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

BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients. METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS ( n = 178,883) and non-BS-GPS ( n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score ≥11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively. RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge. CONCLUSION Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.
Humans ; Retrospective Studies ; Male ; Length of Stay/statistics & numerical data* ; Female ; Middle Aged ; Adult ; Psychological Distress ; Inpatients/psychology* ; Aged ; Anxiety/diagnosis* ; Depression/diagnosis*

OBJECTIVE To investigate the relationship between 23-site chip genetic screening failures and the results of newborns hearing screening,and to provide clinical reference for the diagnosis and treatment of genetic screening failures.METHODS There were 1 916 newborns born in the Beijing area from November 2022 to May 2024,who did not pass the 23-site chip genetic screening tests and underwent newborn hearing screening with definite initial screening results.Chi-square test was used to analyze the relationship between different mutation types and genotypes and the initial hearing screening results.RESULTS The overall neonatal hearing screening failure rate was 5.27%(101/1 916),with a higher failure rate of 61.54%(56/91)for homozygous and compound heterozygous mutations than the failure rate of 2.54%(45/1 772)for heterozygous mutations,0%(0/34)for digenic gene heterozygous mutations,and 0(0/19)for mtDNA 12S rRNA mutations,with a statistically significant difference(P<0.001).Among the homozygous and compound heterozygous mutations,the failure rates of homozygous and compound heterozygous for GJB2 gene and SLC26A4 gene were 59.76%(49/82)and 77.78%(7/9),respectively,with no statistically significant difference between the two groups(P=0.488).The homozygous and compound heterozygous for GJB2 gene were divided into three groups based on genotype:c.109G>A homozygous mutations,c.109G>A compound heterozygous mutations,and other homozygous and compound heterozygous mutations.The hearing screening failure rates of the three groups,from highest to lowest,were as follow:other homozygous and compound heterozygous mutations(88.89%,8/9),c.109G>A homozygous mutations(65.12%,28/43),and c.109G>A compound heterozygous mutations(43.33%,13/30),with a statistically significant difference(P=0.029).The failure rates of heterozygous for GJB2 gene,SLC26A4 gene and GJB3 gene were 2.86%(40/1 398),1.25%(4/321)and 1.89%(1/53),respectively,with no statistically significant difference among the three groups(P=0.241).The failure rate of hearing screening for individuals with GJB2 heterozygotes of different genotypes and individuals with SLC26A4 heterozygotes of different genotypes did not show statistically significant differences.CONCLUSION The failure rate of newborn hearing screening for homozygous and compound heterozygous mutation of 23-site chip genetic screening is higher than that of other mutation types,verifying the effectiveness of the newborn hearing screening program.Some newborns of homozygous and compound heterozygous mutation can pass the hearing screening,especially those with the c.109G>A homozygous and compound heterozygous mutation,who need clinical follow-up.

Objective To explore the influencing factors of the failure in trial of labor after cesarean(TOLAC),construct and verify a prediction model for the risk of TOLAC.Methods The clinical data of 273 pregnant women who underwent TOLAC in The First Affiliated Hospital of Naval Medical University from 2019 to 2022 were retrospectively analyzed.Logistic regression was used to analyze influencing factors of the failure in TOLAC,and a nomogram model was established for individualized risk assessment.The best threshold of failure risk of TOLAC was evaluated by the decision-making curve and clinical influence curve.Results There were statistically significant differences in the age,gestational week,body mass index(BMI)before delivery,time to the last cesarean,cervical Bishop score and delivery times between the successful trial delivery group and the failed trial delivery group.The best intervention threshold was 0.72,that is,vaginal trial delivery should to be stopped when the risk of TOLAC failure was more than 72%as evaluated by the prediction model.Conclusion Age,gestational week,BMI before delivery,time to the last cesarean,Bishop score of cervix and delivery times are influencing factors for TOLAC failure.The prediction model based on these factors can provide a quantifiable TOLAC risk for pregnant women.

BackgroundPatients with depressive disorder often exhibit impaired decision-making functions. However， the relationship between decision-making abilities and depressive and anxiety symptoms in these patients remains unclear. ObjectiveTo explore the characteristics of decision-making behavior in patients with depressive disorder， and to analyze its relationship with clinical symptoms. MethodsA total of 48 patients diagnosed with depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders， fourth edition （DSM-IV） were recruited from the Department of Psychosomatic Medicine of the Affiliated Hospital of Southwest Medical University from October 2020 to May 2023. Concurrently， 52 healthy individuals matched for age and gender were recruited from Luzhou as the control group. Beck Depression Inventory （BDI） and Beck Anxiety Inventory （BAI） were used for assessment， and decision-making behavior was evaluated using Probabilistic Reversal Learning （PRL） task. Indicators assessed included the number of trials to criterion， perseverative errors， win-stay rate and lose-shift rate. Spearman correlation analysis was used to assess the correlation between BDI and BAI scores and PRL task indicators. ResultsThe depression group showed a significantly higher lose-shift rate compared with the control group （t=3.684， P<0.01）. There were no statistically significant differences between two groups in trials to criterion， perseverative errors and win-stay rate （t=0.329， 0.132， 0.609， P>0.05）. In depression group， BDI and BAI scores were positively correlated with the win-stay rate（r=0.450， 0.398， P<0.01）. ConclusionPatients with depressive disorder are more likely to change their decision-making strategies following negative outcomes. Furthermore， the severity of depressive and anxiety symptoms is associated with a greater propensity to maintain existing decisions after receiving positive feedback. ［Funded by 2019 Joint Project of Luzhou Science and Technology Bureau-Southwest Medical University （number， 2019LZXNYDJ39］

