[This corrects the article DOI: 10.1016/j.apsb.2024.09.010.].

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To explore the efficacy and safety of sivelestat sodium in patients with acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU).Methods:Sixty patients with ARDS admitted to the ICU of the Fuyang Hospital Affiliated to Anhui Medical University from August 1, 2023 to November 1, 2024 were selected and divided into the control group (conventional treatment, 30 cases) and the sivelestat sodium group (treated with sivelestat sodium in addition to conventional treatment, 30 cases) by the random number table method. The clinical data such as inflammatory factors, blood gas analysis indicators, Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ score and Sequential Organ Failure Assessment (SOFA) score of the two groups of patients before treatment and 3 days after treatment were compared. The prognostic indicators such as mechanical ventilation time, ICU stay time, total hospital stay time, 28-day mortality rate and clinical efficacy of the two groups of patients were compared.Results:Before treatment, there were no statistically significant differences in inflammatory factors, blood gas analysis indicators, APACHE Ⅱ score and SOFA score between the two groups of patients (all P>0.05). After 3 days of treatment, the improvement degrees of APACHE Ⅱ score, SOFA score, arterial partial pressure of oxygen (PaO 2), oxygenation index (PaO 2/FiO 2), procalcitonin (PCT), interleukin-6 (IL-6), and C-reactive protein (CRP) in the sivelestat sodium group were all greater than those in the control group. The differences were all statistically significant (all P<0.05); The mechanical ventilation time [(5.31±4.12) d vs (7.17±2.32)d] and ICU stay [(6.31±3.42)d vs (8.93±5.26)d] of patients in the sivelestat sodium group were significantly shorter than those in the control group, and the differences were statistically significant (all P<0.05). There was no statistically significant difference in the 28-day mortality rate between the sivelestat sodium group [20.00%(6/30)] and the control group [43.33%(13/30)] ( P>0.05). The total effective rate of treatment in the sivelestat sodium group was significantly higher than that in the control group [80.00%(24/30) vs 56.67%(17/30)], and the difference was statistically significant (χ 2=4.167, P=0.041). Conclusions:Sivelestat sodium is helpful in improving the physiological parameters of patients with ARDS, effectively reducing the levels of inflammatory factors in the body, shortening the duration of mechanical ventilation and ICU stay, but has no significant effect on the 28-day mortality rate.

Objective:To explore the efficacy and safety of sivelestat sodium in patients with acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU).Methods:Sixty patients with ARDS admitted to the ICU of the Fuyang Hospital Affiliated to Anhui Medical University from August 1, 2023 to November 1, 2024 were selected and divided into the control group (conventional treatment, 30 cases) and the sivelestat sodium group (treated with sivelestat sodium in addition to conventional treatment, 30 cases) by the random number table method. The clinical data such as inflammatory factors, blood gas analysis indicators, Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ score and Sequential Organ Failure Assessment (SOFA) score of the two groups of patients before treatment and 3 days after treatment were compared. The prognostic indicators such as mechanical ventilation time, ICU stay time, total hospital stay time, 28-day mortality rate and clinical efficacy of the two groups of patients were compared.Results:Before treatment, there were no statistically significant differences in inflammatory factors, blood gas analysis indicators, APACHE Ⅱ score and SOFA score between the two groups of patients (all P>0.05). After 3 days of treatment, the improvement degrees of APACHE Ⅱ score, SOFA score, arterial partial pressure of oxygen (PaO 2), oxygenation index (PaO 2/FiO 2), procalcitonin (PCT), interleukin-6 (IL-6), and C-reactive protein (CRP) in the sivelestat sodium group were all greater than those in the control group. The differences were all statistically significant (all P<0.05); The mechanical ventilation time [(5.31±4.12) d vs (7.17±2.32)d] and ICU stay [(6.31±3.42)d vs (8.93±5.26)d] of patients in the sivelestat sodium group were significantly shorter than those in the control group, and the differences were statistically significant (all P<0.05). There was no statistically significant difference in the 28-day mortality rate between the sivelestat sodium group [20.00%(6/30)] and the control group [43.33%(13/30)] ( P>0.05). The total effective rate of treatment in the sivelestat sodium group was significantly higher than that in the control group [80.00%(24/30) vs 56.67%(17/30)], and the difference was statistically significant (χ 2=4.167, P=0.041). Conclusions:Sivelestat sodium is helpful in improving the physiological parameters of patients with ARDS, effectively reducing the levels of inflammatory factors in the body, shortening the duration of mechanical ventilation and ICU stay, but has no significant effect on the 28-day mortality rate.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

