Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Humans ; Fibrous Dysplasia, Polyostotic/complications* ; Hemangioma

Objective: To analyze the prevalence and the risk factors of fungal sepsis in 25 neonatal intensive care units (NICU) among preterm infants in China, and to provide a basis for preventive strategies of fungal sepsis. Methods: This was a second-analysis of the data from the "reduction of infection in neonatal intensive care units using the evidence-based practice for improving quality" study. The current status of fungal sepsis of the 24 731 preterm infants with the gestational age of <34⁺⁰ weeks, who were admitted to 25 participating NICU within 7 days of birth between May 2015 and April 2018 were retrospectively analyzed. These preterm infants were divided into the fungal sepsis group and the without fungal sepsis group according to whether they developed fungal sepsis to analyze the incidences and the microbiology of fungal sepsis. Chi-square test was used to compare the incidences of fungal sepsis in preterm infants with different gestational ages and birth weights and in different NICU. Multivariate Logistic regression analysis was used to study the outcomes of preterm infants with fungal sepsis, which were further compared with those of preterm infants without fungal sepsis. The 144 preterm infants in the fungal sepsis group were matched with 288 preterm infants in the non-fungal sepsis group by propensity score-matched method. Univariate and multivariate Logistic regression analysis were used to analyze the risk factors of fungal sepsis. Results: In all, 166 (0.7%) of the 24 731 preterm infants developed fungal sepsis, with the gestational age of (29.7±2.0) weeks and the birth weight of (1 300±293) g. The incidence of fungal sepsis increased with decreasing gestational age and birth weight (both P<0.001). The preterm infants with gestational age of <32 weeks accounted for 87.3% (145/166). The incidence of fungal sepsis was 1.0% (117/11 438) in very preterm infants and 2.0% (28/1 401) in extremely preterm infants, and was 1.3% (103/8 060) in very low birth weight infants and 1.7% (21/1 211) in extremely low birth weight infants, respectively. There was no fungal sepsis in 3 NICU, and the incidences in the other 22 NICU ranged from 0.7% (10/1 397) to 2.9% (21/724), with significant statistical difference (P<0.001). The pathogens were mainly Candida (150/166, 90.4%), including 59 cases of Candida albicans and 91 cases of non-Candida albicans, of which Candida parapsilosis was the most common (41 cases). Fungal sepsis was independently associated with increased risk of moderate to severe bronchopulmonary dysplasia (BPD) (adjusted OR 1.52, 95%CI 1.04-2.22, P=0.030) and severe retinopathy of prematurity (ROP) (adjusted OR 2.55, 95%CI 1.12-5.80, P=0.025). Previous broad spectrum antibiotics exposure (adjusted OR=2.50, 95%CI 1.50-4.17, P<0.001), prolonged use of central line (adjusted OR=1.05, 95%CI 1.03-1.08, P<0.001) and previous total parenteral nutrition (TPN) duration (adjusted OR=1.04, 95%CI 1.02-1.06, P<0.001) were all independently associated with increasing risk of fungal sepsis. Conclusions: Candida albicans and Candida parapsilosis are the main pathogens of fungal sepsis among preterm infants in Chinese NICU. Preterm infants with fungal sepsis are at increased risk of moderate to severe BPD and severe ROP. Previous broad spectrum antibiotics exposure, prolonged use of central line and prolonged duration of TPN will increase the risk of fungal sepsis. Ongoing initiatives are needed to reduce fungal sepsis based on these risk factors.
Infant ; Infant, Newborn ; Humans ; Birth Weight ; Intensive Care Units, Neonatal ; Retrospective Studies ; Tertiary Care Centers ; Infant, Extremely Low Birth Weight ; Gestational Age ; Infant, Extremely Premature ; Sepsis/epidemiology* ; Retinopathy of Prematurity/epidemiology* ; Bronchopulmonary Dysplasia/epidemiology*

