Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Objective To analyze the correlation between the lung allocation score (LAS) and the risk of early death and complications in patients with idiopathic pulmonary fibrosis (IPF) after lung transplantation. Methods Clinical data of 275 patients with IPF were retrospectively analyzed. The correlation between LAS and the risk of early death in IPF patients after lung transplantation and the correlation between LAS and complications at postoperative 1 year was assessed by univariate and multivariate Cox regression analyses. Results Among 275 recipients, 62, 83, 95 and 108 cases died within postoperative 30, 90, 180 and 365 d, respectively. LAS was correlated with 30-, 90-, 180- and 365-d fatality of IPF patients (all P<0.05), whereas it was not correlated with the incidence of primary graft dysfunction (PGD) and acute kidney injury (AKI) at 365 d after lung transplantation (both P>0.05). Conclusions LAS is correlated with the risk of early death of IPF patients after lung transplantation. While, it is not correlated the incidence of PGD and AKI early after lung transplantation. Special attention should be paid to the effect of comprehensive factors upon PGD and AKI.

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:To analyze the epidemiological and clinical characteristics of respiratory syncytial virus (RSV) infection in children in Jiangsu Province from 2014 to 2023.Methods:The acute respiratory infection cases in children aged 0-14 years were selected from outpatient/emergency or inpatient departments in 2 surveillance sentinel hospitals, respectively, in Nanjing, Suzhou and Taizhou of Jiangsu from 1 July 2014 to 31 December 2023, and RSV nucleic acid test was conducted and the intensity of the RSV infection was accessed by WHO influenza epidemiological threshold method, and case information and clinical data were collected. χ2 test was used to compare the differences between groups, and the Bonferroni method was used for pairwise comparisons between groups. Results:In 4 946 cases of acute respiratory infections, the RSV positive rate was 8.21% (406/4 946), and the age M( Q1, Q3) of the cases was 1 (0, 3) years. The RSV positive rate was 10.92% (258/2 362) during 2014-2019 and 6.06% (118/1 948) during 2019-2023, the difference was significant ( χ2=31.74, P<0.001). RSV infection mainly occurred from October to March during 2014-2019, with the incidence peak in December and moderate or higher intensity. The seasonality of RSV infection was not obvious during 2019-2023, with low intensity. The RSV positive rate was highest in children in age group 0- years (17.85%, 151/846), and the positive rate declined gradually with age ( χ2=184.51, P<0.001). The RSV positive rate was higher in inpatient cases (9.84%, 244/2 480) than in outpatient/emergency cases (6.57%, 162/2 466) ( χ2=17.54, P<0.001). In the 155 RSV infection cases with complete clinical data, the clinical symptoms mainly included cough (99.35%, 154/155), fever (55.48%, 86/155), and shortness of breath (45.16%, 70/155). In the cases aged <6 months, the proportion of those with fever was low, but the proportion of those with shortness of breath, transferred to intensive care units, and receiving oxygen therapy were higher (all P<0.05). Children aged <6 months and those with underlying diseases were more likely to have severe RSV infection (all P<0.05). Conclusions:RSV infection in children in Jiangsu Province showed seasonal prevalence in winter from 2014 to 2019. Since 2020, the seasonal characteristics of the epidemic have changed, the epidemic period has been dispersed and the epidemic intensity has decreased. Infants <1 year old were at high risk for RSV infection, and those <6 months old and with underlying diseases might have severe infection.

Objective To explore the establishment of a prognostic model based on machine learning algorithm to predict primary graft dysfunction(PGD)in patients with idiopathic pulmonary fibrosis(IPF)after lung transplantation.Methods Clinical data of 226 IPF patients who underwent lung transplantation were retrospectively analyzed.All patients were randomly divided into the training and test sets at a ratio of 7∶3.Using regularized logistic regression,random forest,support vector machine and artificial neural network,the prognostic model was established through variable screening,model establishment and model optimization.The performance of this prognostic model was assessed by the area under the receiver operating characteristic curve(AUC),positive predictive value,negative predictive value and accuracy.Results Sixteen key features were selected for model establishment.The AUC of the four prognostic models all exceeded 0.7.DeLong and McNemar tests found no significant difference in the performance among different models(both P＞0.05).Conclusions Based on four machine learning algorithms,the prognostic model for grade 3 PGD after lung transplantation is preliminarily established.The overall prediction performance of each model is similar,which may predict the risk of grade 3 PGD in IPF patients after lung transplantation.

177Lu-prostate specific membrane antigen(PSMA)radio-ligand therapy has been approved abroad for advanced prostate cancer and has been in several clinical trials in China.Based on domestic clinical practice and experimental data and referred to international experience and viewpoints,the expert group forms a consensus on the clinical application of 177Lu-PSMA radio-ligand therapy in prostate cancer to guide clinical 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.