ObjectiveTo assess the current status of medical waste management in Jinshan District of Shanghai, China, to identify existing issues, and to provide a scientific basis for formulating targeted strategies. MethodsData were collected from the routine supervision and inspection records of the Jinshan District Health Commission Supervision Institute from 2017 to 2021, covering all aspects of medical waste management, including collection, classification, transportation, storage, and administrative penalties. ResultsThe compliance rates for the establishment of institutional frameworks, staffing, internal handover, and registration in medical and healthcare institutions all exceeded 95.00%. However, only 2.31% of the medical and healthcare institutions met the 48-hour storage limit requirement for medical waste. Private institutions had significantly lower compliance rates (P<0.05) in aspects such as proper classification and collection, maintaining records for three years, adhering to the 48-hour storage limit, refraining from commercial transactions, timely disinfection and cleaning, and implementing emergency measures for waste loss. Compliance rates also varied among different types of institutions regarding the establishment of temporary storage facilities and the implementation of the transfer manifest system, with community healthcare institutions exhibiting relatively lower compliance rates (P<0.05). Over the past five years, private medical and healthcare institutions accounted for 63.33% of administrative penalty cases. ConclusionWhile medical waste management in Jinshan District, Shanghai, has gradually become more standardized, challenges remain. To address the issue of medical waste being stored for over 48 hours, medical waste transfer stations should be established to improve transfer efficiency and ensure complete waste collection. Additionally, for private and community healthcare institutions, weak links in management should be addressed by establishing medical waste quality control teams, enhancing supervision through digital tools, and optimizing management processes to comprehensively elevate medical waste management.

Amyotrophic lateral sclerosis (ALS) is an irreversible, fatal neurodegenerative disorder whose incidence is positively correlated with the aging population. ALS is characterized by the progressive loss of motor neurons, leading to muscle weakness, atrophy, and ultimately respiratory failure. The pathogenesis of ALS involves multiple factors, including genetic and environmental influences, with genetic factors playing a particularly significant role. To date, several causative genes have been identified in ALS, such as the Cu/Zn superoxide dismutase 1 (Cu/Zn SOD1, also known as SOD1) gene, transactive response DNA-binding protein 43 (TDP-43) gene, fused in sarcoma (FUS) gene, and chromosome open reading frame 72 (C9orf72). Mutations in these genes have been found not only in familial ALS but also in sporadic ALS. Based on the identified ALS risk genes, various ALS animal models have been established through multiple approaches, including transgenic models, gene knockout/knock-in models, and adeno-associated virus-mediated overexpression models. These models simulate some typical pathological features of human ALS, such as motor neuron loss, ubiquitinated inclusions, and neuromuscular junction degeneration. However, these models still have limitations: (1) single-gene mutation models are insufficient to fully replicate the complex multi-factorial pathogenesis of sporadic ALS; (2) significant differences in microenvironmental regulation mechanisms and the rate of neurodegeneration between model organisms and humans may affect the accurate reproduction of disease phenotypes and the reliable evaluation of drug efficacy. To better understand the pathogenesis of ALS and promote the development of effective therapies, constructing and optimizing ALS animal models is crucial. This review aims to summarize commonly used ALS gene mutation mouse models, analyze their phenotypes and pathological characteristics, including transgenic mouse models, gene knockout/knock-in mouse models, and adeno-associated virus-mediated overexpression mouse models, and further discuss their specific applications in ALS pathogenesis research and drug development by comparing the advantages and limitations of each model.

