AIM:To evaluate the clinical efficacy of 0.05% cyclosporine eye drops(Ⅱ)combined with sodium hyaluronate eye drops in treating patients with type 2 diabetes mellitus(T2DM)and moderate-to-severe dry eye.METHODS:A total of 120 T2DM patients(120 eyes)with moderate-to-severe dry eye, treated at the endocrinology and ophthalmology departments at the Affiliated Hospital of Xuzhou Medical University from January 2024 to September 2024, were enrolled in the study. The patients were randomly divided into two groups: combination group [0.05% cyclosporine eye drops(Ⅱ)+ sodium hyaluronate eye drops] and control group(sodium hyaluronate eye drops alone), with 60 cases(60 eyes)in each group. Assessments were conducted at baseline and at 1, 2, and 3 mo post-treatment, including the ocular surface disease index(OSDI), non-contact tear meniscus height(NITMH), first non-invasive tear breakup time(NIBUTf), meibomian gland loss score, lipid layer thickness grade, conjunctival hyperemia grade, and corneal fluorescein staining(FL)score. At 3 mo after treatment, changes in tear inflammatory factors were observed, and corneal subbasal nerve plexus(SBN)morphology/density were analyzed using in vivo confocal microscopy(IVCM).RESULTS:At 1, 2, and 3 mo post-treatment, both groups showed statistically significant increases in NITMH and NIBUTf compared to baseline(all P<0.05), with greater improvement observed in the combination group(both P<0.05). OSDI and FL scores significantly decreased from baseline(all P<0.05), with more pronounced reductions in the combination group(both P<0.05). Meibomian gland loss scores showed no significant improvement in either group(all P>0.05). At 3 mo after treatment, tear levels of interleukin 6(IL-6)and matrix metalloproteinase-9(MMP-9)significantly decreased in both groups(all P<0.001), with a greater reduction noted in the combination group(both P<0.001). The combination group displayed increased corneal nerve branch density and nerve fiber density, along with decreased nerve tortuosity and dendritic cell(DC)density compared to baseline(all P<0.001), while the control group did not show significant changes(all P>0.05).CONCLUSION: The combination of 0.05% cyclosporine eye drops(Ⅱ)and sodium hyaluronate eye drops significantly improves clinical outcomes in T2DM patients with moderate-to-severe dry eye. This treatment effectively alleviates ocular surface inflammation, restores corneal nerve morphology and density, and demonstrates a favorable safety profile.

AIM: To explore the risk factors associated with diabetic retinopathy(DR)based on ultra-widefield swept-source optical coherence tomography angiography(UWF-SS-OCTA), and to establish a clinical prediction model.METHODS:A total of 235 patients(235 eyes)with type 2 diabetes mellitus who were treated in the Affiliated Hospital of Xuzhou Medical University from July to November 2024 were selected as the research objects. According to the presence or absence of DR, they were divided into 120 cases(120 eyes)in non-DR group(NDR group)and 115 cases(115 eyes)in non-proliferative DR group(NPDR group). Data on general characteristics, laboratory tests, and OCTA results were collected for both groups. Univariate analysis was employed to identify DR-related risk factors. Logistic regression analysis was conducted to analyze these risk factors and to establish a DR prediction model. The efficacy of the model was evaluated using the receiver operating characteristic(ROC)curve, calibration curve, and decision curve analysis(DCA).RESULTS: The duration of diabetes, fasting blood glucose, blood urea nitrogen(BUN), history of hypertension, and the choroidal vascular index(CVI)were found to be statistically significant in the model(all P<0.05). Specifically, the duration of diabetes, fasting blood glucose, BUN, and history of hypertension were identified as risk factors for DR among diabetic patients, while CVI was recognized as a protective factor. The area under the curve for the model predicting the probability of DR was 0.898(0.859-0.938), with a diagnostic threshold of 0.438. The corresponding sensitivity and specificity were 87.8% and 78.3%, respectively, indicating that the model possesses high predictive value for the occurrence of DR.CONCLUSION: The duration of diabetes, fasting blood glucose, BUN, history of hypertension, and CVI are significantly correlated with DR. The established prediction model demonstrates a substantial screening capability for DR.

Skin malignant tumors mostly occur in the light exposure site. Early diagnosis and treatment can effectively improve the survival rate of patients. Histopathological examination is the gold standard for clinical diagnosis of skin malignant tumors, but this method is an invasive operation which brings pain to patients and takes a long time, and may cause problems such as incision infection and scar formation. In recent years, spectroscopy technology has developed rapidly. It is a non-invasive real-time detection method, which can be simply operated, and has a high sensitivity. It has been gradually applied to the diagnosis of skin malignant tumors. This paper reviews the application progress of spectroscopy technology, including fluorescence spectroscopy, Raman spectroscopy and infrared spectroscopy in the diagnosis of skin malignant tumors.

