BACKGROUND: Prolonged occupational noise exposure poses potential health risks, but its impact on mitochondrial DNA (mtDNA) damage and methylation patterns remains unclear. METHOD: We recruited 306 factory workers, using average binaural high-frequency hearing thresholds from pure-tone audiometry to assess noise exposure. MtDNA damage was evaluated through mitochondrial DNA copy number (mtDNAcn) and lesion rate, and mtDNA methylation changes were identified via pyrophosphate sequencing. RESULTS: There was a reduction in MT-RNR1 methylation of 4.52% (95% CI: -7.43% to -1.62%) among workers with abnormal hearing, whereas changes in the D-loop region were not statistically significant (β = -2.06%, 95% CI: -4.44% to 0.31%). MtDNAcn showed a negative association with MT-RNR1 methylation (β = -0.95, 95% CI: -1.23 to -0.66), while no significant link was found with D-loop methylation (β = -0.05, 95% CI: -0.58 to 0.48). Mediation analysis indicated a significant increase in mtDNAcn by 10.75 units (95% CI: 3.00 to 21.26) in those with abnormal hearing, with MT-RNR1 methylation mediating 35.9% of this effect. CONCLUSIONS These findings suggest that occupational noise exposure may influence compensatory increases in mtDNA content through altered MT-RNR1 methylation.
Humans ; DNA, Mitochondrial ; DNA Methylation ; Male ; Adult ; Noise, Occupational/adverse effects* ; Middle Aged ; Occupational Exposure/adverse effects* ; Biomarkers/blood* ; Female

Palmitoylation, an essential covalent attachment of a fatty acid (usually C16 palmitate) to cysteine residues within proteins, is crucial for regulating protein functionality and enzymatic activities. This lipid modification facilitates the anchoring of proteins to cellular membranes, dictating their subcellular distribution and influencing protein transport dynamics and intracellular positioning. Additionally, it plays a role in regulating protein degradation through the ubiquitin-proteasome system. Palmitoylation is implicated in the pathogenesis and progression of cardiovascular diseases by modulating substrates and prompting additional post-translational modifications, as well as by interacting with other molecular alterations. Moreover, an intervention strategy focusing on palmitoylation processes is anticipated to offer novel therapeutic avenues for cardiovascular pathologies and address extant challenges in clinical settings. This review consolidates current research on the role and importance of palmitoylation in cardiovascular diseases by exploring its regulatory functions, the catalyzing enzymes, and the involved substrates. It highlights recent discoveries connecting palmitoylation-targeted therapies to cardiovascular health and examines potential approaches and future challenges in cardiovascular treatment.

Objective:s To investigate the risk factors for delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) in patients with acute carbon monoxide poisoning (ACOP) and to develop predictive models based on machine learning algorithms.Methods:Patients with ACOP hospitalized at the First Affiliated Hospital of Zhengzhou University from August 2019 to October 2024 were included, with the occurrence of DEACMP as the outcome measure. The dataset was randomly divided into training and validation sets at a ratio of 7:3. Lasso regression was used to select features influencing the outcome in training sets. Nine machine learning models—including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—were constructed. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) calculated for each model. Calibration curves were used to assess accuracy, and decision curve analysis (DCA) was applied to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) method was employed to visualize and interpret the best-performing model.Results:A total of 264 ACOP patients were included, of whom 54 (20.5%) developed DEACMP. Lasso regression identified eight key feature variables. Based on these factors, predictive models were constructed, showing good AUC stability across the nine machine learning models in both training (0.92–0.99) and validation sets (0.85–0.91). The RF model performed best, with an AUC of 0.99 in the training set and 0.90 in the validation set; its calibration curve and DCA curve also demonstrated excellent performance. SHAP analysis of the RF model revealed the importance ranking of factors from highest to lowest as follows: Glasgow Coma Scale (GCS) score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, diastolic blood pressure (DBP), and drinking history.Conclusions:The RF model exhibited the highest predictive performance for DEACMP occurrence in ACOP patients. The influencing factors, ranked in order of importance from highest to lowest, are as follows: GCS score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, DBP, and drinking history.

