Acute kidney injury (AKI) is a common complication of sepsis. Immune-inflammatory markers are commonly used to assess the prognosis of these patients. However, studies evaluating microRNAs (miR) in this context are scarce, indicating a need for further clinical investigation.

To investigate the expression of serum amyloid A (SAA), interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and miR in pediatric patients with sepsis-induced AKI and analyze their prognostic assessment value.

This study included 100 pediatric patients with sepsis-induced AKI admitted to the First People's Hospital of Pingdingshan from March 2020 to March 2023 as the observation group, and 80 pediatric patients with sepsis alone as the control group. General patient data were collected, and serum levels of SAA, IL-6, and TNF-α were measured using enzyme-linked immunosorbent assay (ELISA). The relative expression of miR-21-3p, miR-182-5p, and miR-128-3p was quantified using real-time quantitative PCR. The Sequential Organ Failure Assessment (SOFA) score and the Acute Physiology and Chronic Health EvaluationⅡ (APACHE Ⅱ) score were compared between the groups. Pearson correlation analysis was used to evaluate the relationship between the levels of serum SAA, IL-6, TNF-α, and miRs and the SOFA and APACHEⅡ scores. Receiver operating characteristic (ROC) curves were plotted to explore the predictive value of these markers for mortality in pediatric patients with sepsis-induced AKI and to calculate the area under the ROC curve (AUC) .

The observation group showed significantly higher SOFA scores, APACHE Ⅱ scores, and levels of serum SAA, IL-6, TNF-α, miR-21-3p, miR-182-5p, and miR-128-3p compared to the control group (P<0.05). After 28 days of hospitalization, 74 patients in the observation group survived, while 26 died. Surviving patients had lower levels of serum SAA, IL-6, TNF-α, miR-21-3p, miR-182-5p, and miR-128-3p compared to those who died (P<0.05). Levels of serum SAA, IL-6, TNF-α, miR-21-3p, miR-182-5p, and miR-128-3p were positively correlated with SOFA and APACHEⅡ scores (P<0.05). ROC curve results showed a combined predictive AUC of 0.926 (95%CI=0.856-0.969, P<0.05) .

The serum levels of SAA, IL-6, TNF-α, miR-21-3p, miR-182-5p, miR-128-3p are abnormally high in children with sepsis complicated with AKI. Clinical detection of these indicators has a high value and early warning effect on the prognosis of children.

Salidroside has been shown to protect diabetic kidney disease (DKD) rats, however, whether it is equally effective in a hypoxic environment and the specific mechanism of action remain unclear.

To observe the effects of salidroside on biochemical parameters, renal tissue pathological lesion, and the expression of cell pyroptosis-related proteins in a rat model of DKD under hypoxia, and explore its mechanisms of action.

From March 2022 to March 2023, forty 6-week-old SPF-grade SD male rats were used, with eight randomly selected as the control group, the remaining were modeled. Twenty-four DKD model rats were randomly divided into three groups of the model group, salidroside group, and salidroside+nod-like receptor protein 3 (NLRP3) activator group for intervention, with 8 in each group. After the intervention, blood was collected from the abdominal aorta for biochemical parameter testing, hematoxylin-eosin (HE) staining, and transmission electron microscopy were used to observe renal pathological changes. Enzyme-linked immunosorbent assay (ELISA) was used to detect serum levels of interleukin (IL) 1β and IL-18. Western blotting was used to measure the expression levels of Caspase-1, Gasdermin D (GSDMD), NLRP3, and transforming growth factor β1 (TGF-β1) in renal tissue.

