants, and the mortality risk of T2DM were assessed using regression analysis and the Poisson Log-linear Model. RESULTS: A coastal district in East Guangdong, China, had the highest risk (Relative Risk [RR] = 4.58, P < 0.01), followed by the 10 coastal districts/counties in West Guangdong, China (RR = 2.88, P < 0.01). The coastal county in the Pearl River Delta, China (RR = 2.24, P < 0.01), had the third-highest risk. The remaining risk areas were two coastal counties in East Guangdong, 16 districts/counties in the Pearl River Delta, and two counties in North Guangdong, China. Mortality due to T2DM was associated with gross domestic product per capita (GDP per capita). In pilot assessments, T2DM mortality was significantly associated with carbon monoxide. CONCLUSION High mortality from T2DM occurred in the coastal areas of East and West Guangdong, especially where the economy was progressing towards the upper middle-income level.
Diabetes Mellitus, Type 2/epidemiology* ; China/epidemiology* ; Humans ; Risk Factors ; Spatio-Temporal Analysis ; Air Pollutants/analysis* ; Socioeconomic Factors ; Bayes Theorem ; Female ; Male ; Middle Aged

Objective To investigate the effects of circDNMT1 on the proliferation,apoptosis and epithelial-mesenchymal transition(EMT)of triple-negative breast cancer(TNBC)cells by regulating miR-377-3p/PUM1 axis.Methods The TNBC tissues and adjacent normal tissues of 24 patients with TNBC treated in Danzhou People's Hospital from 2018 to 2021 were collected.qRT-PCR and Western blot were used to detect the expression of circDNMT1,miR-377-3p,and PUM1 protein in tissue and mouse normal breast epithelial cell line HC11 and TNBC cell lines 4T1,Eph41424,and JC.4T1 cells were divided into the 4T1 group(untransfected),the si-NC group(transfected with si-NC),the si-DNMT1 group(transfected with si-DNMT1),the si-DNMT1+anti-NC group(simultaneously transfected with si-DNMT1 and anti-NC),and the si-DNMT1+anti-miR-377-3p group(simultaneously transfected with si-DNMT1 and anti-miR-377-3p).qRT-PCR was used to detect the expression of circDNMT1 and miR-377-3p of 4T1 cells in each group;CCK-8 was used to detect the proliferation of 4T1 cells in each group;flow cytometry was used to detect the apoptosis of 4T1 cells in each group;Western blot was used to detect the expression of PUM1,EMT-related proteins and apoptosis-related proteins of 4T1 cells in each group;TargetScan website was used to predict the binding sites of miR-377-3p with circDNMT1 and PUM1;dual luciferase report was used to verify the targeting relationships of miR-377-3p with circDNMT1 and PUM1.After inoculation with 4T1 cells,BALB/c mice were randomly divided into the blank control group(injected with equal amount of normal saline),the negative control group(injected with si-NC via tail vein),the DNMT1 silencing group(injected with si-DNMT1 via tail vein),the combined control group(injected with si-DNMT1 and anti-NC via tail vein),and the combined silencing group(injected with si-DNMT1 and anti-miR-377-3p via tail vein),the tumor massess of mice were recorded and the morphological changes of tumors were observed by hematoxylin-eosin staining.Results circDNMT1 and PUM1 were up-regulated in TNBC tissues and cells,and miR-377-3p was down-regulated.The expression difference was most obvious in 4T1 cells,so 4T1 cells were selected for subsequent experiments.Compared with the 4T1 group and the si-NC group,the expression of miR-377-3p,the apoptosis rate of 4T1 cells,the expression levels of Bax,cleaved caspase-3 and E-cadherin protein of 4T1 cells in the si-DNMT1 group were significantly increased(P<0.05),the circ-DNMT1 level,the expression level of PUM1 protein,OD values at 24 hours and 48 hours,the expression level of Bcl-2,N-cadherin,Vimentin protein were significantly decreased(P<0.05).Compared with the si-DNMT1 group and the si-DNMT1+anti-NC group,the expression of miR-377-3p,the apoptosis rate of 4T1 cells,the expression levels of Bax,cleaved caspase-3 and E-cadherin proteins of 4T1 cells in the si-DNMT1+anti-miR-377-3p group were significantly decreased(P<0.05),the expression level of PUM1 protein,OD values at 24 hours and 48 hours,the expression levels of Bcl-2,N-cadherin,Vimentin proteins were significantly increased(P<0.05).Compared with the miR-NC+WT-circDNMT1 group,the cell luciferase activity in the miR-377-3p mimic+WT-circDNMT1 group was significantly decreased(P<0.05);compared with the miR-NC+WT-PUM1 group,the cell luciferase activity in the miR-377-3p mimic+WT-PUM1 group was significantly decreased(P<0.05).The tumor cells in the blank control group and the negative control group were densely arranged with clear boundary;the tumor cells in the DNMT1 silencing group and the combined control group were loosely arranged,the nuclei were pyknotic,and the cell fragments were increased;the tumor cells in the combined silencing group were densely arranged and the boundaries tended to be clear.Compared with the blank control group and the negative control group,the tumor mass of mice in the DNMT1 silencing group was significantly decreased(P<0.05).Compared with the DNMT1 silencing group and the combined control group,the tumor mass of mice in the combined silencing group was significantly increased(P<0.05).Conclusion Silencing circDNMT1 may inhibit the expression of PUM1 by up-regulating miR-377-3p,thereby inhibiting the proliferation and EMT of TNBC cells,and promoting cell apoptosis.

