AIM: To investigate the correlation of the expression of stromal cell-derived factor-1(SDF-1)and angiopoietin like protein 4(ANGPTL4)in serum with the severity of disease in patients with diabetic macular edema(DME).METHODS: From April 2020 to August 2023, 193 patients with diabetic retinopathy who were admitted to our hospital were prospectively separated into DME group(128 cases)(56 cases in mild group, 44 cases in moderate group, 28 cases in severe group)and non DME group(65 cases)according to whether the patients had macular edema and the severity of disease. Enzyme-linked immunosorbent assay(ELISA)was applied to determine the levels of ANGPTL4 and SDF-1 in serum. Multivariate Logistic regression was applied to analyze the factors that affected the severity of DME; receiver operating characteristic(ROC)curve was applied to analyze the diagnostic value of ANGPTL4 and SDF-1 levels in serum of DME patients for the severity of DME.RESULTS: The levels of ANGPTL4 and SDF-1 in serum of the DME group were obviously higher than those of the non DME group(P<0.01); the expression levels of ANGPTL4 and SDF-1 in serum of the mild, moderate, and severe groups increased obviously in sequence(P<0.05); multivariate Logistic regression analysis showed that the levels of ANGPTL4 and SDF-1 in serum were risk factors affecting the severity of DME(P<0.01); The area under the curve(AUC)of serum SDF-1 in the diagnosis of DME severity was 0.772(95%CI: 0.690-0.842), and the AUC of ANGPTL4 in the diagnosis of DME severity was 0.801(95%CI: 0.722-0.867). The AUC of ANGPTL4 combined with SDF-1 in the diagnosis of DME was 0.884(95%CI: 0.816-0.934), the sensitivity was 87.50%, and the specificity was 85.71%, which were significantly higher than ANGPTL4 or SDF-1 alone(Z=2.658, 2.469, all P<0.05).CONCLUSION: The levels of ANGPTL4 and SDF-1 in serum of DME patients are significantly increased, and their levels increase with the severity of the disease. They can be used as auxiliary indicators for diagnosing the severity of DME disease, and the combined diagnosis has a better effect.

AIM: To investigate the correlation of the expression of stromal cell-derived factor-1(SDF-1)and angiopoietin like protein 4(ANGPTL4)in serum with the severity of disease in patients with diabetic macular edema(DME).METHODS: From April 2020 to August 2023, 193 patients with diabetic retinopathy who were admitted to our hospital were prospectively separated into DME group(128 cases)(56 cases in mild group, 44 cases in moderate group, 28 cases in severe group)and non DME group(65 cases)according to whether the patients had macular edema and the severity of disease. Enzyme-linked immunosorbent assay(ELISA)was applied to determine the levels of ANGPTL4 and SDF-1 in serum. Multivariate Logistic regression was applied to analyze the factors that affected the severity of DME; receiver operating characteristic(ROC)curve was applied to analyze the diagnostic value of ANGPTL4 and SDF-1 levels in serum of DME patients for the severity of DME.RESULTS: The levels of ANGPTL4 and SDF-1 in serum of the DME group were obviously higher than those of the non DME group(P<0.01); the expression levels of ANGPTL4 and SDF-1 in serum of the mild, moderate, and severe groups increased obviously in sequence(P<0.05); multivariate Logistic regression analysis showed that the levels of ANGPTL4 and SDF-1 in serum were risk factors affecting the severity of DME(P<0.01); The area under the curve(AUC)of serum SDF-1 in the diagnosis of DME severity was 0.772(95%CI: 0.690-0.842), and the AUC of ANGPTL4 in the diagnosis of DME severity was 0.801(95%CI: 0.722-0.867). The AUC of ANGPTL4 combined with SDF-1 in the diagnosis of DME was 0.884(95%CI: 0.816-0.934), the sensitivity was 87.50%, and the specificity was 85.71%, which were significantly higher than ANGPTL4 or SDF-1 alone(Z=2.658, 2.469, all P<0.05).CONCLUSION: The levels of ANGPTL4 and SDF-1 in serum of DME patients are significantly increased, and their levels increase with the severity of the disease. They can be used as auxiliary indicators for diagnosing the severity of DME disease, and the combined diagnosis has a better effect.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.

In recent years， the number of special clinical cases related to assisted reproductive technology （ART） for pregnancy has been on the rise annually. For example， gamete donation， assisted reproduction for couples of advanced maternal ages， and the ownership and disposition of embryos when one or both partners pass away， all involve the health of offspring and conflicts of interest in the implementation of ART. Although China’s ethical principles for ART include the protection of the offspring， the detailed rules are vague and urgently need to be supplemented and improved. In addition， the clinical application of ART lacks legislative collaborative governance. The corresponding administrative departments， practitioners， and infertile couples should work together. Starting from the perspective of focusing on the well-being of offspring born from the clinical application of ART， while conducting clinical research to address technical challenges， efforts should be made to overcome ethical dilemmas in the application of technology， comprehensively consider the well-being of offspring born from assisted reproductive treatments and help them seek the best care to promote their healthy growth.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.