N⁶-methyladenosine (m⁶A) modification plays a critical role in cell cycle regulation, while the mechanism of m⁶A in regulating mitosis remains underexplored. Here, we found that the total m⁶A modification level in cells increased during mitosis by the liquid chromatography-mass spectrometry/mass spectrometry and m⁶A dot blot assays. Silencing methyltransferase-like 3 (METTL3) or METTL14 results in delayed mitosis, abnormal spindle assembly, and chromosome segregation defects by the immunofluorescence. By analyzing transcriptome-wide m⁶A targets in HeLa cells, we identified polo-like kinase 1 (PLK1) as a key gene modified by m⁶A in regulating mitosis. Specifically, through immunoblotting and RNA pulldown, m⁶A modification inhibits PLK1 translation via YTH N⁶-methyladenosine RNA binding protein 1, thus mediating cell cycle homeostasis. Demethylation of PLK1 mRNA leads to significant mitotic abnormalities. These findings highlight the critical role of m⁶A in regulating mitosis and the potential of m⁶A as a therapeutic target in proliferative diseases such as cancer.
Humans ; Polo-Like Kinase 1 ; Cell Cycle Proteins/metabolism* ; Proto-Oncogene Proteins/metabolism* ; Protein Serine-Threonine Kinases/metabolism* ; Mitosis/physiology* ; HeLa Cells ; Adenosine/genetics* ; Methyltransferases/metabolism* ; RNA, Messenger/metabolism* ; RNA-Binding Proteins/metabolism*

Biomimetic wet-adhesive hydrogels mimic the adhesive properties of biological organisms to achieve strong bonding in moist environments.Compared to conventional medical adhesives,these materials are characterized by enhanced biocompatibility,robust ad-hesion,and adjustable physicochemical properties.Although biomimetic wet-adhesive hydrogels have been applied in oral mucosal drug delivery,intraoral wound management,and implant surgery,a systematic review is currently lacking.This article aims to summarize the wet-adhesion mechanisms of bio-inspired materials and their applications in various scenarios and to provide insights and methodolo-gies for the design of novel intraoral dressings.

Objective:To construct the risk prediction model for early-onset peritoneal dialysis-associated peritonitis (PDAP) in peritoneal dialysis patients based on six machine learning algorithms.Methods:This study was retrospective. Convenience sampling was used to select peritoneal dialysis patients who were regularly followed up in the Department of Nephrology of Guizhou Provincial People's Hospital from December 2009 to August 2023 to collect general information, primary diseases, and laboratory indicators of the study population. It was randomly divided into a modeling set and validation set in the ratio of 7∶3. With the occurrence of early-onset PDAP as the dependent variable, the risk prediction model of early-onset PDAP in peritoneal dialysis patients was constructed based on six machine learning algorithms, namely, Logistic regression, decision tree, support vector machine, random forest, extreme gradient boosting, and artificial neural network, respectively. Model performance was evaluated based on the area under the receiver operating characteristic curve ( AUC) , accuracy, and F1 score to select the optimal model. Results:The final data of 890 peritoneal dialysis patients were analyzed, of which 86 patients developed early-onset PDAP, and the incidence of early-onset PDAP was 9.66%. The four prediction models, Logistic regression, support vector machine, extreme gradient boosting, and random forest, had high accuracy with AUC values of 0.703, 0.729, 0.782, and 0.814, respectively, with the random forest model having higher AUC value, accuracy, and F1 score. Further ranking of the importance of risk factors for early-onset PDAP based on the random forest model showed that the top five characteristic variables were C-reactive protein, triglycerides, platelet, ferritin, and leukocyte, in that order. Conclusions:The risk prediction model for early-onset PDAP in peritoneal dialysis patients constructed based on the random forest model has optimal performance, which can help medical and nursing staff assess and prevent early-onset PDAP at an early stage.

Biomimetic wet-adhesive hydrogels mimic the adhesive properties of biological organisms to achieve strong bonding in moist environments.Compared to conventional medical adhesives,these materials are characterized by enhanced biocompatibility,robust ad-hesion,and adjustable physicochemical properties.Although biomimetic wet-adhesive hydrogels have been applied in oral mucosal drug delivery,intraoral wound management,and implant surgery,a systematic review is currently lacking.This article aims to summarize the wet-adhesion mechanisms of bio-inspired materials and their applications in various scenarios and to provide insights and methodolo-gies for the design of novel intraoral dressings.

