ObjectiveTo investigate the independent predictive factors for 90-day mortality in patients with acute-on-chronic liver failure （ACLF）， to establish a risk predictive model， and to assess its predictive efficacy in comparison with MELD， MELD-Na， MELD 3.0， and COSSH-ACLF Ⅱ. MethodsA retrospective analysis was performed for the clinical data of 394 patients with ACLF who were admitted to The Affiliated Hospital of Inner Mongolia Medical University and Hohhot Second Hospital from July 2018 to July 2024， and general information and laboratory markers on admission were collected from all patients. The independent-samples t test or the Mann-Whitney U test was used for comparison of quantitative data between two groups， and the chi-square test or the adjusted chi-square test was used for comparison of qualitative data between two groups. The LASSO regression analysis was used to identify related variables， and the multivariate logistic regression analysis was used to establish a predictive model and generate a nomogram. The receiver operating characteristic （ROC） curve， the area under the ROC curve （AUC）， calibration curve， and clinical decision curve were used to assess the performance of the model. ResultsA total of 394 patients with ACLF were included in this study， with 136 patients in the training set， 58 in the internal validation set， and 200 in the external validation set. The cohort had a mean age of 52.9±11.7 years， among whom male patients accounted for 72.84% （287/394）， the patients with HBV infection accounted for 22.33% （88/394）， the patients with alcohol-related causes accounted for 45.94% （181/394）， and the patients with other causes （including drug-induced and autoimmune diseases） accounted for 31.73% （125/394）. The overall 90-day mortality rate was 27.41% （108/394）. The multivariate logistic regression analysis showed that diabetes （odds ratio ［OR］= 5.831， 95% confidence interval ［CI］： 1.587 — 21.424， P=0.008）， cystatin C （Cys-C） （OR=2.984， 95%CI： 1.501 — 5.933， P=0.002）， and spontaneous peritonitis （SBP） （OR=5.692， 95%CI： 2.150 — 15.071， P<0.001） were independent risk factors， and a nomogram was generated based on these factors. This model had an AUC of 0.836 in the training set， 0.881 in the internal validation set， and 0.878 in the external validation set， showing a good discriminatory ability. The calibration curve showed a good degree of fitting， with a relatively high net clinical benefit. The subgroup analysis based on etiology showed that the model had an AUC of 0.850 in the patients with HBV infection， 0.858 in the patients with alcohol-induced ACLF， and 0.908 in the patients with other etiologies， indicating that the model had a good discriminatory ability across the populations with different etiologies. Compared with traditional scores， the model （AUC=0.836） had a significantly better predictive value than MELD （AUC=0.619， Z=3.197， P=0.001）， MELD-Na （AUC=0.651， Z=2.998， P=0.003）， MELD 3.0 （AUC=0.601， Z=3.682， P<0.001）， and COSSH-ACLF Ⅱ （AUC=0.719， Z=2.396， P=0.017） alone. ConclusionDiabetes， SBP， and Cys-C are independent risk factors for 90-day mortality in patients with ACLF. Compared with MELD， MELD-Na， MELD 3.0， and COSSH-ACLF Ⅱ scores， this model has a higher predictive value for 90-day prognosis in patients with ACLF and is suitable for patients with ACLF caused by various etiologies.