Amyotrophic lateral sclerosis (ALS) is an irreversible, fatal neurodegenerative disorder whose incidence is positively correlated with the aging population. ALS is characterized by the progressive loss of motor neurons, leading to muscle weakness, atrophy, and ultimately respiratory failure. The pathogenesis of ALS involves multiple factors, including genetic and environmental influences, with genetic factors playing a particularly significant role. To date, several causative genes have been identified in ALS, such as the Cu/Zn superoxide dismutase 1 (Cu/Zn SOD1, also known as SOD1) gene, transactive response DNA-binding protein 43 (TDP-43) gene, fused in sarcoma (FUS) gene, and chromosome open reading frame 72 (C9orf72). Mutations in these genes have been found not only in familial ALS but also in sporadic ALS. Based on the identified ALS risk genes, various ALS animal models have been established through multiple approaches, including transgenic models, gene knockout/knock-in models, and adeno-associated virus-mediated overexpression models. These models simulate some typical pathological features of human ALS, such as motor neuron loss, ubiquitinated inclusions, and neuromuscular junction degeneration. However, these models still have limitations: (1) single-gene mutation models are insufficient to fully replicate the complex multi-factorial pathogenesis of sporadic ALS; (2) significant differences in microenvironmental regulation mechanisms and the rate of neurodegeneration between model organisms and humans may affect the accurate reproduction of disease phenotypes and the reliable evaluation of drug efficacy. To better understand the pathogenesis of ALS and promote the development of effective therapies, constructing and optimizing ALS animal models is crucial. This review aims to summarize commonly used ALS gene mutation mouse models, analyze their phenotypes and pathological characteristics, including transgenic mouse models, gene knockout/knock-in mouse models, and adeno-associated virus-mediated overexpression mouse models, and further discuss their specific applications in ALS pathogenesis research and drug development by comparing the advantages and limitations of each model.

Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs. MDFF was trained and validated on two DDI datasets, evaluated across three distinct scenarios, and compared with advanced DDI prediction models using accuracy, precision, recall, area under the curve, and F1 score metrics. MDFF achieved state-of-the-art performance across all metrics. Ablation experiments showed that integrating multi-dimensional drug features yielded the best results. More importantly, we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs. Among 12 real-world adverse drug reaction reports, the predictions of 9 reports were supported by relevant evidence. Additionally, MDFF demonstrated the ability to explain adverse DDI mechanisms, providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.

Objective:The clinical data of prenatal stillbirth were analyzed in order to increase the understand-ing of the causes of stillbirth.Methods:Prenatal stillbirth cases that terminated pregnancy in Nanjing Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University from January 2018 to December 2022 were col-lected,and the distribution characteristics of clinical data and the stillbirth causes were analyzed.The causes of death were classified according to the standards developed by the Stillbirth Collaborative Research Network(SCRN)in the United States,and they were divided into clear cause-of-death group and unknown cause-of-death group.The different characteristics of the two groups were compared and analyzed.Results:There were 210 ca-ses of prenatal stillbirth during the study period,and 104 cases met the inclusion criteria.Among them,33 cases(31.7%)had autopsy results,39 cases(37.5%)had genetic results,and 75 cases(72.1%)had placental pathol-ogy.According to the classification of SCRN standard,55 cases(52.9%)were probably related to the cause of death,33 cases(31.7%)were classified as possible,13 cases(12.5%)were probably unrelated,and 3 cases(2.9%)could not be attributed to the cause of death,that is,84.6%(88 cases)in the clear cause of death group and 15.4%(16 cases)in the unknown cause of death group.The rate of placental pathological examination in the clear cause of death group was significantly higher than that in the unknown cause of death group(78.4%).In the classification of causes of death,placental pathological changes accounted for the largest proportion,account-ing for 26.9%(28 cases),followed by pregnancy complications accounting for 25.0%(26 cases),and 15.4%of the cases were still unexplained.Conclusions:Placental pathology is of great significance for clarifying the cause of stillbirth.It is feasible to use SCRN to classify the etiology of stillbirth.Pathological placental conditions account for a relatively high proportion in the classification of stillbirth causes.It is recommended that each case of stillbirth placenta should undergo pathological examination.