Objective:To construct the risk prediction model for early-onset peritoneal dialysis-associated peritonitis (PDAP) in peritoneal dialysis patients based on six machine learning algorithms.Methods:This study was retrospective. Convenience sampling was used to select peritoneal dialysis patients who were regularly followed up in the Department of Nephrology of Guizhou Provincial People's Hospital from December 2009 to August 2023 to collect general information, primary diseases, and laboratory indicators of the study population. It was randomly divided into a modeling set and validation set in the ratio of 7∶3. With the occurrence of early-onset PDAP as the dependent variable, the risk prediction model of early-onset PDAP in peritoneal dialysis patients was constructed based on six machine learning algorithms, namely, Logistic regression, decision tree, support vector machine, random forest, extreme gradient boosting, and artificial neural network, respectively. Model performance was evaluated based on the area under the receiver operating characteristic curve ( AUC) , accuracy, and F1 score to select the optimal model. Results:The final data of 890 peritoneal dialysis patients were analyzed, of which 86 patients developed early-onset PDAP, and the incidence of early-onset PDAP was 9.66%. The four prediction models, Logistic regression, support vector machine, extreme gradient boosting, and random forest, had high accuracy with AUC values of 0.703, 0.729, 0.782, and 0.814, respectively, with the random forest model having higher AUC value, accuracy, and F1 score. Further ranking of the importance of risk factors for early-onset PDAP based on the random forest model showed that the top five characteristic variables were C-reactive protein, triglycerides, platelet, ferritin, and leukocyte, in that order. Conclusions:The risk prediction model for early-onset PDAP in peritoneal dialysis patients constructed based on the random forest model has optimal performance, which can help medical and nursing staff assess and prevent early-onset PDAP at an early stage.

Skin malignant tumors mostly occur in the light exposure site. Early diagnosis and treatment can effectively improve the survival rate of patients. Histopathological examination is the gold standard for clinical diagnosis of skin malignant tumors, but this method is an invasive operation which brings pain to patients and takes a long time, and may cause problems such as incision infection and scar formation. In recent years, spectroscopy technology has developed rapidly. It is a non-invasive real-time detection method, which can be simply operated, and has a high sensitivity. It has been gradually applied to the diagnosis of skin malignant tumors. This paper reviews the application progress of spectroscopy technology, including fluorescence spectroscopy, Raman spectroscopy and infrared spectroscopy in the diagnosis of skin malignant tumors.

Objective:To evaluate the short-term rehabilitation effect of wearable visual training devices based on extended reality (XR) glasses in patients after macular hole surgery.Methods:A self-controlled study was conducted.Eleven patients with monocular low vision after macular hole surgery were recruited at Renmin Hospital of Wuhan University from October 2022 to March 2024.All patients underwent biofeedback training for 3 months using the independently developed visual rehabilitation training glasses LOOKBON T10.The LogMAR best corrected visual acuity (BCVA), retinal sensitivity, effective fixation rate, fixation stability, reading speed, vertical metamorphopsia (MV), horizontal metamorphopsia (MH), and Chinese version of the visual-related quality of life assessment form (CVRQoL-25) were compared before and after training.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY2024-K263).Written informed consent was obtained from each subject.Results:After training, the patients' BCVA, retinal sensitivity, effective fixation rate, fixation stability, and reading speed were 0.69±0.19, (21.61±2.75)db, (92.43±4.06)%, (93.09±4.31)%, and (104.82±21.85) characters/minute, respectively, which were significantly improved compared to 0.85±0.28, (17.71±3.17)db, (31.83±19.05)%, (32.35±19.12)%, and (69.64±20.17) characters/minute before training ( t=5.253, -5.987, -11.561, -12.003, -11.682; all at P<0.001).After training, MV and MH were (0.29±0.20)° and (0.21±0.24)°, respectively, which were significantly reduced compared to pre-training (0.44±0.24)° and (0.43±0.41)° ( t=9.238, 4.068; both at P<0.01).After training, the CVRQoL-25 score was 1 193.18±229.43, which was significantly higher than pre-training 947.73±203.86 ( t=-11.687, P<0.001). Conclusions:The application of wearable visual training equipment based on XR glasses can effectively improve the visual function of patients with poor visual function recovery after macular hole surgery, and enhance their quality of life.