The adipose tissue of mammals represents an important energy-storing and endocrine organ, and its dysfunction is relevant to the onset of several health problems, including non-alcoholic fatty liver disease (NAFLD). However, whether treatments targeting adipose dysfunction could alleviate NAFLD has not been well-studied. Adrenomedullin 2 (ADM2), belonging to the CGRP superfamily, is a protective peptide that has been shown to inhibit adipose dysfunction. To investigate the adipose tissue-specific effects of ADM2 on NAFLD, adipose-specific ADM2-overexpressing transgenic (aADM2-tg) mice were developed. When fed a high-fat diet, aADM2-tg mice displayed decreased hepatic triglyceride accumulation compared to wild-type mice, which was attributable to the inhibition of hepatic de novo lipogenesis. Results from lipidomics studies showed that ADM2 decreased ceramide levels in adipocytes through the upregulation of ACER2, which catalyzes ceramide catabolism. Mechanically, activation of adipocyte HIF2α was required for ADM2 to promote ACER2-dependent adipose ceramide catabolism as well as to decrease hepatic lipid accumulation. This study highlights the role of ADM2 and adipose-derived ceramide in NAFLD and suggests that its therapeutic targeting could alleviate disease symptoms.

Objective:To carry out invasive prenatal diagnosis for a fetus with ultrasound-indicated agenesis of corpus callosum and explore its genetic etiology.Methods:A pregnant woman presented at the Affiliated Hospital of Putian College on December 16, 2022 was selected as the study subject. Amniotic fluid and peripheral blood samples from the fetus and the couple were collected. Conventional G-banded chromosomal karyotyping was carried out, and whole-genome copy number variation analysis was performed using single nucleotide polymorphism microarray (SNP-array).Results:The karyotypes of the fetus and the couple were normal by the G-banding analysis. SNP-array analysis of the amniotic fluid sample revealed a 4.5 Mb microdeletion in the 18q21.2q21.31 region of the fetus. SNP-array analysis of peripheral blood samples from the couple did not find any abnormality.Conclusion:Through G-banded chromosomal karyotyping and SNP-array analysis, a fetus with 18q21.2q21.31 microdeletion was identified, which has conformed to the diagnosis of Pitt-Hopkins syndrome. Above finding has provided a basis for genetic counseling for the couple.

Objective To explore the current status of H.pylori infection in the natural population of Sanya City,analyze its influencing factors,and provide a reference basis for the prevention and control of H.pylori infection.Methods A total of 677 residents from four districts of Sanya City were selected by overall stratified random sampling method,and were subjected to urea 14C breath test and questionnaire survey to calculate the positive rate of H.pylori in the natural population and analyze the influencing factors of H.pylori infection.Results A total of 606 residents were included,and the number of H.pylori positive detections was 261,with a positive detection rate of 38.5％.Among them,different ethnicity,marital status,smoking,eating vegetables and fruits,and literacy level were associated with H.pylori infection(P＜0.05);gender,age,BMI,alcohol consumption,drinking water source,betel quid chewing,and the number of cohabitants were not significantly associated with H.pylori infection(P＞0.05).Family infection was an independent risk factor for H.pylori infection in the natural population of Sanya City,and Li ethnicity,frequent consumption of fruits and vegetables,and college and higher education level were independent protective factors for H.pylori infection in the natural population of Sanya City.Conclusion The rate of H.pylori infection in the natural population of Sanya City is lower than the national average.Consuming more fruits and vegetables and improving the awareness of hygiene protection are conducive to the prevention of H.pylori infection;and the promotion of the family and related members with the same examination and treatment is important to avoid aggregation of infection within the family.