Objective:The clinical data of prenatal stillbirth were analyzed in order to increase the understand-ing of the causes of stillbirth.Methods:Prenatal stillbirth cases that terminated pregnancy in Nanjing Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University from January 2018 to December 2022 were col-lected,and the distribution characteristics of clinical data and the stillbirth causes were analyzed.The causes of death were classified according to the standards developed by the Stillbirth Collaborative Research Network(SCRN)in the United States,and they were divided into clear cause-of-death group and unknown cause-of-death group.The different characteristics of the two groups were compared and analyzed.Results:There were 210 ca-ses of prenatal stillbirth during the study period,and 104 cases met the inclusion criteria.Among them,33 cases(31.7%)had autopsy results,39 cases(37.5%)had genetic results,and 75 cases(72.1%)had placental pathol-ogy.According to the classification of SCRN standard,55 cases(52.9%)were probably related to the cause of death,33 cases(31.7%)were classified as possible,13 cases(12.5%)were probably unrelated,and 3 cases(2.9%)could not be attributed to the cause of death,that is,84.6%(88 cases)in the clear cause of death group and 15.4%(16 cases)in the unknown cause of death group.The rate of placental pathological examination in the clear cause of death group was significantly higher than that in the unknown cause of death group(78.4%).In the classification of causes of death,placental pathological changes accounted for the largest proportion,account-ing for 26.9%(28 cases),followed by pregnancy complications accounting for 25.0%(26 cases),and 15.4%of the cases were still unexplained.Conclusions:Placental pathology is of great significance for clarifying the cause of stillbirth.It is feasible to use SCRN to classify the etiology of stillbirth.Pathological placental conditions account for a relatively high proportion in the classification of stillbirth causes.It is recommended that each case of stillbirth placenta should undergo pathological examination.

Objective:The clinical data of prenatal stillbirth were analyzed in order to increase the understand-ing of the causes of stillbirth.Methods:Prenatal stillbirth cases that terminated pregnancy in Nanjing Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University from January 2018 to December 2022 were col-lected,and the distribution characteristics of clinical data and the stillbirth causes were analyzed.The causes of death were classified according to the standards developed by the Stillbirth Collaborative Research Network(SCRN)in the United States,and they were divided into clear cause-of-death group and unknown cause-of-death group.The different characteristics of the two groups were compared and analyzed.Results:There were 210 ca-ses of prenatal stillbirth during the study period,and 104 cases met the inclusion criteria.Among them,33 cases(31.7%)had autopsy results,39 cases(37.5%)had genetic results,and 75 cases(72.1%)had placental pathol-ogy.According to the classification of SCRN standard,55 cases(52.9%)were probably related to the cause of death,33 cases(31.7%)were classified as possible,13 cases(12.5%)were probably unrelated,and 3 cases(2.9%)could not be attributed to the cause of death,that is,84.6%(88 cases)in the clear cause of death group and 15.4%(16 cases)in the unknown cause of death group.The rate of placental pathological examination in the clear cause of death group was significantly higher than that in the unknown cause of death group(78.4%).In the classification of causes of death,placental pathological changes accounted for the largest proportion,account-ing for 26.9%(28 cases),followed by pregnancy complications accounting for 25.0%(26 cases),and 15.4%of the cases were still unexplained.Conclusions:Placental pathology is of great significance for clarifying the cause of stillbirth.It is feasible to use SCRN to classify the etiology of stillbirth.Pathological placental conditions account for a relatively high proportion in the classification of stillbirth causes.It is recommended that each case of stillbirth placenta should undergo pathological examination.

Objective:The clinical data of prenatal stillbirth were analyzed in order to increase the understand-ing of the causes of stillbirth.Methods:Prenatal stillbirth cases that terminated pregnancy in Nanjing Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University from January 2018 to December 2022 were col-lected,and the distribution characteristics of clinical data and the stillbirth causes were analyzed.The causes of death were classified according to the standards developed by the Stillbirth Collaborative Research Network(SCRN)in the United States,and they were divided into clear cause-of-death group and unknown cause-of-death group.The different characteristics of the two groups were compared and analyzed.Results:There were 210 ca-ses of prenatal stillbirth during the study period,and 104 cases met the inclusion criteria.Among them,33 cases(31.7%)had autopsy results,39 cases(37.5%)had genetic results,and 75 cases(72.1%)had placental pathol-ogy.According to the classification of SCRN standard,55 cases(52.9%)were probably related to the cause of death,33 cases(31.7%)were classified as possible,13 cases(12.5%)were probably unrelated,and 3 cases(2.9%)could not be attributed to the cause of death,that is,84.6%(88 cases)in the clear cause of death group and 15.4%(16 cases)in the unknown cause of death group.The rate of placental pathological examination in the clear cause of death group was significantly higher than that in the unknown cause of death group(78.4%).In the classification of causes of death,placental pathological changes accounted for the largest proportion,account-ing for 26.9%(28 cases),followed by pregnancy complications accounting for 25.0%(26 cases),and 15.4%of the cases were still unexplained.Conclusions:Placental pathology is of great significance for clarifying the cause of stillbirth.It is feasible to use SCRN to classify the etiology of stillbirth.Pathological placental conditions account for a relatively high proportion in the classification of stillbirth causes.It is recommended that each case of stillbirth placenta should undergo pathological examination.