Objective: To understand the characteristics and associated factors of viral nucleic acid conversion in children infected with Omicron variant strain of SARS-CoV-2 in Shanghai. Methods: The clinical symptoms, laboratory results and other data of 177 children infected with SARS-CoV-2 who were hospitalized in Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University (designated hospital for SARS-CoV-2 infection in Shanghai) from April 25 to June 8, 2022 were retrospectively analyzed. According to the chest imaging findings, the children were divided into mild and common type groups. According to their age, the unvaccinated children were divided into<3 years old group and 3-<18 years old group. According to the vaccination status, the children aged 3-<18 year were divided into non-vaccination group, 1-dose vaccination group and 2-dose vaccination group. Comparison between groups was performed by independent sample t-test and analysis of variance, and multivariate linear regression analysis was used for multivariate analysis. Results: Among the 177 children infected with Omicron variant of SARS-CoV-2, 96 were males and 81 were females, aged 3 (1, 6) years. The time of viral nucleic acid negative conversion was (10.3±3.1) days. The 177 children were 138 cases of mild type and 39 cases of common type. Among the children aged 3-<18 years old, 55 cases were not vaccinated, 5 cases received 1-dose and 36 cases received 2-dose vaccination. Among the 36 children who received 2 doses of vaccination, the time of viral nucleic acid negative conversion was shorter in those vaccinated within 6 months than those over 6 months ((7.1±1.9) vs. (10.8±3.0) d, t=-3.23, P=0.004). Univariate analysis showed that the time of nucleic acid negative conversion of SARS-CoV-2 was associated with age, underlying diseases, gastrointestinal symptoms, white blood cell count, proportion of neutrophils, proportion of lymphocytes, and the number of doses of SARS-CoV-2 vaccine (t=3.87, 2.55, 2.04, 4.24, 3.51, 2.92, F=16.27, all P<0.05). Multiple linear regression analysis showed that older age (β=-0.33, 95% CI -0.485--0.182, P<0.001) and more doses of vaccination (β=-0.79, 95% CI -1.463--0.120, P=0.021) were associated with shortened nucleic acid negative conversion time in children, while lower lymphocyte proportion (β=-0.02, 95% CI -0.044--0.002, P=0.031) and underlying diseases (β=1.52, 95% CI 0.363-2.672, P=0.010) were associated with prolonged nucleic acid negative conversion time in children. Conclusion: The children infected with Omicron variant of SARS-CoV-2 with reduced lymphocyte proportion and underlying diseases may have longer time of viral nucleic acid negative conversion,while children with older age and more doses of vaccination may have shorter time of viral nucleic acid negative conversion.
Child ; Female ; Male ; Humans ; Child, Preschool ; Adolescent ; SARS-CoV-2 ; COVID-19 Vaccines ; Nucleic Acids ; COVID-19 ; Retrospective Studies ; China/epidemiology* ; Translocation, Genetic ; Hospitals, Pediatric

OBJECTIVE: To study the clinicopathological features of children with lupus nephritis (LN) with positive anti-neutrophil cytoplasmic antibody (ANCA). METHODS: A retrospective analysis was performed for the children who were diagnosed with LN in the First Affiliated Hospital of Sun Yat-sen University from January 2003 to December 2019. According to the results of serum ANCA, they were divided into two groups: ANCA-positive group ( RESULTS: Compared with the ANCA-negative group, the ANCA-positive group had a significant reduction in leukocytes and a significant increase in erythrocyte sedimentation rate ( CONCLUSIONS Children with ANCA-positive LN tend to have more severe renal pathological injury, which is not exactly parallel with clinical manifestations, suggesting that timely renal biopsy is of great importance.
Antibodies, Antineutrophil Cytoplasmic ; Child ; Creatinine ; Humans ; Kidney ; Lupus Nephritis ; Retrospective Studies

Food deprivation can rescue obesity and overweight-induced mood disorders, and promote mood performance in normal subjects. Animal studies and clinical research have revealed the antidepressant-like effect of calorie restriction, but little is known about the mechanism of calorie restriction-induced mood modification. Previous studies have found that astrocytes modulate depressive-like behaviors. Inositol 1,4,5-trisphosphate receptor type 2 (IP3R2) is the predominant isoform in mediating astrocyte Ca
Adenosine Triphosphate ; Animals ; Antidepressive Agents/therapeutic use* ; Caloric Restriction ; Mice ; Mice, Knockout ; Prefrontal Cortex