The body weight of the rats after modeling was significantly lower than that of the control group (P<0.05). Compared to the control group, the levels of triglyceride (TG), total cholesterol (TC), fasting blood glucose (FBG), urinary microalbumin (UMA), blood urea nitrogen (BUN), and serum creatinine (Scr) were significantly higher in the model group (P<0.05). Compared to the model group, the BUN, UMA, and Scr levels were significantly lower in the salidroside group (P<0.05). Compared to the salidroside group, the UMA, BUN, and Scr levels were significantly higher in the salidroside+NLRP3 activator group (P<0.05). HE staining and transmission electron microscopy revealed that renal tissue pathological changes in the salidroside group were significantly reduced than the model group, and aggravated in the salidroside+NLRP3 activator group. Compared to the control group, serum IL-1β and IL-18 levels were significantly higher in the model group (P<0.05) ; these levels were significantly lower in the salidroside group compared to the model group (P<0.05), and higher in the salidroside+NLRP3 activator group compared to the salidroside group (P<0.05). Compared to the control group, the expression of Caspase-1, GSDMD, NLRP3, and TGF-β1 proteins was significantly higher in the model group (P<0.05) ; it was significantly lower in the salidroside group compared to the model group (P<0.05), and higher in the salidroside+NLRP3 activator group compared to the salidroside group (P<0.05) .

Salidroside exerted therapeutic effects on DKD rats in a hypoxic environment without reducing blood glucose and lipid levels, this effect may be related to the inhibition of NLRP3, affecting the NLRP3/IL-1β/TGF-β1 signaling pathway, ultimately improving podocyte pyroptosis injury.

Previous studies on renin-angiotensin-aldosterone system (RAAS) mainly focus on renin, prorenin and prorenin receptor (PRR), which play an important role in cardiovascular, renal and other diseases. The physiological function of PRR has been widely studied. Soluble prorenin receptor (sPRR) is generated by cleavage of extracellular components of PRR by protease and secreted into extracellular space, therefore the change of sPRR level may reflect the change of RAAS in tissue. However, there are limited clinical researchs on sPRR. In recent years, increasing evidences show that sPRR has important biological functions in various pathophysiological processes. This article summarized the recent advances in sPRR in cardiovascular system, kidney diseases, respiratory diseases, endocrine and metabolic diseases, pregnancy complications and cancers, and considered that sPRR may be a new biomarker of multiple diseases, and has potential therapeutic effects on metabolic diseases.

Subclinical Cushing's syndrome (SCS) is a common subtype of adrenal incidentaloma. There are few reports on the correlation between hyperglycemia and hypercortisone secretion and its postoperative change in SCS patients.

To assess the pre- and post-surgical prevalence of hyperglycemia in patients with SCS secondary to adrenal incidentaloma.

The data of 202 patients who consulted in respiratory department of endocrinology, renhe hospital affiliated to shanghai university (shanghai baoshan district renhe hospital). Participants included 36 SCS patients, 41 patients with Cushing's syndrome due to adrenal tumor (CSA), 47 with nonfunctional adrenal tumor (NAT), and 53 controls. OGTT was performed in all of them, and based on the results, HOMA-IR, the area under the curve of blood glucose (AUCGlu) and insulin (AUCIns) were calculated, and plasma cortisol and urinary free cortisol and plasma ACTH were measured, then the values of the parameters were compared between controls and patients. Surgical treatment was given to SCS and CSA patients. The association of hormone and glucose metabolism parameters was assessed using Pearson correlation analysis.

The prevalence of hyperglycemia in SCS, CSA and NAT patients before surgery and in controls was 41.7%, 51.2%, 25.5%, and 24.5%, respectively. The HbA_1c, 2-hour post-load insulin (2 hPIN), AUCGlu and AUCIns in SCS patients were higher than those of controls (P<0.05). CSA patients had higher fasting insulin, AUCIns and HOMA-IR than SCS patients, NAT patients and controls (P<0.05). CSA patients had higher HbA_1c, fasting plasma glucose (FPG), 2-hour post-load plasma glucose (2 hPPG), 2 hPIN and AUCGlu than NAT patients and controls (P<0.05). After controlling for sex and age, in SCS patients, HbA_1c was positively associated with cortisol measured at 8: 00 and 16: 00 on the day after admission, and 24-hour urinary free cortisol (r=0.68, 0.657, 0.522, P<0.05), and so was 2 hPPG (r=0.569, 0.544, 0.369, P<0.05) ; FPG was positively associated with cortisol measured at 8: 00 on the day after admission (r=0.434, P<0.05) ; AUCGlu was positively associated with cortisol measured at 8: 00 and 16: 00 on the day after admission (r=0.397, 0.409, P<0.05). In CSA group, HbA_1c was positively associated with cortisol measured at 8: 00 on the day after admission (r=0.748, P<0.05), and so was FPG, 2 hPPG, AUCGlu, and 2 hPIN (r=0.631, 0.669, 0.602, 0.319, P<0.05). HbA_1c was also positively associated with cortisol measured at 16: 00 on the day after admission (r=0.674, P<0.05), and so was FPG, 2 hPPG, AUCGlu, (r=0.655, 0.640, 0.624, P<0.05). Plasma cortisol and 24-hour urinary free cortisol decreased in SCS and CSA patients after surgery (P<0.05). 2 hPIN and AUCIns decreased in SCS patients after surgery (P<0.05). FIN, 2 hPIN, AUCGlu, AUCIns and HOMA-IR decreased in CSA patients after surgery (P<0.05). The postsurgical prevalence of hyperglycemia SCS and CSA patients was 33.3% and 39.0%, respectively.

