Background: The surgical neck of the humerus is located approximately 2–3 cm distal to the anatomical neck (collum anatomicum) at the junction of the greater and lesser tubercles and the humeral shaft. Owing to the transition between cortical and cancellous bones, this region is biomechanically vulnerable and prone to fractures, particularly in older adults. Fractures of the surgical neck of the humerus are classified under the International Classification of Diseases, 10th Revision (ICD-10), with diagnostic codes S42.21.3, S42.22, and S42.23. National statistics indicate that 210,763 new trauma cases were reported in Mongolia in 2023, representing an 11.6% increase compared to the previous year, of which 11,452 cases (5.4%) involved shoulder and humeral injuries. Aim: This study aimed to compare the clinical effects of traditional Mongolian bone-setting therapy and surgical treatment on pain intensity, shoulder function, range of motion, and activities of daily living in patients with surgical neck of the humerus fractures. Materials and Methods: Based on the Department of Traditional Therapies of the Mongolian National University of Medical Sciences (MNUMS) and the Department of Orthopedic Trauma of the Inner Mongolia International Hospital, Inner Mongolia Autonomous Region, China, a total of 60 patients diagnosed with surgical neck fractures of the humerus were enrolled in this study between January 2024 and December 2025. The patients were randomly assigned into two groups: a Mongolian traditional bone-setting therapy group (n=30) and a surgical treatment group (n=30). Assessments were conducted before treatment and at 1 month and 3 months after treatment. Shoulder joint range of motion, activities of daily living, and muscle strength were evaluated using the Constant–Murley score, while pain intensity was assessed using the Visual Analog Scale (VAS). The collected data were systematically analyzed, and the therapeutic outcomes of the two treatment modalities were comprehensively compared and summarized. Result: The baseline demographic and clinical characteristics were comparable between the groups. In the traditional bone-setting group, VAS scores decreased significantly, whereas Constant–Murley scores improved markedly at both follow-up points. The overall outcomes in the traditional bone-setting group were significantly better than those in the surgical group (P<0.05). Conclusion Traditional Mongolian bone-setting therapy offers rapid pain relief, superior long-term functional recovery, and fewer complications, making it a valuable treatment option, particularly for elderly patients and those unsuitable for surgery.

AIM:To observe the effect of cationic nanoemulsion eye drops on the tear film and ocular surface morphology of orthokeratology wearers, and to provide a basis for the clinical selection of eye drops during orthokeratology wear. METHODS:A prospective study was conducted on patients with refractive errors who were fitted with orthokeratology lenses for the first time at Anhui Aier Eye Hospital,from February 2025 to October 2025. Patients were randomly assigned to the experimental group, which received a cationic nanoemulsion eye drops, or the control group, which received a 0.1% sodium hyaluronate eye drops. One drop of the assigned lubricant was instilled in each eye before lens insertion and before lens removal in both groups for 3 consecutive months. Uncorrected visual acuity, spherical equivalent, anterior corneal curvature, tear film break-up time, tear film lipid layer score, and corneal fluorescein staining score were compared between the two groups at baseline and at 1 wk, 1 and 3 mo after lens wear. RESULTS: A total of 64 eyes(all right eyes)from 64 patients with refractive errors were enrolled. Ultimately, 60 patients completed the final follow-up, including 30 in the experimental group(n=30; male/female: 14/16; age: 9.57±1.07 y)and the control group(n=30; male/female: 12/18; age: 9.43±1.01 y). No significant intergroup differences were found in uncorrected visual acuity, spherical equivalent, anterior corneal curvature, or corneal fluorescein staining scores either before or after lens wear(all P>0.05).The tear film break-up time was significantly longer and tear film lipid layer score was significantly better in the experimental group than in the control group at post-wear time point(all P<0.05). Uncorrected visual acuity, spherical equivalent, and anterior corneal curvature improved significantly from baseline at 1 wk, 1 and 3 mo after lens wear in both groups(all P<0.05). Corneal fluorescein staining scores increased significantly from baseline at 1 wk and 1 mo in the control group(both P<0.05), whereas no significant change was observed in the experimental group. No cases of severe corneal injury(score of 3)were found in either group. CONCLUSION: Cationic nanoemulsion eye drops have a similar effect to 0.1% sodium hyaluronate eye drops in improving the visual acuity and corneal morphology of orthokeratology wearers, but have more advantages in stabilizing the tear film and improving the state of the tear film lipid layer, and have good safety, so they can be used as an ideal choice of eye drops for orthokeratology wearers.