Bacterial biofilms gave rise to persistent infections and multi-organ failure, thereby posing a serious threat to human health. Biofilms were formed by cross-linking of hydrophobic extracellular polymeric substances (EPS), such as proteins, polysaccharides, and eDNA, which were synthesized by bacteria themselves after adhesion and colonization on biological surfaces. They had the characteristics of dense structure, high adhesiveness and low drug permeability, and had been found in many human organs or tissues, such as the brain, heart, liver, spleen, lungs, kidneys, gastrointestinal tract, and skeleton. By releasing pro-inflammatory bacterial metabolites including endotoxins, exotoxins and interleukin, biofilms stimulated the body’s immune system to secrete inflammatory factors. These factors triggered local inflammation and chronic infections. Those were the key reason for the failure of traditional clinical drug therapy for infectious diseases.In order to cope with the increasingly severe drug-resistant infections, it was urgent to develop new therapeutic strategies for bacterial-biofilm eradication and anti-bacterial infections. Based on the nanoscale structure and biocompatible activity, nanobiomaterials had the advantages of specific targeting, intelligent delivery, high drug loading and low toxicity, which could realize efficient intervention and precise treatment of drug-resistant bacterial biofilms. This paper highlighted multiple strategies of biofilms eradication based on nanobiomaterials. For example, nanobiomaterials combined with EPS degrading enzymes could be used for targeted hydrolysis of bacterial biofilms, and effectively increased the drug enrichment within biofilms. By loading quorum sensing inhibitors, nanotechnology was also an effective strategy for eradicating bacterial biofilms and recovering the infectious symptoms. Nanobiomaterials could intervene the bacterial metabolism and break the bacterial survival homeostasis by blocking the uptake of nutrients. Moreover, energy-driven micro-nano robotics had shown excellent performance in active delivery and biofilm eradication. Micro-nano robots could penetrate physiological barriers by exogenous or endogenous driving modes such as by biological or chemical methods, ultrasound, and magnetic field, and deliver drugs to the infection sites accurately. Achieving this using conventional drugs was difficult. Overall, the paper described the biological properties and drug-resistant molecular mechanisms of bacterial biofilms, and highlighted therapeutic strategies from different perspectives by nanobiomaterials, such as dispersing bacterial mature biofilms, blocking quorum sensing, inhibiting bacterial metabolism, and energy driving penetration. In addition, we presented the key challenges still faced by nanobiomaterials in combating bacterial biofilm infections. Firstly, the dense structure of EPS caused biofilms spatial heterogeneity and metabolic heterogeneity, which created exacting requirements for the design, construction and preparation process of nanobiomaterials. Secondly, biofilm disruption carried the risk of spread and infection the pathogenic bacteria, which might lead to other infections. Finally, we emphasized the role of nanobiomaterials in the development trends and translational prospects in biofilm treatment.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.

Objective To investigate the effect of respiratory training based on core stability training on feedforward control in pa-tients with chronic nonspecific low back pain(CNLBP). Methods A total of 60 patients with CNLBP in Jiaxing Second Hospital from January,2022 to March,2023 were ran-domly divided into control group(n=30)and experimental group(n=30).Both groups received health educa-tion,physical factor therapy and core stability training,while the experimental group received respiratory training in addition,for four weeks.Visual Analogue Scale(VAS)score,Japanese Orthopaedic Association low back pain(JOA)score and Oswestry Dysfunction Index(ODI)were compared between two groups before and after treat-ment,while surface electromyography was used to detect the root mean square(RMS)and integrated electromy-ography(iEMG)of transversus abdominis,multifidus and triceps(movement muscles),and the activation se-quence and relative activation time of transversus abdominis,multifidus and triceps were calculated. Results After treatment,the scores of VAS,JOA and ODI improved significantly in both groups(|t|>8.515,P<0.001),and the scores were better in the experimental group than in the control group(|t|>2.089,P<0.05).RMS and iEMG of transversus abdominis and multifidus improved significantly after treatment in both groups(|t|>18.831,P<0.001),and were significantly better in the experimental group(|t|>3.481,P<0.05).The transversus abdominis and multifidus in both groups were activated before the movement muscles,and the relative activation time of transversus abdominis and multifidus increased in negative(|t|>48.115,P<0.001),the experimental group being better(|t|>3.229,P<0.05). Conclusion Combination of core stability training and respiratory training is beneficial in reducing the pain of patients with CNLBP,reducing the lumbar dysfunction,improving the order of muscle activation,and strengthening feed-forward control.

Objective:To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.Methods:The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-Ⅳ v2.0 (MIMIC-Ⅳ v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA).Results:A total of 3?295 patients with septic shock were included, with 2?164 surviving and 1?131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2?307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95% CI) was 0.795-0.849], 0.823 (95% CI was 0.796-0.849), and 0.810 (95% CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95% CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95% CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca 2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score Ⅲ (SAPSⅢ), the highest WBC, acute physiology score Ⅲ (APSⅢ), the lowest Na +, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95% CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APSⅢ score, SAPSⅢ score, and sequential organ failure assessment (SOFA) score] with AUC (95% CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. Conclusions:The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca 2+, Hb, WBC, SAPSⅢ score, APSⅢ score, Na +, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.