Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.
Humans ; Prostatic Neoplasms/diagnosis* ; Male ; Artificial Intelligence ; Deep Learning ; Prognosis

Objective To investigate the risk factors for pneumonia complicating infectious mononucleosis(IM)in adults and construct a nomogram prediction model.Methods A retrospective analysis was conducted on 198 IM patients admitted to the Second Affiliated Hospital of Air Force Medical University from January 2015 to December 2021.Patients were divided into pneumonia group(n=52)and non-pneumonia group(n=146)based on whether pulmonary infection occurred during hospitalization.The baseline data(age,gender,place of onset,etc.),clinical manifestations(maximum body temperature,lymph node enlargement,splenomegaly,etc.),and inflammatory indicators[white blood cell count(WBC),C-reactive protein(CRP),etc.]were compared between the two groups.Kaplan-Meier curves were plotted to analyze the key indicators affecting the hospital stay of IM patients.Multivariate logistic regression was used to analyze the independent risk factors for pneumonia complicating IM in adults and construct a nomogram prediction model based on the identified risk factors.The predictive efficacy of the model was evaluated using the receiver operating characteristic(ROC)curve and the consistency of the model was assessed using the calibration curve.The fit of the model was evaluated using the Hosmer-Lemeshow test.Additionally,the sensitivity,specificity,and accuracy of the model were assessed using confusion matrix.Results Compared with non-pneumonia group,the pneumonia group had a significantly higher proportion of patients from rural areas,with body mass index(BMI)≥24 kg/m2,smoking history,hepatomegaly,fever duration of≥7 d,as well as increased total hospitalization costs and average daily hospitalization costs,and prolonged hospital stay(P<0.05).The proportion of patients with a history of antibiotic use was lower in the pneumonia group(P<0.05).Kaplan-Meier survival analysis showed that patients from rural areas,with BMI≥24 kg/m2,smoking history,no prophylactic use of antibiotics,fever duration≥7 d,and hepatomegaly had significantly prolonged hospital stays(P<0.05).Multivariate logistic regression analysis revealed that living in a rural area(OR=4.089,P<0.05),hepatomegaly(OR=4.082,P<0.05),and elevated WBC(OR=1.205,P<0.05)were independent risk factors for pneumonia complicating IM in adults,while the prophylactic use of antibiotics(OR=0.142,P<0.05)was an independent protective factor.The area under the ROC curve of the constructed nomogram prediction model was 0.827(95%CI 0.762-0.892),and the slope of the calibration curve was close to 1,and the Hosmer-Lemeshow test showed χ2=5.299,P=0.725,indicating good consistency and fit of the prediction model.The results of the confusion matrix assessment showed that the sensitivity of the model was 0.669(0.624-0.773),the specificity was 0.827(0.724-0.930),and the accuracy was 0.732(0.665-0.793).Conclusion The nomogram prediction model based on place of onset,hepatomegaly,the prophylactic use of antibiotics and WBC has excellent fit and discrimination,providing an effective quantitative tool for prognosis assessment of IM.

Drug-induced liver injury(DILI)is a common adverse drug reaction in clinical practice,which can lead to acute liver failure and even death in severe cases.In recent years,with the continuous introduction of new drugs and the expansion of their usage,the incidence and mortality rates of DILI have shown an upward trend,posing significant challenges to public health and clinical treatment.Macrophages,as a crucial component of the innate immune system,exhibit high plasticity and heterogeneity.They can polarize into pro-inflammatory M1 type or anti-inflammatory M2 type in response to microenvironmental signals.Research has demonstrated that macrophage polarization plays a central regulatory role in the occurrence and progression of DILI by influencing various processes such as inflammatory responses,cell apoptosis,and tissue repair.This review focuses on elucidating the regulatory mechanisms and roles of macrophage polarization in DILI,providing a theoretical framework for developing precise immunotherapeutic strategies.