ObjectiveCell adhesion is a critical process that regulates cellular physiological functions. Quantitative characterization of adhesion dynamics is essential for elucidating the intrinsic mechanical mechanisms underlying cellular activities. Although atomic force microscopy-based single-cell force spectroscopy is widely used for single-cell adhesion measurements, it requires complex chemical modifications for preparation of live-cell probes, leading to limitations such as cumbersome operation, low throughput, and potential impacts on cell viability. Fluidic force microscopy, which combines atomic force microscopy with microfluidic probes, is a technique allowing the operation of force-controlled nanopipettes in aqueous environments. By applying negative or positive pressure via a pressure controller, a single living cell can be captured onto or released from the cantilever under physiological conditions. This procedure offers a simple workflow and high assay throughput for single-cell adhesion measurements without the need for chemical functionalization. In this study, fluidic force microscopy-based single-cell force spectroscopy was adopted to achieve long-term quantitative characterization of single-cell adhesion dynamics in a simpler and more efficient manner, comparing the dynamic differences in adhesion establishment between two cell lines with different differentiation levels. MethodsHEK 293T and hTERT RPE-1 cells were non-invasively captured on the cantilever of a fluidic force microscope via its integrated microfluidic system during 40 h of adhesion culture. Cell-substrate detachment assays were performed, and force-distance curves were recorded to extract key mechanical adhesion parameters, including adhesion force, adhesion energy, and maximum detachment distance. These measurements were combined with real-time monitoring of cell spreading area to systematically characterize the dynamic evolution of single-cell adhesion. ResultshTERT RPE-1 cells rapidly entered a stable adhesion phase within 1 h after seeding, with both area-normalized adhesion force and area-normalized adhesion energy reaching peak values. In contrast, HEK 293T cells required 4 h to achieve stable adhesion. Subsequently, the adhesion force, adhesion energy and maximum detachment distance of hTERT RPE-1 and HEK 293T cells stabilized at approximately 240 nN vs. 30 nN, 2.2 pJ vs. 0.12 pJ and 6 μm vs. 4 μm, respectively. hTERT RPE-1 cells reached the peak of area-normalized adhesion parameters earlier than HEK 293T cells, with their peak area-normalized adhesion force and area-normalized adhesion energy being substantially elevated relative to HEK 293T cells. HEK 293T cells presented stronger linear correlations among adhesion energy, maximum detachment distance and adhesion force compared with hTERT RPE-1 cells. For both cell lines, cell spreading area exhibited a weak correlation with adhesion force. Whereas the area-normalized adhesion parameters of HEK 293T cells remained relatively constant throughout the adhesion process, hTERT RPE-1 cells exhibited elevated values in the early phase, followed by a gradual decline. These results indicated distinct dynamic adhesion patterns between the two cell types, with hTERT RPE-1 cells exhibiting stronger adhesion strength and higher adhesion efficiency. ConclusionIn this study, fluidic force microscopy-based single-cell force spectroscopy was successfully applied to perform long-term in situ quantitative measurement of the adhesion dynamics in single adherent cells. The approach revealed divergent adhesion patterns between HEK 293T and hTERT RPE-1 cells, suggesting a close association between cell differentiation and adhesion behaviors. These findings provide quantitative mechanical evidence for further understanding the underlying mechanisms of cell adhesion.

