OBJECTIVE To provide reference for medical institutions to establish the record management mode and review rules of off-label use of 5-aminolevulinic acid （ALA） in photodynamic therapy based on the level of evidence. METHODS All ALA-containing outpatient prescriptions in the rational drug use system in our hospital from January 1， 2024 to December 31， 2025 were retrospectively collected. Based on the drug instructions， the current status of off-label use of ALA in photodynamic therapy was identified . The relevant studies in Micromedex， PubMed， CNKI， Wanfang Data and other databases were systematically searched as the relevant evidence-based evidence of ALA off-label use. According to the Off-label Drug Use Filing Standard of the hospital，the evidence-based evaluation method was used to evaluate the evidence-based evidence of ALA off-label use and carry out hierarchical management. RESULTS A total of 1 803 effective prescriptions were included， of which 676 （37.49%） were off-label use， distributed in the dermatology department （564 prescriptions，83.43%） and the plastic surgery department （112 prescriptions，16.57%）. All 676 prescriptions were off-indications medication， involving ten types of skin diseases， primarily including moderate to severe acne （39.94%）， skin warts （25.44%）， Bowen’s disease （11.98%）， and others. According to evidence-based evidence，off-label uses such as moderate to severe acne， actinic keratosis， and Bowen’s disease were managed according to the evidence categoryⅠ orⅡ.The uses of extramammary Paget’s disease and rosacea were managed according to the evidence category Ⅲ.The uses of lichen sclerosus and keloids were managed according to the evidence category Ⅳ.The results of evidence-based evaluation showed that 92.01% of off-label use in our hospital had high-level evidence-based support （ evidence category was gradeⅠ-Ⅱ）. CONCLUSIONS Off-label uses supported by high-level evidence， such as moderate to severe acne， skin warts， and Bowen’s disease， can be managed under filing category Ⅰ or Ⅱ. For the use of lichen sclerosus and keloids， evidence-based evidence is insufficient and should be strictly restricted.The vast majority of ALA off-label use in our hospital has sufficient evidence-based basis.

AIM:To observe the morphological and structural changes of foveal cone photoreceptors in patients with age-related macular degeneration(ARMD)using adaptive optics scanning laser ophthalmoscopy(AOSLO)and to evaluate its application value in ARMD.METHODS:This was a retrospective cross-sectional study. Patients with ARMD who visited the Department of Ophthalmology, Army Medical Center of PLA, Army Medical University, and underwent AOSLO examination between September 2025 and October 2025 were enrolled as the experimental group(ARMD group). Age-matched individuals who underwent AOSLO examination during the same period and had either age-related cataract or pseudophakia with a normal macular region were selected as the control group(CON group). The AOSLO device was used to image a 2.4°×2.4° area of the fovea, and parameters including parafoveal cone photoreceptor density(PCPD), average inter-cell spacing, cell dispersion, and cell regularity were analyzed.RESULTS:A total of 53 participants(66 eyes)were included, comprising 24 patients(33 eyes)in the ARMD group [comprising 6 participants(6 eyes)in the intermediate ARMD group and 22 participants(27 eyes)in the late ARMD group(4 participants had one eye in the intermediate group and the other in the late ARMD group)], and 29 participants(33 eyes)in the CON group. The ARMD group included 13 males and 11 females, with a mean age of 69.36±9.79 y. The control group included 17 males and 12 females, with a mean age of 64.64±10.31 y. Compared to the CON group, the ARMD group exhibited significantly lower PCPD(31635±4887 vs 38524±3578 cells/mm2, P<0.01)and cell regularity(95.16%±0.75% vs 96.07%±0.67%, P<0.01), along with significantly greater average inter-cell spacing(4.43±0.26 vs 4.22±0.23 μm, P<0.01)and cell dispersion(20.23%±2.72% vs 16.47%±1.85%, P<0.01). Subgroup analysis within the ARMD group revealed that PCPD was significantly lower in the late ARMD subgroup(30831±4826 cells/mm2)compared to the intermediate ARMD subgroup(35254±3534 cells/mm2, P<0.05).CONCLUSION:Photoreceptor pathology in ARMD patients, as assessed by AOSLO, is characterized by decreased PCPD and cell regularity, as well as increased inter-cell spacing and dispersion. These structural alterations are closely associated with photoreceptor cell lesions. AOSLO, as a non-invasive and quantitative imaging modality, demonstrates promising application prospects in the clinical diagnosis of ARMD.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Non-alcoholic fatty liver disease (NAFLD) is a metabolic disease characterized by abnormal deposition of lipid in hepatocytes. If not intervened in time, NAFLD may develop into liver fibrosis or liver cancer, and ultimately threatening life. NAFLD has complicated etiology and pathogenesis, and there are no effective therapeutic means and specific drugs. Currently, insulin sensitizers, lipid-lowering agents and hepatoprotective agents are often used for clinical intervention, but these drugs have obvious side effects, and their effectiveness and safety need to be further confirmed. Adenosine monophosphate (AMP)-activated protein kinase (AMPK) plays a central role in maintaining energy homeostasis. Activated AMPK can enhance lipid degradation, alleviate insulin resistance (IR), suppress oxidative stress and inflammatory response, and regulate autophagy, thereby alleviating NAFLD. Natural herbal medicines have received extensive attention recently because of their regulatory effects on AMPK and low side effects. In this article, we reviewed the biologically active natural herbal medicines (such as natural herbal medicine formulas, extracts, polysaccharides, and monomers) that reported in recent years to treat NAFLD via regulating AMPK, which can serve as a foundation for subsequent development of candidate drugs for NAFLD.

Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.

Peroxisome proliferator activated receptor-α (PPAR-α) is significantly expressed in various tissues such as the liver, kidney, myocardium, and skeletal muscle, which plays a central role in the development of various diseases by regulating key physiological processes such as energy homeostasis, redox balance, inflammatory response, and ferroptosis. As an important metabolic and excretory organ of the body, renal dysfunction can lead to water and electrolyte imbalance, toxin accumulation, and multiple system complications. The causes of kidney injury are complex and diverse, including acute injury factors (such as ischemia/reperfusion, nephrotoxic drugs, septic shock, and immune glomerulopathy), as well as chronic progressive causes [such as metabolic disease-related nephropathy, hypertensive nephropathy (HN)], and risk factors such as alcohol abuse, obesity, and aging. This review briefly describes the structure, function, and activity regulation mechanism of PPAR-α, systematically elucidates the molecular regulatory network of PPAR-α in the pathological process of kidney injury including acute kidney injury (AKI) such as renal ischemia/reperfusion injury (IRI), drug-induced AKI, sepsis-associated acute kidney injury (SA-AKI), glomerulonephritis, chronic kidney disease (CKD) such as diabetic nephropathy (DN), HN, and other kidney injury, and summarizes the mechanisms related to PPAR-α regulation of kidney injury, including regulation of metabolism, antioxidation, anti-inflammation, anti-fibrosis, and anti-ferroptosis. This review also evaluates PPAR-α's medical value as a novel therapeutic target, and aims to provide theoretical basis for the development of kidney protection strategies based on PPAR-α targeted intervention.
Humans ; PPAR alpha/metabolism* ; Acute Kidney Injury/therapy* ; Animals ; Kidney/metabolism*