The high prevalence of hyperglycemia may be related to high secretion of glucocorticoid in SCS patients, and the hyperglycemic condition was improved after surgical treatment.

Early diagnosis of acute kidney injury (AKI) in neonates is difficult with a high mortality rate. However, there is currently a lack of research on severe neonatal asphyxia complicated with AKI.

To investigate the risk factors and short-term prognosis of neonatal asphyxia complicated with AKI, and analyze the predictive value of related factors, so as to take measures to reduce the occurrence of AKI and improve the success rate of resuscitation of the neonates.

A total of 172 neonates with severe asphyxia who were hospitalized in the Neonatal Intensive Care Unit of the First Affiliated Hospital of Bengbu Medical College from January 2016 to January 2023 were included as the study subjects and divided into AKI group (n=43) and non-AKI group (n=129) according to whether the neonates were complicated with AKI. Clinical data and laboratory results were collected, and the short-term prognosis (survival or death during hospitalization) of the children with AKI was recorded. Multivariate Logistic regression analysis was used to explore the influencing factors of severe neonatal asphyxia complicated with AKI, and receiver operating characteristics (ROC) curve was used to explore the predictive value of related indicators for severe neonatal asphyxia complicated with AKI.

Gestational age, birth weight, 5-min Apgar score and platelet count in AKI group were lower than those in non-AKI group, and the proportions of coma, invasive mechanical ventilation and combined respiratory failure, cystatin C (Cys C) were higher than those in non-AKI group, with statistically significant difference (P<0.05). Multivariate Logistic regression analysis showed that 5-min Apgar score (OR=1.553, 95%CI=1.193-2.021, P=0.001), invasive mechanical ventilation (OR=2.965, 95%CI=1.021-8.611, P=0.046) and blood Cys C value (OR=0.231, 95%CI=0.109-0.487, P<0.001) were the influential factors for severe neonatal asphyxia complicated with AKI. ROC curve analysis showed that the AUC of blood Cys C for predicting AKI was 0.777 (95%CI=0.701-0.854, P<0.05), and the AUC of 5-min Apgar score for predicting AKI was 0.792 (95%CI=0.715-0.869, P<0.05). The hospitalized mortality was 51.2% (22/43) in AKI group and 21.7% (28/129) in non-AKI group, and the mortality in AKI group was higher than that in non-AKI group, the difference was statistically significant (χ²=13.572, P<0.001) .

Low 5-min Apgar score, invasive mechanical ventilation, and high postnatal blood Cys C can increase the risk of AKI in neonates with severe asphyxia. Postnatal blood Cys C and 5-min Apgar Score are reliable predictor of neonatal asphyxia complicated with AKI.

Chronic kidney disease-mineral and bone disorder (CKD-MBD) has a direct impact on patients' quality of life, hospitalization rates and fracture risk. In recent years, osteoblasts and osteoclasts have become central to the pathophysiology of CKD-MBD. Osteoblasts interact with other organs by synthesizing fibroblast growth factor-23 (FGF-23) and sclerostin (SOST), making the skeleton an endocrine organ. Therefore, dysregulation of osteoblast differentiation is an important early event in the pathogenesis of CKD. In this paper, we systematically discuss the metabolic pathways of osteoblasts and the mechanisms related to the altered metabolic reprogramming of osteoblasts in the early CKD-MBD pathology. This paper shows that abnormalities in signaling pathways and metabolites such as Wnt/β-catenin, FGF-23, uremic toxins, metabolic acidosis, can alter the metabolic activity of osteoblasts, causing impaired maturation of the osteogenic spectrum, which in turn affects bone remodeling, which will provide a new way of thinking for explaining the pathological changes in renal bone disease and developing clinical treatment options.