Chronic pain is a complex condition shaped by long-standing alterations in both physiological and psychological processes. Rather than representing a simple continuation of acute nociceptive signaling, chronic pain is increasingly understood as the outcome of progressive dysregulation within distributed neural systems that govern sensation, affect, motivation, and cognitive control. Neuroimaging and electrophysiological studies indicate that this state is accompanied by extensive plastic changes in deep brain structures and large-scale networks. Beyond well-described central sensitization processes, chronic pain is characterized by disrupted oscillatory rhythms and altered connectivity within large-scale brain networks, including thalamo-cortical circuits and prefrontal-limbic-reward networks. These findings support a conceptual shift from viewing chronic pain as a focal, lesion-driven phenomenon toward recognizing it as a disorder of distributed network pathology. Pharmacological treatments remain central to clinical practice, yet their long-term efficacy is often limited and frequently accompanied by substantial side effects. The ongoing concerns about opioid-related risks and the inadequate therapeutic response in a subset of patients highlight the need for safe, non-pharmacological approaches that can address not only pain but also comorbid disturbances in mood, sleep, and social functioning. Neuromodulation provides a promising path toward mechanism-based and non-pharmacological management of chronic pain by employing physical or chemical stimulation to alter the excitability and synchrony of specific neural populations within central, peripheral, and autonomic systems. While invasive deep brain stimulation demonstrates that targeting deep brain structures can be effective, its clinical application is restricted by surgical risks and cost, highlighting the importance of non-invasive techniques capable of reaching deep targets. Current non-invasive approaches, such as transcranial electric stimulation, are constrained by limited penetration depth and insufficient spatial precision. These limitations hinder reliable engagement of deep regions implicated in pain, including the thalamus and nucleus accumbens, and tend to produce broad, non-specific modulation of cross-network oscillatory activity. Temporal interference (TI) stimulation has emerged as a means of overcoming these obstacles. By delivering interacting high-frequency currents that generate a low-frequency envelope within the head, TI enables focal stimulation of deep targets while minimizing superficial current delivery. Recent multiscale modeling and animal studies indicate that TI exploits the nonlinear rectification properties of neuronal membranes in response to high-frequency carriers, as well as their phase-locked responses to low-frequency envelopes, to generate “peak-focused” electric fields in deep regions under relatively low superficial current loads. Moreover, TI appears to exhibit potential advantages in terms of cell-type selectivity and rhythm-specific engagement, including differential responses across neuronal subtypes and distinct coupling to θ-, β-, and γ-band oscillations. These features suggest a promising avenue for correcting abnormal rhythms and network dynamics that contribute to chronic pain. This review summarizes current knowledge of the neural mechanisms underlying chronic pain and recent advances in TI research. It examines functional disturbances across key pain-related regions and networks, outlines the principles and technical characteristics of TI, and discusses potential deep-brain targets and stimulation strategies relevant to chronic pain. Evidence to date indicates that TI, with its non-invasiveness, tolerability, and capacity for precise deep brain modulation, holds great promise for the management of treatment-resistant chronic pain and may evolve into a new generation of precise and efficient non-pharmacological analgesic strategies.

Chronic pain is a complex condition shaped by long-standing alterations in both physiological and psychological processes. Rather than representing a simple continuation of acute nociceptive signaling, chronic pain is increasingly understood as the outcome of progressive dysregulation within distributed neural systems that govern sensation, affect, motivation, and cognitive control. Neuroimaging and electrophysiological studies indicate that this state is accompanied by extensive plastic changes in deep brain structures and large-scale networks. Beyond well-described central sensitization processes, chronic pain is characterized by disrupted oscillatory rhythms and altered connectivity within large-scale brain networks, including thalamo-cortical circuits and prefrontal-limbic-reward networks. These findings support a conceptual shift from viewing chronic pain as a focal, lesion-driven phenomenon toward recognizing it as a disorder of distributed network pathology. Pharmacological treatments remain central to clinical practice, yet their long-term efficacy is often limited and frequently accompanied by substantial side effects. The ongoing concerns about opioid-related risks and the inadequate therapeutic response in a subset of patients highlight the need for safe, non-pharmacological approaches that can address not only pain but also comorbid disturbances in mood, sleep, and social functioning. Neuromodulation provides a promising path toward mechanism-based and non-pharmacological management of chronic pain by employing physical or chemical stimulation to alter the excitability and synchrony of specific neural populations within central, peripheral, and autonomic systems. While invasive deep brain stimulation demonstrates that targeting deep brain structures can be effective, its clinical application is restricted by surgical risks and cost, highlighting the importance of non-invasive techniques capable of reaching deep targets. Current non-invasive approaches, such as transcranial electric stimulation, are constrained by limited penetration depth and insufficient spatial precision. These limitations hinder reliable engagement of deep regions implicated in pain, including the thalamus and nucleus accumbens, and tend to produce broad, non-specific modulation of cross-network oscillatory activity. Temporal interference (TI) stimulation has emerged as a means of overcoming these obstacles. By delivering interacting high-frequency currents that generate a low-frequency envelope within the head, TI enables focal stimulation of deep targets while minimizing superficial current delivery. Recent multiscale modeling and animal studies indicate that TI exploits the nonlinear rectification properties of neuronal membranes in response to high-frequency carriers, as well as their phase-locked responses to low-frequency envelopes, to generate “peak-focused” electric fields in deep regions under relatively low superficial current loads. Moreover, TI appears to exhibit potential advantages in terms of cell-type selectivity and rhythm-specific engagement, including differential responses across neuronal subtypes and distinct coupling to θ-, β-, and γ-band oscillations. These features suggest a promising avenue for correcting abnormal rhythms and network dynamics that contribute to chronic pain. This review summarizes current knowledge of the neural mechanisms underlying chronic pain and recent advances in TI research. It examines functional disturbances across key pain-related regions and networks, outlines the principles and technical characteristics of TI, and discusses potential deep-brain targets and stimulation strategies relevant to chronic pain. Evidence to date indicates that TI, with its non-invasiveness, tolerability, and capacity for precise deep brain modulation, holds great promise for the management of treatment-resistant chronic pain and may evolve into a new generation of precise and efficient non-pharmacological analgesic strategies.