Food deprivation can rescue obesity and overweight-induced mood disorders, and promote mood performance in normal subjects. Animal studies and clinical research have revealed the antidepressant-like effect of calorie restriction, but little is known about the mechanism of calorie restriction-induced mood modification. Previous studies have found that astrocytes modulate depressive-like behaviors. Inositol 1,4,5-trisphosphate receptor type 2 (IP3R2) is the predominant isoform in mediating astrocyte Ca

OBJECTIVE: To investigate the clinical features and outcome of neonatal acute respiratory distress syndrome (ARDS) in southwest Hubei, China. METHODS: According to the Montreux definition of neonatal ARDS, a retrospective clinical epidemiological investigation was performed on the medical data of neonates with ARDS who were admitted to Department of Neonatology/Pediatrics in 17 level 2 or level 3 hospitals in southwest Hubei from January to December, 2017. RESULTS: A total of 7 150 neonates were admitted to the 17 hospitals in southwest Hubei during 2017 and 66 (0.92%) were diagnosed with ARDS. Among the 66 neonates with ARDS, 23 (35%) had mild ARDS, 28 (42%) had moderate ARDS, and 15 (23%) had severe ARDS. The main primary diseases for neonatal ARDS were perinatal asphyxia in 23 neonates (35%), pneumonia in 18 neonates (27%), sepsis in 12 neonates (18%), and meconium aspiration syndrome in 10 neonates (15%). Among the 66 neonates with ARDS, 10 neonates (15%) were born to the mothers with an age of ≥35 years, 30 neonates (45%) suffered from intrauterine distress, 32 neonates (49%) had a 1-minute Apgar score of 0 to 7 points, 24 neonates (36%) had abnormal fetal heart monitoring results, and 21 neonates (32%) experienced meconium staining of amniotic fluid. Intraventricular hemorrhage was the most common comorbidity (12 neonates), followed by neonatal shock (9 neonates) and patent ductus arteriosus (8 neonates). All 66 neonates with ARDS were treated with mechanical ventilation in addition to the treatment for primary diseases. Among the 66 neonates with ARDS, 10 died, with a mortality rate of 15% (10/66), and 56 neonates were improved or cured, with a survival rate of 85% (56/66). CONCLUSIONS Neonatal ARDS in southwest Hubei is mostly mild or moderate. Perinatal asphyxia and infection may be the main causes of neonatal ARDS in this area. Intraventricular hemorrhage is the most common comorbidity. Neonates with ARDS tend to have a high survival rate after multimodality treatment.
China ; Female ; Humans ; Infant, Newborn ; Meconium Aspiration Syndrome ; Pregnancy ; Respiratory Distress Syndrome, Newborn ; Retrospective Studies

Objective:To establish an artificial intelligence (AI)-assisted diagnostic system for lung cancer via deep transfer learning.Methods:The researchers collected 519 lung pathologic slides from 2016 to 2019, covering various lung tissues, including normal tissues, adenocarcinoma, squamous cell carcinoma and small cell carcinoma, from the Beijing Chest Hospital, the Capital Medical University. The slides were digitized by scanner, and 316 slides were used as training set and 203 as the internal test set. The researchers labeled all the training slides by pathologists and establish a semantic segmentation model based on DeepLab v3 with ResNet-50 to detect lung cancers at the pixel level. To perform transfer learning, the researchers utilized the gastric cancer detection model to initialize the deep neural network parameters. The lung cancer detection convolutional neural network was further trained by fine-tuning of the labeled data. The deep learning model was tested by 203 slides in the internal test set and 1 081 slides obtained from TCIA database, named as the external test set.Results:The model trained with transfer learning showed substantial accuracy advantage against the one trained from scratch for the internal test set [area under curve (AUC) 0.988 vs. 0.971, Kappa 0.852 vs. 0.832]. For the external test set, the transferred model achieved an AUC of 0.968 and Kappa of 0.828, indicating superior generalization ability. By studying the predictions made by the model, the researchers obtained deeper understandings of the deep learning model.Conclusions:The lung cancer histopathological diagnostic system achieves higher accuracy and superior generalization ability. With the development of histopathological AI, the transfer learning can effectively train diagnosis models and shorten the learning period, and improve the model performance.