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

PURPOSE: To investigate the regulatory role of nerve injury-induced protein 1 (NINJ1) in the anti-inflammatory function of human umbilical cord mesenchymal stem cells (hUC-MSCs) co-stimulated by interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). METHODS: hUC-MSCs were expanded in vitro using standard protocols, with stem cell characteristics confirmed by flow cytometry and multilineage differentiation assays. The immunomodulatory properties and cellular activity of cytokine-co-pretreated hUC-MSCs were systematically evaluated via quantitative reverse transcription RT-qPCR, lymphocyte proliferation suppression assays, and Cell Counting Kit-8 viability tests. Transcriptome sequencing, Western blotting and small interfering RNA interference were integrated to analyze the regulatory mechanisms of NINJ1 expression. Functional roles of NINJ1 in pretreated hUC-MSCs were elucidated through gene silencing combined with lactate dehydrogenase release assays, Annexin V/Propidium Iodide apoptosis analysis, macrophage co-culture models, and cytokine Enzyme-Linked Immunosorbent Assay. Therapeutic efficacy was validated in a cecal ligation and puncture-induced septic mouse model: 80 mice were randomly allocated into 4 experimental groups (n=20/group): sham group (laparotomy without cecal ligation); phosphate-buffered saline-treated group (cecal ligation and puncture (CLP) + 0.1 mL phosphate-buffered saline); hUC-MSCs (small interfering RNA (siRNA)-interferon-gamma and tumor necrosis factor-alpha co-stimulation (IT))-treated group (CLP + hUC-MSCs transfected with scrambled siRNA); and hUC-MSCs (siNINJ1-IT)-treated group (CLP + hUC-MSCs with NINJ1-targeting siRNA). RESULTS: hUC-MSCs demonstrated compliance with International Society for Cellular Therapy criteria, confirming their stem cell identity. IFN-γ/TNF-α co-pretreatment enhanced the immunosuppressive capacity of hUC-MSCs, accompanied by the reduction of cellular viability, while concurrently upregulating pro-inflammatory cytokines such as interleukin-6 and interleukin-1β. This co-stimulation significantly elevated NINJ1 expression in hUC-MSCs, whereas genetic silencing of NINJ1 effectively suppressed pro-inflammatory cytokine production and attenuated damage-associated molecular patterns release through inhibition of programmed plasma membrane rupture. Furthermore, the NINJ1 interference potentiated the ability of cytokine-pretreated hUC-MSCs to suppress LPS-induced pro-inflammatory responses in RAW264.7 macrophages. In cecal ligation and puncture-induced sepsis model, NINJ1-silenced hUC-MSCs exhibited enhanced therapeutic efficacy, manifested by reduced systemic inflammation and multi-organ damage. CONCLUSION Our findings shed new light on the immunomodulatory functions of cytokine-primed MSCs, offering groundbreaking insights for developing MSC-based therapies against inflammatory diseases via interfering the expression of NINJ1.
Mesenchymal Stem Cells/drug effects* ; Animals ; Interferon-gamma/pharmacology* ; Tumor Necrosis Factor-alpha/pharmacology* ; Humans ; Mice ; Umbilical Cord/cytology* ; Cells, Cultured ; Apoptosis ; Male

PURPOSE: The rate of complications among patients undergoing surgery has increased due to infection with SARS-CoV-2 and other variants of concern. However, Omicron has shown decreased pathogenicity, raising questions about the risk of postoperative complications among patients who are infected with this variant. This study aimed to investigate complications and related factors among patients with recent Omicron infection prior to undergoing orthopedic surgery. METHODS: A historical control study was conducted. Data were collected from all patients who underwent surgery during 2 distinct periods: (1) between Dec 12, 2022 and Jan 31, 2023 (COVID-19 positive group), (2) between Dec 12, 2021 and Jan 31, 2022 (COVID-19 negative control group). The patients were at least 18 years old. Patients who received conservative treatment after admission or had high-risk diseases or special circumstances (use of anticoagulants before surgery) were excluded from the study. The study outcomes were the total complication rate and related factors. Binary logistic regression analysis was used to identify related factors, and odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the impact of COVID-19 infection on complications. RESULTS: In the analysis, a total of 847 patients who underwent surgery were included, with 275 of these patients testing positive for COVID-19 and 572 testing negative. The COVID-19-positive group had a significantly higher rate of total complications (11.27%) than the control group (4.90%, p < 0.001). After adjusting for relevant factors, the OR was 3.08 (95% CI: 1.45-6.53). Patients who were diagnosed with COVID-19 at 3-4 weeks (OR = 0.20 (95% CI: 0.06-0.59), p = 0.005), 5-6 weeks (OR = 0.16 (95% CI: 0.04-0.59), p = 0.010), or ≥7 weeks (OR = 0.26 (95% CI: 0.06-1.02), p = 0.069) prior to surgery had a lower risk of complications than those who were diagnosed at 0-2 weeks prior to surgery. Seven factors (age, indications for surgery, time of operation, time of COVID-19 diagnosis prior to surgery, C-reactive protein levels, alanine transaminase levels, and aspartate aminotransferase levels) were found to be associated with complications; thus, these factors were used to create a nomogram. CONCLUSION Omicron continues to be a significant factor in the incidence of postoperative complications among patients undergoing orthopedic surgery. By identifying the factors associated with these complications, we can determine the optimal surgical timing, provide more accurate prognostic information, and offer appropriate consultation for orthopedic surgery patients who have been infected with Omicron.
Humans ; COVID-19/complications* ; Male ; Female ; Middle Aged ; Postoperative Complications/epidemiology* ; SARS-CoV-2 ; Orthopedic Procedures/adverse effects* ; Aged ; Nomograms ; Adult ; Retrospective Studies ; Risk Factors