Objective To investigate the interaction between a novel antimicrobial compound,HL-J6,and penicillin-binding protein 1(PBP1)of Staphylococcus aureus.Methods With MRSA252 genomic DNA as the template and PBP1F and PBP1R as primers,the expression plasmid pET30a-pbp1-39-608 was constructed by amplifying the target gene fragment followed by cloning into the Nde I/Xho I restriction sites of the pET30a vector.Then the obtained plasmids were transformed into Escherichia coli for the expression of PBP1-39-608 protein,and the product was purified by affinity chromatography.The inhibitory effect of HL-J6 on the transpeptidase activity of PBP1-39-608 was measured using peptidoglycan side chain backbone peptide,with thiol ester analog S2d as the substrate.The affinity between HL-J6 and PBP1-39-608 was detected using microscale thermophoresis(MST),and the binding interaction was confirmed by cellular thermal shift assay(CETSA).Molecular docking and dynamics simulation were performed using AutoDock Vina and Desmond software,respectively,to elucidate the binding mode of HL-J6 with the PBP1-39-608 protein and the key amino acid residues involved.Results The recombinant plasmid pET30a-pbp1-39-608 was successfully constructed,and PBP1-39-608 protein was produced after induction and purified,yielding a protein with an approximate molecular mass of 65×103.HL-J6 inhibited the transpeptidase activity of PBP1-39-608 in a time-dependent manner(P<0.001).The dissociation constant Kd of the binding between HL-J6 and PBP1-39-608 was 64.92 μmol/L.Molecular docking results showed that HL-J6 bound to the active pocket of PBP1-39-608 by interacting with key residues such as ILE-348,ASN-370,THR-516 and PHE-423,with a binding score of-8.38 kcal/mol(<-5.00 kcal/mol).Dynamics simulation results indicated that the complex became stable after 50 ns.Conclusion HL-J6 effectively inhibits the transpeptidase activity of Staphylococcus aureus PBP1,and shows stable interaction with the protein.

ObjectiveAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties with communication and social interaction, restricted and repetitive behaviors. Previous studies have indicated that individuals with ASD exhibit early and lifelong attention deficits, which are closely related to the core symptoms of ASD. Basic visual attention processes may provide a critical foundation for their social communication and interaction abilities. Therefore, this study explores the behavior of children with ASD in capturing attention to changes in topological properties. MethodsOur study recruited twenty-seven ASD children diagnosed by professional clinicians according to DSM-5 and twenty-eight typically developing (TD) age-matched controls. In an attention capture task, we recorded the saccadic behaviors of children with ASD and TD in response to topological change (TC) and non-topological change (nTC) stimuli. Saccadic reaction time (SRT), visual search time (VS), and first fixation dwell time (FFDT) were used as indicators of attentional bias. Pearson correlation tests between the clinical assessment scales and attentional bias were conducted. ResultsThis study found that TD children had significantly faster SRT (P<0.05) and VS (P<0.05) for the TC stimuli compared to the nTC stimuli, while the children with ASD did not exhibit significant differences in either measure (P>0.05). Additionally, ASD children demonstrated significantly less attention towards the TC targets (measured by FFDT), in comparison to TD children (P<0.05). Furthermore, ASD children exhibited a significant negative linear correlation between their attentional bias (measured by VS) and their scores on the compulsive subscale (P<0.05). ConclusionThe results suggest that children with ASD have difficulty shifting their attention to objects with topological changes during change detection. This atypical attention may affect the child’s cognitive and behavioral development, thereby impacting their social communication and interaction. In sum, our findings indicate that difficulties in attentional capture by TC may be a key feature of ASD.