Objective:To analyze the effects of SARS-CoV-2 infection and vaccination on virus mutation.Methods:The whole genome sequencing sequences of 2 659 local SARS-CoV-2 specimens from Jiangsu Province in 2023 were selected for analysis, and relevant information such as demographic and clinical characteristics were collected, and the effects of infection and vaccination on the genome-wide mutation rate and S gene′s selective pressure of the virus were analyzed by univariate and multivariate linear regression models.Results:The average age of these infected patients was 55.0 (31.0, 74.0) years, 1 150 cases (43.2%) in the age group of ≥60 years, 1 367 cases (51.4%) were males, 2 044 cases (76.9%) had a history of COVID-19 vaccination, and 1 629 cases (61.3%) had the first-time infection. The clinical symptoms of the infected patients were mainly mild, with a total of 2434 cases (91.5%), and 29 cases (1.1%) with severe symptoms or more. The average substitution rate of SARS-CoV-2 was 9.69 (9.38, 9.98)×10 -4 subs/site/year, and the dN/dS value of the S gene was 6.08 (5.56, 8.66), which was significantly greater than that of 1 ( P<0.001), indicating positive selection. The result of univariate and multivariate linear regression model analysis showed that the SARS-CoV-2 substitution rate was higher in those with vaccination history and reinfection, aged 20-30 years, ≥60 years, and the SARS-CoV-2 substitution rate was lower in males with moderate clinical symptoms and severe disease and above. Those with a history of vaccination and reinfection, aged 50-60 years old, ≥60 years old have smaller S gene dN/dS. Conclusions:Under the immune pressure exerted by vaccination and infection, the genome-wide mutation of SARS-COV-2 accelerated, but the non-synonymous mutation rate of the S gene decreased. The mechanism causing these phenomena needs further study.

Dental age estimation is a crucial aspect and one of the ways to accomplish forensic age estimation,and imaging technology is an important technique for dental age estimation.In recent years,some studies have preliminarily confirmed the feasibility of magnetic resonance imaging(MRI)in evaluating dental development,providing a new perspective and possibility for the evaluation of den-tal development,suggesting that MRI is expected to be a safer and more accurate tool for dental age estimation.However,further research is essential to verify its accuracy and feasibility.This article re-views the current state,challenges and limitations of MRI in dental development and age estimation,offering reference for the research of dental age assessment based on MRI technology.

Objective To establish age estimation models of northern Chinese Han adults using cranial suture images obtained by CT and multiplanar reformation(MPR),and to explore the applicability of cranial suture closure rule in age estimation of northern Chinese Han population.Methods The head CT samples of 132 northern Chinese Han adults aged 29-80 years were retrospectively collected.Volume reconstruction(VR)and MPR were performed on the skull,and 160 cranial suture tomography images were generated for each sample.Then the MPR images of cranial sutures were scored according to the closure grading criteria,and the mean closure grades of sagittal suture,coronal sutures(both left and right)and lambdoid sutures(both left and right)were calculated respectively.Finally taking the above grades as independent variables,the linear regression model and four machine learning models for age estimation(gradient boosting regression,support vector regression,decision tree regression and Bayesian ridge regression)were established for northern Chinese Han adults age estimation.The accu-racy of each model was evaluated.Results Each cranial suture closure grade was positively correlated with age and the correlation of sagittal suture was the highest.All four machine learning models had higher age estimation accuracy than linear regression model.The support vector regression model had the highest accuracy among the machine learning models with a mean absolute error of 9.542 years.Conclusion The combination of skull CT-MPR and machine learning model can be used for age esti-mation in northern Chinese Han adults,but it is still necessary to combine with other adult age estima-tion indicators in forensic practice.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

The emerging and re-emerging arthropod-borne infectious diseases pose a serious threat to global public health security. Field and laboratory studies of arthropods of medical importance are essential and critical for the prevention and control of arthropod-borne infectious diseases. Various institutions or universities in China have been conducting research in the field or laboratory study of arthropods of medical importance, but up to 2023, it is still lacking detailed biosafety guidelines or recommendations that can guide the related work for arthropods of medical importance. In order to proactively address potential biosafety issues in the field or laboratory activities related to arthropods of medical importance, improve the standardization of arthropod biosafety classification, operations, and protection, and ensure the safety of practitioners, an expert consensus on the biosafety recommendation of arthropods of medical importance in field and laboratory has been developed, aiming to guide the future work of arthropods and ensure the national biosafety and biosecurity of China.

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.