Background: Mongolian traditional bone-setting is a non-surgical treatment method for fractures; however, there is a lack of evidencebased research evaluating its effectiveness, which forms the basis of our study. Aim: To study the therapeutic efficacy of traditional bonesetting treatment for surgical neck fractures of the humerus. Materials and Methods: From June 2023 to June 2025, 120 patients diagnosed with surgical neck fractures of the humerus and treated at the Inner Mongolia International Mongolian Hospital were randomly divided into a traditional bone-setting group (n=64) and a surgical treatment group (n=56). Shoulder and upper limb function were evaluated at 1, 3, and 6 months after treatment using the Constant–Murley score, Oxford Shoulder Score (OSS), and Visual Analog Scale (VAS) for pain. Result: A total of 120 patients with surgical neck fractures of the humerus were divided into two groups, with no significant differences in the baseline characteristics (p>0.05).In the traditional bone-setting group, pain (VAS) decreased more rapidly at 1–3 months (p<0.01), while activities of daily living and functional outcomes improved significantly at 3–6 months (p<0.01). At 6 months, shoulder function (Constant–Murley score) was significantly improved (p=0.049).In contrast, the surgical group showed faster recovery in joint range of motion and muscle strength in the early stage; however, by 6 months, the outcomes between the two groups were comparable. Conclusion In the treatment of surgical neck fractures of the humerus, no significant difference was observed between traditional bone-setting and surgical treatment in terms of shoulder range of motion and muscle strength recovery. However, the advantages of traditional bone-setting lie in its ability to relieve pain more rapidly, promote faster functional recovery, improve activities of daily living, and achieve better long-term outcomes than modern bone-setting. Therefore, traditional bone setting is a safe and effective treatment method that aligns with modern theories of bone healing and rehabilitation principles and holds significant value for wider clinical application and further research in the management of surgical neck fractures of the humerus.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that severely affects the health of the elderly, marked by its incurability, high prevalence, and extended latency period. The current approach to AD prevention and treatment emphasizes early detection and intervention, particularly during the pre-AD stage of mild cognitive impairment (MCI), which provides an optimal “window of opportunity” for intervention. Clinical detection methods for MCI, such as cerebrospinal fluid monitoring, genetic testing, and imaging diagnostics, are invasive and costly, limiting their broad clinical application. Speech, as a vital cognitive output, offers a new perspective and tool for computer-assisted analysis and screening of cognitive decline. This is because elderly individuals with cognitive decline exhibit distinct characteristics in semantic and audio information, such as reduced lexical richness, decreased speech coherence and conciseness, and declines in speech rate, voice rhythm, and hesitation rates. The objective presence of these semantic and audio characteristics lays the groundwork for computer-based screening of cognitive decline. Speech information is primarily sourced from databases or collected through tasks involving spontaneous speech, semantic fluency, and reading, followed by analysis using computer models. Spontaneous language tasks include dialogues/interviews, event descriptions, narrative recall, and picture descriptions. Semantic fluency tasks assess controlled retrieval of vocabulary items, requiring participants to extract information at the word level during lexical search. Reading tasks involve participants reading a passage aloud. Summarizing past research, the speech characteristics of the elderly can be divided into two major categories: semantic information and audio information. Semantic information focuses on the meaning of speech across different tasks, highlighting differences in vocabulary and text content in cognitive impairment. Overall, discourse pragmatic disorders in AD can be studied along three dimensions: cohesion, coherence, and conciseness. Cohesion mainly examines the use of vocabulary by participants, with a reduction in the use of nouns, pronouns, verbs, and adjectives in AD patients. Coherence assesses the ability of participants to maintain topics, with a decrease in the number of subordinate clauses in AD patients. Conciseness evaluates the information density of participants, with AD patients producing shorter texts with less information compared to normal elderly individuals. Audio information focuses on acoustic features that are difficult for the human ear to detect. There is a significant degradation in temporal parameters in the later stages of cognitive impairment; AD patients require more time to read the same paragraph, have longer vocalization times, and produce more pauses or silent parts in their spontaneous speech signals compared to normal individuals. Researchers have extracted audio and speech features, developing independent systems for each set of features, achieving an accuracy rate of 82% for both, which increases to 86% when both types of features are combined, demonstrating the advantage of integrating audio and speech information. Currently, deep learning and machine learning are the main methods used for information analysis. The overall diagnostic accuracy rate for AD exceeds 80%, and the diagnostic accuracy rate for MCI also exceeds 80%, indicating significant potential. Deep learning techniques require substantial data support, necessitating future expansion of database scale and continuous algorithm upgrades to transition from laboratory research to practical product implementation.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Blonanserin,a second-generation atypical antipsychotic agent,acts as an antagonist for dopamine D2,D3,and serotonin 5-HT2A receptors.Clinical studies have demonstrated that blonanserin is non-inferior to other antipsychotics,such as haloperidol and risperidone,in alleviating the symptoms of schizophrenia.Moreover,it exhib-its beneficial effects on cognitive symptoms and social functioning,with a favorable safety profile,making it one of the key treatment options for schizophrenia.With extensive clinical experience accumulated in China,this expert consensus aims to provide psychiatrists with updated and localized guidance on the optimal use of blonan-serin.Based on a systematic review of the latest evidence-particularly studies in Chinese population,this paper pres-ents the updated Chinese expert recommendations for the clinical use of blonanserin in 2024.