Researchers commonly use cyclization recombination enzyme/locus of X-over P1 (Cre/loxP) technology-based conditional gene knockouts of model mice to investigate the functional roles of genes of interest in Sertoli and Leydig cells within the testis. However, the shortcomings of these genetic tools include high costs, lengthy experimental periods, and limited accessibility for researchers. Therefore, exploring alternative gene silencing techniques is of great practical value. In this study, we employed adeno-associated virus (AAV) as a vector for gene silencing in Sertoli and Leydig cells. Our findings demonstrated that AAV serotypes 1, 8, and 9 exhibited high infection efficiency in both types of testis cells. Importantly, we discovered that all three AAV serotypes exhibited exquisite specificity in targeting Sertoli cells via tubular injection while demonstrating remarkable selectivity in targeting Leydig cells via interstitial injection. We achieved cell-specific knockouts of the steroidogenic acute regulatory ( Star ) and luteinizing hormone/human chorionic gonadotropin receptor (Lhcgr) genes in Leydig cells, but not in Sertoli cells, using AAV9-single guide RNA (sgRNA)-mediated gene editing in Rosa26-LSL-Cas9 mice. Knockdown of androgen receptor ( Ar ) gene expression in Sertoli cells of wild-type mice was achieved via tubular injection of AAV9-short hairpin RNA (shRNA)-mediated targeting. Our findings offer technical approaches for investigating gene function in Sertoli and Leydig cells through AAV9-mediated gene silencing.
Animals ; Male ; Leydig Cells/metabolism* ; Mice ; Dependovirus/genetics* ; Sertoli Cells/metabolism* ; Gene Silencing ; Genetic Vectors ; Testis/cytology*

OBJECTIVES: To explore the efficacy and adverse reactions of nusinersen combined with risdiplam in the treatment of spinal muscular atrophy (SMA). METHODS: A retrospective analysis was conducted on the clinical data of 10 pediatric SMA patients treated with nusinersen combined with risdiplam at the Children's Medical Center of Xiangya Hospital, Central South University. RESULTS: Among the 10 SMA patients, there were 4 with type I, 4 with type II, and 2 with type III. Nine patients initially received nusinersen monotherapy, while 1 patient received nusinersen combined with risdiplam. The median duration of combination therapy with nusinersen and risdiplam for the 10 patients was 10.5 months (range: 0.5-20.0 months), with 6 patients undergoing combination therapy for more than 6 months, showing improvements in motor and/or respiratory function. The remaining 4 patients had combination treatment durations of 0.5, 1.0, 1.3, and 4.0 months, respectively, with no significant overall improvement. After combined treatment, 5 patients experienced skin hyperpigmentation, 2 had lumbar puncture site pain, 1 experienced vomiting, 1 had increased sputum production, and 1 had reduced total sleep time. All adverse reactions were mild and did not require medical intervention. CONCLUSIONS Nusinersen combined with risdiplam demonstrates efficacy in the treatment of SMA, and no significant adverse reactions have been observed.
Humans ; Oligonucleotides/adverse effects* ; Male ; Female ; Child, Preschool ; Retrospective Studies ; Infant ; Muscular Atrophy, Spinal/drug therapy* ; Drug Therapy, Combination ; Child ; Azo Compounds ; Pyrimidines

BACKGROUND: The association of systemic inflammatory response index (SIRI) with prognosis of coronary artery disease (CAD) patients has never been investigated in a large sample with long-term follow-up. This study aimed to explore the association of SIRI with all-cause and cause-specific mortality in a nationally representative sample of CAD patients from United States. METHODS: A total of 3386 participants with CAD from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were included in this study. Cox proportional hazards model, restricted cubic spline (RCS), and receiver operating characteristic curve (ROC) were performed to investigate the association of SIRI with all-cause and cause-specific mortality. Piece-wise linear regression and sensitivity analyses were also performed. RESULTS: During a median follow-up of 7.7 years, 1454 all-cause mortality occurred. After adjusting for confounding factors, higher lnSIRI was significantly associated with higher risk of all-cause (HR = 1.16, 95% CI: 1.09-1.23) and CVD mortality (HR = 1.17, 95% CI: 1.05-1.30) but not cancer mortality (HR = 1.17, 95% CI: 0.99-1.38). The associations of SIRI with all-cause and CVD mortality were detected as J-shaped with threshold values of 1.05935 and 1.122946 for SIRI, respectively. ROC curves showed that lnSIRI had robust predictive effect both in short and long terms. CONCLUSIONS SIRI was independently associated with all-cause and CVD mortality, and the dose-response relationship was J-shaped. SIRI might serve as a valid predictor for all-cause and CVD mortality both in the short and long terms.

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.