Aldosterone-producing adenoma (APA) is a common type of primary aldosteronism. For those with unilateral adrenocortical adenoma, although expert consensus recommends plasma aldosterone-to-renin ratio (ARR) as a screening indicator for APA, the range of ARR cut-off values varies widely due to the lack of unified detection method and diagnostic process. Therefore, there is a clinical need for a reliable and rapid predictive model to assist in identifying APA.

To explore the correlation between glomerular filtration rate (GFR) and APA, construct and validate the nomogram prediction model of APA.

A total of 493 patients with with pathologic results of unilateral adrenal mass who underwent surgical treatment after evaluation of adrenal endocrine hormones in the first affiliated hospital of Shihezi University from 2012 to 2022 were collected, 155 patients were ultimately included in the APA group and 113 patients in nonfunctioning adrenal adenoma combined with essential hypertension group according to the diagnostic criteria of APA and nonfunctioning adrenal adenoma. The patients' clinical data and biochemical data were collected. The patients were grouped according to GFR quartiles, and the correlation between GFR and APA was analyzed. The risk factors for APA were screened by multivariate Logistic regression analysis and a nomogram prediction model was constructed. Receiver operating characteristic (ROC) curve was used to analyze the discrimination of the prediction model, a consistency index (C-index) was used to evaluate the predictive accuracy of the model, Hosmer Lemeshow test was used to verify the fit of model, and the diagnostic efficacy of the model was evaluated using decision curve and clinical benefit curve.

The patients were grouped according to GFR quartiles (Q1 to Q4 groups), Q1 group: ≥107.4 mL·min^-1· (1.73 m²) ^-1 (n=67), Q2 group: 99.7-107.3 mL·min^-1· (1.73 m²) ^-1 (n=67), Q3 group: 88.6-99.6 mL·min^-1· (1.73 m²) ^-1 (n=67) and Q4 group: ≤88.5 mL·min^-1· (1.73 m²) ^-1 (n=67), and the proportion of APA in each group was 47.8% (32/67), 53.7% (36/67), 58.2% (39/67) and 71.6% (48/67). Logistic regression trend test suggested that the risk of APA tended to increase as GFR levels decreased (P<0.05). Multivariate Logistic regression analysis showed that systolic blood pressure >160 mmHg (OR=5.209, 95%CI=2.531-10.720), hypertension duration≥59 months (OR=4.326, 95%CI=1.950-9.595), blood potassium<3.25mmol/L (OR=4.714, 95%CI=2.046-10.860), GFR[Q4 gourp: ≤88.5 mL·min^-1· (1.73 m²) ^-1] (OR=4.106, 95%CI=1.492-11.300), basal aldosterone>13.42 ng/dL (OR=8.756, 95%CI=4.320-17.749) were independent risk factors for the occurrence of APA (P<0.050). The Nomogram prediction model was constructed based on the above variables of multivariate regression with an AUC of 0.898 (95%CI=0.859-0.936) and a C-index of 0.898, indicating a good prediction accuracy. The Hosmer-Lemeshow test showed that the model had a good fit (χ²=14.059, P=0.080). The model had a significant predictive efficacy at prediction probability thresholds of 0.10 to 0.90.

The risk of APA prevalence tends to increase with decreasing GFR levels. The APA prediction model constructed based on five factors, including systolic blood pressure, hypertension course, blood potassium, GFR quartile grouping and basal aldosterone, has good predictability, consistency and clinical practicality, which can help identify APA and contribute to clinical decision making.

The elderly tend to coexist with multiple chronic diseases (such as diabetes and hypertension) , while diabetes and hypertension can lead to chronic kidney damage, and fewer studies have been conducted on chronic kidney disease in older adults.

To investigate the proteinuria and renal dysfunction in the elderly population and provide guidance for the management of chronic kidney disease in the community-dwelling older adults by collecting the clinical data of physical examination of the elderly residents in community.