Objective:To investigate the differences in clinical outcomes of septic children with varying serum zinc levels,and to analyze the relationship between reduced serum zinc levels and organ dysfunction as well as 28-day mortality in septic children.Methods:This study conducted a retrospective analysis of clinical data from pediatric patients diagnosed with sepsis or septic shock in the Department of critical care medicine of the children's Hospital of Soochow University between January 2017 and December 2022.Clinical characteristics,organ dysfunction,and prognosis were compared between two groups:children with low serum zinc levels and those with normal zinc levels.Results:The serum zinc level of septic children within 24 hours of admission was 9.60(5.52,13.80)μmol/L,with 50.54%(94/186)of the children exhibiting low serum zinc levels(<10.07 μmol/L).Compared to the normal serum zinc group,the low serum zinc group had a significantly lower Pediatric Critical Illness Score(PCIS)[(78.71±9.35)vs.(85.12±8.51),P=0.005]and higher 28-day mortality(46.80%vs.14.13%,P<0.001).The low serum zinc group also had a higher proportion of invasive mechanical ventilation(64.89%vs.47.82%,P=0.019),renal replacement therapy(15.59%vs.3.26%,P=0.003),and use of vasoactive drugs(56.38%vs.30.43%,P<0.001).The rate of underlying conditions in the low serum zinc group was significantly higher than that in the normal serum zinc group(57.44%vs.36.95%,P=0.005).Additionally,the low serum zinc group had a higher incidence of disseminated intravascular coagulation(DIC),respiratory failure,acute kidney injury,shock,and multiple organ dysfunction syndrome(MODS)compared to the normal serum zinc group(P<0.05).Serum zinc levels had predictive value for 28-day mortality in septic children(AUC=0.813;95%CI:0.725～0.902;P<0.001).A serum zinc level of less than 6.950 μmol/L predicted the death of septic children with a sensitivity of 0.618 and a specificity of 0.902.Conclusion:Sepsis in children is commonly associated with low serum zinc levels,especially in those with underlying conditions such as hematologic and oncologic disorders.Sepsis patients hypozincemia with a higher incidence of DIC,respiratory failure,acute kidney injury,shock,and MODS.A serum zinc level below 6.95 μmol/L serves as a significant predictor of 28-day mortality in children with severe sepsis.

Human non-histone lysine crotonylation plays crucial roles in biological activities.However,traditional wet-lab experiments are time-consuming and labor-intensive,making computational prediction methods increasingly popular in recent years.Despite the biological importance of lysine crotonylation,there are relatively few studies on human non-histone proteins.In this study,we developed an ensemble deep learning predictor named iHNHC-RsFPN by constructing a Residual Pyramid Network(RsFPN).First,three feature extraction methods were employed to encode sequence samples.Next,weak classifi-ers based on RsFPN were individually trained for different feature types.Finally,these weak classifiers were integrated to build a robust final predictor.Independent test results demonstrated that iHNHC-RsF-PN achieved outstanding performance across four key metrics:sensitivity(Sn=0.8580),specificity(Sp=0.7463),accuracy(Acc=0.7798),and Matthews correlation coefficient(MCC=0.5586).Comparative experiments revealed that iHNHC-RsFPN significantly improved prediction accuracy for hu-man non-histone crotonylation sites over existing methods.Additionally,we established a user-friendly web server(http://www.lzzzlab.top/ihnc/)that provides straightforward prediction services without complex calculations,facilitating further research for experts in related fields.

Objective:This study aims to clarify the role of family and community health environments in improving the health status of elderly patients with chronic diseases,and provide recommendations and references for optimizing and enhancing chronic disease management capabilities.Methods:Based on data from the 2018 China Health and Retirement Longitudinal Study(CHARLS),this study analyzes a sample of 9,388 elderly patients with chronic diseases.A hierarchical linear model(HLM)is employed to examine the effects of family and community health environments on chronic disease management outcomes,as well as their variations across urban-rural settings and disease types.Results:The findings indicate that a supportive family health environment significantly improves both self-rated health(β=0.097,P<0.001)and disease control outcomes(β=0.033,P<0.05)among elderly patients with chronic diseases.In contrast,community health environments contribute positively only to self-rated health(β=0.062,P<0.001)but do not significantly affect disease control outcomes.Moreover,no moderating effect of community health environments was observed on the relationship between family health environments and either self-rated health or disease control.The effects of family and community health environment on self-rated health and control results were different between urban and rural areas,while the effects of family health environment on control results were different in disease types.Conclusion:The explanatory power of the family health environment on the health status of elderly patients with chronic diseases is higher than that of the community health environment.However,the two have not been effectively integrated.It is recommended to incorporate the health needs of elderly patients with chronic diseases into the optimization of healthy family construction and to establish a four-in-one comprehensive chronic disease management system involving"patients,families,communities,and primary healthcare institutions."new models for chronic disease management should be explored and innovated.