Objective:The clinical data of prenatal stillbirth were analyzed in order to increase the understand-ing of the causes of stillbirth.Methods:Prenatal stillbirth cases that terminated pregnancy in Nanjing Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University from January 2018 to December 2022 were col-lected,and the distribution characteristics of clinical data and the stillbirth causes were analyzed.The causes of death were classified according to the standards developed by the Stillbirth Collaborative Research Network(SCRN)in the United States,and they were divided into clear cause-of-death group and unknown cause-of-death group.The different characteristics of the two groups were compared and analyzed.Results:There were 210 ca-ses of prenatal stillbirth during the study period,and 104 cases met the inclusion criteria.Among them,33 cases(31.7%)had autopsy results,39 cases(37.5%)had genetic results,and 75 cases(72.1%)had placental pathol-ogy.According to the classification of SCRN standard,55 cases(52.9%)were probably related to the cause of death,33 cases(31.7%)were classified as possible,13 cases(12.5%)were probably unrelated,and 3 cases(2.9%)could not be attributed to the cause of death,that is,84.6%(88 cases)in the clear cause of death group and 15.4%(16 cases)in the unknown cause of death group.The rate of placental pathological examination in the clear cause of death group was significantly higher than that in the unknown cause of death group(78.4%).In the classification of causes of death,placental pathological changes accounted for the largest proportion,account-ing for 26.9%(28 cases),followed by pregnancy complications accounting for 25.0%(26 cases),and 15.4%of the cases were still unexplained.Conclusions:Placental pathology is of great significance for clarifying the cause of stillbirth.It is feasible to use SCRN to classify the etiology of stillbirth.Pathological placental conditions account for a relatively high proportion in the classification of stillbirth causes.It is recommended that each case of stillbirth placenta should undergo pathological examination.

Objective:The clinical data of prenatal stillbirth were analyzed in order to increase the understand-ing of the causes of stillbirth.Methods:Prenatal stillbirth cases that terminated pregnancy in Nanjing Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University from January 2018 to December 2022 were col-lected,and the distribution characteristics of clinical data and the stillbirth causes were analyzed.The causes of death were classified according to the standards developed by the Stillbirth Collaborative Research Network(SCRN)in the United States,and they were divided into clear cause-of-death group and unknown cause-of-death group.The different characteristics of the two groups were compared and analyzed.Results:There were 210 ca-ses of prenatal stillbirth during the study period,and 104 cases met the inclusion criteria.Among them,33 cases(31.7%)had autopsy results,39 cases(37.5%)had genetic results,and 75 cases(72.1%)had placental pathol-ogy.According to the classification of SCRN standard,55 cases(52.9%)were probably related to the cause of death,33 cases(31.7%)were classified as possible,13 cases(12.5%)were probably unrelated,and 3 cases(2.9%)could not be attributed to the cause of death,that is,84.6%(88 cases)in the clear cause of death group and 15.4%(16 cases)in the unknown cause of death group.The rate of placental pathological examination in the clear cause of death group was significantly higher than that in the unknown cause of death group(78.4%).In the classification of causes of death,placental pathological changes accounted for the largest proportion,account-ing for 26.9%(28 cases),followed by pregnancy complications accounting for 25.0%(26 cases),and 15.4%of the cases were still unexplained.Conclusions:Placental pathology is of great significance for clarifying the cause of stillbirth.It is feasible to use SCRN to classify the etiology of stillbirth.Pathological placental conditions account for a relatively high proportion in the classification of stillbirth causes.It is recommended that each case of stillbirth placenta should undergo pathological examination.