A total of 13 080 elderly residents who underwent physical examination in Zhuanqiao Community Health Service Center of Minhang District, Shanghai from 2020 to 2021 were included as the study objects. General information, physical examination results and laboratory examination data of study objects were collected. Subjects with estimated glomerular filtration rate (eGFR) <60 mL·min^-1· (1.73 m²) ^-1 were included in the abnormal renal function group (n=713) , and subjects with eGFR≥60 mL·min^-1· (1.73 m²) ^-1 were included in the normal renal function group (n=12 367) . The subjects with positive urine protein were included in the proteinuria group (n=1 690) , and the subjects with negative urine protein were included in the non-proteinuria group (n=11 390) . At the same time, the subjects were divided into 60 to 69 years old group (n=6 901) , 70 to 79 years old group (n=4 867) , 80 to 89 years old group (n=1 128) and ≥90 years old group (n=184) according to the age interval of 10 years. Multivariate Logistic regression analysis was used to explore the influencing factors of renal dysfunction and proteinuria in the study population.

There were significant differences in the detection rates of urine protein positive and renal dysfunction in 60 to 69 years old, 70 to 79 years old, 80 to 89 years old and ≥90 years old groups (P<0.05) . The detection rate of urinary protein positive in males aged 60 to 69 years and 70 to 79 years was higher than that in females, the detection rate of renal dysfunction in males aged 60 to 69 years was higher than females, and the detection rate of renal dysfunction in males aged 80 to 89 years was lower than females, the difference was statistically significant (P<0.05) . Age, the proportion of diabetes and hypertension, blood urea nitrogen (BUN) , serum creatinine (Scr) and triglyceride (TG) in the abnormal renal function group were higher than those in the normal renal function group, while total cholesterol (TC) , high-density lipoprotein cholesterol (HDL-C) , low density lipoprotein cholesterol (LDL-C) and hemoglobin (Hb) in the abnormal renal function group were lower than those in the normal renal function group. There was significant difference in urinary, albumin/creatinine ratio (ACR) between the two groups (P<0.05) . Multivariate Logistic regression analysis showed that age, hypertension, diabetes, proteinuria and anemia were the influencing factors of renal dysfunction (P<0.05) , while male, diabetes, obesity, hypertriglyceridemia, Scr and BNU were the influencing factors of proteinuria (P<0.05) .

The detection rates of proteinuria and renal dysfunction in the elderly aged 60 years and above are high, which increase with age. Age, hypertension, diabetes, anemia, hypertriglyceridemia and low HDL-C level are risk factors for renal dysfunction in community-dwelling elderly population (P<0.05) ; male, diabetes, obesity, hypertriglyceridemia, Scr and BUN are risk factors for proteinuria in community-dwelling elderly population.

Diabetic nephropathy (DN) is one of the most common microvascular complications of diabetes, which is highly prevalent and harmful. Early detection of DN is an important task in preventing related diseases. Currently, most of the researches are based on traditional statistical prediction methods, and data need to meet the prerequisites it requires. It is necessary to try to apply new methods such as machine learning in the area of DN prediction for its failing to meet the needs in the field of DN prediction in recent years.

To construct DN prediction model using the LASSO regression and BP neural network optimized by sparrow search algorithm (SSA-BP) .

This study was conducted from April 2023 to August 2023, and the data was obtained from publicly available data on complications of 133 patients with diabetes mellitus in Iran. Univariate analysis was conducted using SPSS 26.0 software, and variables were screened using LASSO regression. Using the presence of DN as the dependent variable, the training and testing sets were divided into 8∶2 and 7∶3 ratios, respectively. The SSA-BP neural network was used for modeling and analysis, and the prediction performance was compared with classical machine learning models to analyze the better DN model. Model evaluation was performed based on accuracy, precision, sensitivity, specificity, F1-score and AUC indicators.