Objective: To evaluate the feasibility of exempting Asian-type DEL pregnant women from anti-D monitoring and RhD immunoglobulin prophylaxis injections by comparing and analyzing the clinical incidence of anti-D alloimmunization between Asian-type DEL pregnant women and true RhD-negative pregnant women. Methods: A total of 165 pregnant women who were initially screened as RhD negative by the saline method and received medical treatment in our hospital from December 2022 to August 2024 were collected as the research subjects. Absorption and elution tests, DEL genotyping, and gene sequencing were used to divide the pregnant women into the Asian-type DEL group and the true negative group. After obtaining informed consent, the following clinical management plan was implemented for pregnant women with Asian-type DEL: exemption from routine anti-D antibody detection, exemption from RhD immunoglobulin prophylaxis, and transfusion of RhD-positive red blood cells. Blood samples of newborns were sent for examination of hemolytic disease of the fetus and newborn (HDFN). The routine management plan was implemented for true negative pregnant women. The incidence of alloimmunization and HDFN was comparatively analyzed between the two groups. Results: Among 165 initially screened RhD negative pregnant women, serological testing and genotyping confirmed 42 as Asian-type DEL, 9 as D variant, and 114 as true negative. Among 42 pregnant women with Asian-type DEL, 3 cases tested positive for HDFN due to receiving RhD immunoglobulin prophylaxis injection. The remaining 39 cases were exempted from anti-D testing after being fully informed of the risk, and did not receive RhD immunoglobulin prophylaxis. The HDFN tests were all negative. In the true negative group, anti-D antibodies were detected in 20 cases, of which 6 cases tested positive for HDFN. A pregnant woman with Asian -type DEL did not show RhD homologous immune response after receiving 2 units of RhD positive red blood cells. Statistical analysis revealed a significantly lower risk of anti-D alloimmunization in Asian-type DEL carriers compared to true D-negative pregnant women (P<0.05). Conclusion: Pregnant women with Asian-type DEL can be exempted from routine anti-D antibody testing and do not require routine RhD immunoglobulin prophylaxis injections.

With the development of medical technology towards precision and individualization, the protection of gastric function and the postoperative quality of life of patients are increasingly receiving the attention and emphasis of surgeons. In the context of the booming development of function-preserving gastrectomy, the combined approach using both laparoscopy and endoscopy also presents a trend of diversity and innovation. The laparoscopic and endoscopic cooperative surgery not only addresses for the limitations of each method, but also plays a role in locating the tumor precisely, narrowing the scope of gastrectomy, preserving the anatomical structure and function of the stomach, maintaining the continuity of the gastric wall, precise lymphadenectomy, and preserving the vagus nerve in function-preserving gastrectomy. In this paper, the author expounds on the above advantages and scope of application of dual-scope cooperative surgery and recognizes the significance of dual-scope cooperative surgery in future development.

This paper reviews the current application status of big language model in nursing, the needs for information technology development in critical care nursing, the current application status of and future development direction of big language model in critical care nursing, as well as the risks and challenges faced, with a view to providing a reference for promoting the application of big language model in critical care nursing.

With the development of medical technology towards precision and individualization, the protection of gastric function and the postoperative quality of life of patients are increasingly receiving the attention and emphasis of surgeons. In the context of the booming development of function-preserving gastrectomy, the combined approach using both laparoscopy and endoscopy also presents a trend of diversity and innovation. The laparoscopic and endoscopic cooperative surgery not only addresses for the limitations of each method, but also plays a role in locating the tumor precisely, narrowing the scope of gastrectomy, preserving the anatomical structure and function of the stomach, maintaining the continuity of the gastric wall, precise lymphadenectomy, and preserving the vagus nerve in function-preserving gastrectomy. In this paper, the author expounds on the above advantages and scope of application of dual-scope cooperative surgery and recognizes the significance of dual-scope cooperative surgery in future development.

This paper reviews the current application status of big language model in nursing, the needs for information technology development in critical care nursing, the current application status of and future development direction of big language model in critical care nursing, as well as the risks and challenges faced, with a view to providing a reference for promoting the application of big language model in critical care nursing.

Vestibular schwannomas (VSs) are the most common cerebellopontine tumors. The natural history of smaller-sized VSs (<30 mm) has been well-studied, leading to the recommendation of a “watch and wait” approach. However, large VSs (>30 mm) have not been extensively studied, mainly because of their rarity. As such, most patients are conventionally offered surgery which carries a significant risk of neurological morbidity. Here, we report a case of a giant VS (>40 mm) in a 30-year-old man who regressed spontaneously. He was lost to follow-up for 18 years and, upon re-presentation, the symptomatology drastically improved and repeat imaging demonstrated a marked reduction in tumor size. Referring to similar cases in other studies, we postulate that most large and giant VSs undergo a phase of growth and stasis, followed by regression due to shifts in the balance between tumorigenic and regressive factors. Taken together with emerging molecular data, further studies are required to better understand the history of large and giant VSs to shape more personalized treatment options. This potentially includes non-operative management as a tenable option.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.