Objective To explore the correlation between colorectal cancer tissue Janus kinase 2(JAK2)gene mutations and T cytokine 3(TCF3)protein expression with clinical pathological characteristics and prognosis,and to provide laboratory reference indicators for early evaluation of the illness severity and prognosis of colorectal cancer.Methods A retrospective analysis was conducted on the data of 50 colorectal cancer patients who were admitted from January 2016 to April 2021 and retained colon cancer and adjacent tissue wax blocks.Basic information,clinical and pathological features such as TNM staging,lymph node metastasis,and 3-year survival prognosis of the patients were collected.The wax blocks of colon cancer and adjacent tissues of patients were detected,in which Taqman fluorescence probe method was applied to detect the distribution of JAK2 gene at the rs2230724 locus AA,AG,and GG genotypes in colon cancer tissues,and immunohistochemistry method was applied to detect the positive expression rate of TCF3 protein in colon cancer and adjacent tissues.The basic information,JAK2 rs2230724 gene mutation,and TCF3 protein expression of patients with different clinical and pathological characteristics were compared,and the influencing factors of clinical and pathological characteristics of colon cancer was analyzed by logistic regression model.Kaplan Meier survival curve was applied to compare the survival prognosis of patients with JAK2 gene mutations and TCF3 protein expression in colorectal cancer tissue,and Cox regression model was applied to analyze the risk factors affecting the prognosis of colorectal cancer patients.Results The positive expression rate of TCF3 protein in colon cancer tissues was higher than that in adjacent tissues(P＜0.05).The age,BMI,proportion of GG genotype at rs2230724 locus of JAK2 gene and positive expression rate of TCF3 protein in TNM stage Ⅲ colon cancer patients were higher than those in TNM stage Ⅰ-Ⅱ patients(P＜0.05);The age,BMI,smoking rate,proportion of GG type at the rs2230724 locus of JAK2 gene in colon cancer tissue,and positive expression rate of TCF3 protein in patients with lymph node metastasis were higher than those without lymph node metastasis(P＜0.05);The results of the logistic regression model analysis showed that the influencing factors of clinical pathological features such as TNM staging and lymph node metastasis in colon cancer were age,mutation of JAK2 gene rs2230724 site in colon cancer tissue,and positive expression rate of TCF3 protein(P＜0.05).The Kaplan Meier survival curve analysis showed that patients with JAK2 gene rs2230724 GG genotype and TCF3 protein positivity in colon cancer tissue had higher cumulative all-cause mortality rates(P＜0.05).The results of univariate and multivariate Cox regression model analysis showed that independent risk factors affecting the prognosis of colorectal cancer patients include JAK2 gene rs2230724 site GG type,TCF3 protein positive expression,TNM stage Ⅲ,lymph node metastasis,and age.Conclusion The proportion of JAK2 gene rs2230724 GG type and TCF3 protein expression in colorectal cancer tissues are related to their clinical pathological characteristics and prognosis,and can be used as reference indicators for evaluating clinical pathological characteristics and predicting prognosis of colorectal cancer.

Pediatric acute hyperextension spinal cord injury (SCI) named as PAHSCI by us, is a special type of thoracolumbar SCI without radiographic abnormality and highly related to back-bend in dance training, which has been increasingly reported. At present, it has become the leading cause of SCI in children, and brings a heavy social and economic burden. Both domestic and foreign academic institutions and dance education organizations lack a correct understanding of PAHSCI and relevant standards, specifications or guidelines. In order to provide standardized guidance, the expert team formulated this guideline based on the principles of science and practicability, starting from the diagnosis, differential diagnosis, etiology, admission evaluation, treatment, complications and prevention. This guideline puts forward 23 recommendations for 14 related issues.
Child ; Humans ; Spinal Cord Injuries/complications* ; Spinal Cord