Excluding 9 patients with type 1 diabetes, the effective sample size included in this study was 124 patients with type 2 diabetes mellitus (T2DM) , of which 73 (58.9%) were diagnosed with DN. Univariate analysis of risk factors for type 2 DN showed statistically significant for age, BMI, duration of diabetes, fasting blood glucose (FBG) , glycosylated hemoglobin (HbA_1c) , low-density lipoprotein (LDL) , high-density lipoprotein (HDL) , triacylglycerol (TG) , systolic blood pressure (SBP) and diastolic blood pressure (DBP) (P<0.05) . When the ratio of the training set to the test set was 8∶2, there were 59 DN patients in the training set (n=100) and 14 DN patients in the test set (n=24) . Five influencing factors of age, diabetes duration, HbA_1c, LDL, and SBP were obtained by LASSO regression screening. The accuracy rates of Logistic regression (LR) , K-nearest neighbor (KNN) , support vector machine (SVM) and SSA-BP models in the test set were 83.33%, 79.17%, 79.17%, 87.50%, and 95.83%, with F1-score as 0.846 2, 0.800 0, 0.800 0, 0.888 9, and 0.960 0, respectively. When the ratio of the training set to the test set was 7∶3, there were 52 DN patients in the training set (n=88) and 21 DN patients in the test set (n=36) . Seven influencing factors obtained by LASSO regression screening included age, BMI, diabetes duration, LDL, HDL, SBP, and DBP. The accuracy rates of LR, KNN, SVM, BP, and SSA-BP models in the test set were 86.11%, 86.11%, 86.11%, 72.22%, and 91.67%, with F1-score as 0.871 8, 0.871 8, 0.864 9, 0.705 9, and 0.909 1, respectively.

LR, KNN, and SVM perform better when the training set to the test set is 7∶3, while BP and SSA-BP perform better when the training set to the test set is 8∶2. Compared with the BP neural network and traditional machine learning models, SSA-BP model has the best prediction performance and can timely and accurately identify type 2 DN patients, realize early detection and treatment of DN, thus preventing and mitigating the harm to their bodies.

Diabetes nephropathy (DN) is a common complication of diabetes patients. The prediction and validation of its risk will help identify high-risk patients in advance and take intervention measures to avoid or delay the progress of nephropathy.

To analyze the risk factors affecting the complication of DN in patients with type 2 diabetes mellitus (T2DM) , construct a risk prediction model for the risk of DN in T2DM patients and validate it.

A total of 5 810 patients with T2DM admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2016 to June 2021 were selected as the study subjects and divided into the DN group (n=481) and non-DN group (n=5 329) according to the complication of DN. A 1∶1 case-control matching was performed on 481 of these DN patients and non-DN patients by gender and age (±2 years) , and the matched 962 T2DM patients were randomly divided into the training group (n=641) and validation group (n=321) based on a 2∶1 ratio. Basic data of patients, such as clinical characteristics, laboratory test results and other related data, were collected. LASSO regression was applied to optimize the screening variables, and a nomogram prediction model was developed using multivariate Logistic regression analysis. The discriminability, calibration and clinical validity of the prediction model were evaluated by using the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow calibration curve, and decision curve analysis (DCA) , respectively.

There were significant differences in gender, age, BMI, course of diabetes, white blood cell count, total cholesterol, triacylglycerol, low-density lipoprotein cholesterol, serum creatinine, hypertension, systolic blood pressure, diastolic blood pressure, glycosylated hemoglobin, apolipoprotein B, 24-hour urinary micro total protein, qualitative urinary protein between the DN and non-DN group (P<0.05) . Five predictor variables associated with the risk of DN in patients with T2DM were screened using LASSO regression analysis, and the results combined with multivariate Logistic regression analysis showed that duration of diabetes, total cholesterol, serum creatinine, hypertension, and qualitative urinary protein were risk factors for the complication of DN in T2DM patients (P<0.05) . The area under the ROC curve (AUC) for the risk of DN in the training group of the model was 0.866 (95%CI=0.839-0.894) , and the AUC for predicting the risk of DN in the validation group was 0.849 (95%CI=0.804-0.889) based on the predictor variables. The Hosmer-Lemeshow calibration curve fit was good (P=0.748 for the training group; P=0.986 for the validation group) . DCA showed that the use of nomogram prediction model was more beneficial in predicting DN when the threshold probability of patients was 0.15 to 0.95.

The nomogram prediction model containing five predictor variables (diabetes duration, total cholesterol, serum creatinine, hypertension, qualitative urinary protein) developed in this study can be used to predict the risk of DN in patients with T2DM.