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 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.

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.

Intraoperative cell salvage (IOCS) has been widely applied as an important blood conservation measure in surgical operations. However, there is currently a lack of clinical practice guidelines for the implementation of IOCS in patients with malignant tumors. This report aims to provide clinicians with recommendations on the use of IOCS in patients with malignant tumors based on the review and assessment of the existed evidence. Data were derived from databases such as PubMed, Embase, the Cochrane Library and Wanfang. The guideline development team formulated recommendations based on the quality of evidence, balance of benefits and harms, patient preferences, and health economic assessments. This study constructed seven major clinical questions. The main conclusions of this guideline are as follows: 1) Compared with no perioperative allogeneic blood transfusion (NPABT), perioperative allogeneic blood transfusion (PABT) leads to a more unfavorable prognosis in cancer patients (Recommended); 2) Compared with the transfusion of allogeneic blood or no transfusion, IOCS does not lead to a more unfavorable prognosis in cancer patients (Recommended); 3) The implementation of IOCS in cancer patients is economically feasible (Recommended); 4) Leukocyte depletion filters (LDF) should be used when implementing IOCS in cancer patients (Strongly Recommended); 5) Irradiation treatment of autologous blood to be reinfused can be used when implementing IOCS in cancer patients (Recommended); 6) A careful assessment of the condition of cancer patients (meeting indications and excluding contraindications) should be conducted before implementing IOCS (Strongly Recommended); 7) Informed consent from cancer patients should be obtained when implementing IOCS, with a thorough pre-assessment of the patient's condition and the likelihood of blood loss, adherence to standardized internally audited management procedures, meeting corresponding conditions, and obtaining corresponding qualifications (Recommended). In brief, current evidence indicates that IOCS can be implemented for some malignant tumor patients who need allogeneic blood transfusion after physician full evaluation, and LDF or irradiation should be used during the implementation process.

Background/Aims: The association between caffeine intake and constipation remains inconclusive. This study aims to investigate whether caffeine intake is associated with constipation. Methods: This cross-sectional study included 13 941 adults from the 2005-2010 National Health and Nutrition Examination Survey. The weighted logistic regression analyses were exerted to evaluate the association between caffeine intake and constipation. Besides, stratified analyses and interaction tests were conducted to determine the potential modifying factors. Results: After adjusting for confounders, increased caffeine intake by 100 mg was not associated with constipation, as defined by stool frequency (OR, 1.01; 95% CI, 0.94-1.10) or stool consistency (OR, 1.01; 95% CI, 0.98-1.05). Subgroup analyses showed that cholesterol intake modified the relationship between increased caffeine by 100 mg and stool frequency-defined constipation (P for interaction = 0.037). Each 100 mg increase in caffeine intake was associated with a 20% decreased risk of constipation defined by stool frequency in participants who consumed high cholesterol (OR, 0.80; 95% CI, 0.64-1.00), but no association in the other 2 cholesterol level groups. Furthermore, the association between caffeine intake and stool consistency-defined constipation was not found in different cholesterol groups. Conclusions Caffeine consumption is not associated with stool frequency or consistency-defined constipation. Nevertheless, increased caffeine intake may decrease the risk of constipation (defined by stool frequency) among participants in the high-cholesterol intake group.

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*

Background/Aims: The association between caffeine intake and constipation remains inconclusive. This study aims to investigate whether caffeine intake is associated with constipation. Methods: This cross-sectional study included 13 941 adults from the 2005-2010 National Health and Nutrition Examination Survey. The weighted logistic regression analyses were exerted to evaluate the association between caffeine intake and constipation. Besides, stratified analyses and interaction tests were conducted to determine the potential modifying factors. Results: After adjusting for confounders, increased caffeine intake by 100 mg was not associated with constipation, as defined by stool frequency (OR, 1.01; 95% CI, 0.94-1.10) or stool consistency (OR, 1.01; 95% CI, 0.98-1.05). Subgroup analyses showed that cholesterol intake modified the relationship between increased caffeine by 100 mg and stool frequency-defined constipation (P for interaction = 0.037). Each 100 mg increase in caffeine intake was associated with a 20% decreased risk of constipation defined by stool frequency in participants who consumed high cholesterol (OR, 0.80; 95% CI, 0.64-1.00), but no association in the other 2 cholesterol level groups. Furthermore, the association between caffeine intake and stool consistency-defined constipation was not found in different cholesterol groups. Conclusions Caffeine consumption is not associated with stool frequency or consistency-defined constipation. Nevertheless, increased caffeine intake may decrease the risk of constipation (defined by stool frequency) among participants in the high-cholesterol intake group.

Background/Aims: The association between caffeine intake and constipation remains inconclusive. This study aims to investigate whether caffeine intake is associated with constipation. Methods: This cross-sectional study included 13 941 adults from the 2005-2010 National Health and Nutrition Examination Survey. The weighted logistic regression analyses were exerted to evaluate the association between caffeine intake and constipation. Besides, stratified analyses and interaction tests were conducted to determine the potential modifying factors. Results: After adjusting for confounders, increased caffeine intake by 100 mg was not associated with constipation, as defined by stool frequency (OR, 1.01; 95% CI, 0.94-1.10) or stool consistency (OR, 1.01; 95% CI, 0.98-1.05). Subgroup analyses showed that cholesterol intake modified the relationship between increased caffeine by 100 mg and stool frequency-defined constipation (P for interaction = 0.037). Each 100 mg increase in caffeine intake was associated with a 20% decreased risk of constipation defined by stool frequency in participants who consumed high cholesterol (OR, 0.80; 95% CI, 0.64-1.00), but no association in the other 2 cholesterol level groups. Furthermore, the association between caffeine intake and stool consistency-defined constipation was not found in different cholesterol groups. Conclusions Caffeine consumption is not associated with stool frequency or consistency-defined constipation. Nevertheless, increased caffeine intake may decrease the risk of constipation (defined by stool frequency) among participants in the high-cholesterol intake group.