OBJECTIVE To observe the effects of measures dominated by intensifying link quality on prevention and control of carbapenem-resistant Klebsiella pneumoniae(CRKP)among general ICU patients.METHODS The conventional intervention measures were taken from Jan.2016 to Oct.2020,which was assigned as the pre-inter-vention period;the intervention measures dominated by enhancement of link quality were taken for the general ICU patients(the study group)from Nov.2020 to Oct.2021,which was set as the intervention period,the inter-vention measures were taken for the neurosurgery ICU patients and the surgery ICU patients(the control group).A two-group interrupted time series of a quasi-experimental study was analyzed,and whether or not the intervention measures could reduce the incidence of CRKP hospital-associated infection among the general ICU patients was evaluated.RESULTS A total of 23 713 case-times of patients from three ICU wards were enrolled in the study.The incidence of CRKP hospital-associated infection among the general ICU patients was 4.49 cases per thousand patient-days during intervention period,3.71 cases per thousand patient-days during pre-intervention pe-riod,and there was no significant difference(u=-1.412,P=0.079);it was lower than 7.06 cases per thousand patient-days during nearly one year before the intervention period(from Nov.2019 to Oct.2020),and there was significant difference(u=3.128,P=0.002).The result of analysis of the interrupted time series showed that there was significant difference in the slope change between the study group and the control group after the inter-vention(P=0.035),indicating that the intervention measures could reduce the incidence of CRKP hospital-associ-ated infection in the study group,and there was significant difference in the changing trend between the study group and the control group.CONCLUSIONS The prevention and control measures for CRKP dominated by the link quality management can effectively reduce the incidence of CRKP hospital-associated infection among the gen-eral ICU patients.The prevention and control strategies may provide reference for prevention and control of CRKP in ICUs of other medical institutions.

Protein lysine β-hydroxybutyrylation (Kbhb) is a newly discovered post-translational modification associated with a wide range of biological processes. Identifying Kbhb sites is critical for a better understanding of its mechanism of action. However, biochemical experimental methods for probing Kbhb sites are costly and have a long cycle. Therefore, a feature embedding learning method based on the Transformer encoder was proposed to predict Kbhb sites. In this method, amino acid residues were mapped into numerical vectors according to their amino acid class and position in a learnable feature embedding method. Then the Transformer encoder was used to extract discriminating features, and the bidirectional long short-term memory network (BiLSTM) was used to capture the correlation between different features. In this paper, a benchmark dataset was constructed, and a Kbhb site predictor, AutoTF-Kbhb, was implemented based on the proposed method. Experimental results showed that the proposed feature embedding learning method could extract effective features. AutoTF-Kbhb achieved an area under curve (AUC) of 0.87 and a Matthews correlation coefficient (MCC) of 0.37 on the independent test set, significantly outperforming other methods in comparison. Therefore, AutoTF-Kbhb can be used as an auxiliary means to identify Kbhb sites.
Protein Processing, Post-Translational ; Lysine/chemistry* ; Proteins/chemistry* ; Machine Learning ; Algorithms

OBJECTIVE: To explore the accuracy of human-computer interaction software in identifying and locating type C1 distal radius fractures. METHODS: Based on relevant inclusion and exclusion criteria, 14 cases of type C1 distal radius fractures between September 2023 and March 2024 were retrospectively analyzed, comprising 3 males and 11 females(aged from 27 to 82 years). The data were assigned randomized identifiers. A senior orthopedic physician reviewed the films and measured the ulnar deviation angle, radial height, palmar inclination angle, intra-articular step, and intra-articular gap for each case on the hospital's imaging system. Based on the reduction standard for distal radius fractures, cases were divided into reduction group and non-reduction group. Then, the data were sequentially imported into a human-computer interaction intelligent software, where a junior orthopedic physician analyzed the same radiological parameters, categorized cases, and measured fracture details. The categorization results from the software were consistent with manual classifications (6 reduction cases and 8 non-reduction cases). For non-reduction cases, the software performed further analyses, including bone segmentation and fracture recognition, generating 8 diagnostic reports containing fracture recognition information. For the 6 reduction cases, the senior and junior orthopedic physicians independently analyzed the data on the hospital's imaging system and the AI software, respectively. Bone segments requiring reduction were identified, verified by two senior physicians, and measured for displacement and rotation along the X (inward and outward), Z (front and back), and Y (up and down) axes. The AI software generated comprehensive diagnostic reports for these cases, which included all measurements and fracture recognition details. RESULTS: Both the manual and AI software methods consistently categorized the 14 cases into 6 reduction and 8 non-reduction groups, with identical data distributions. A paired sample t-test revealed no statistically significant differences (P>0.05) between the manual and software-based measurements for ulnar deviation angle, radial ulnar bone height, palmar inclination angle, intra-articular step, and joint space. In fracture recognition, the AI software correctly identified 10 C-type fractures and 4 B-type fractures. For the 6 reduction cases, a total of 24 bone fragments were analyzed across both methods. After verification, it was found that the bone fragments identified by the two methods were consistent. A paired sample t-tests revealed that the identified bone fragments and measured displacement and rotation angles along the X, Y, and Z axes were consistent between the two methods. No statistically significant differences(P>0.05) were found between manual and software measurements for these parameters. CONCLUSION Human-computer interaction software employing AI technology demonstrated comparable accuracy to manual measurement in identifying and locating type C1 distal radius fractures on CT imaging.
Humans ; Male ; Female ; Radius Fractures/surgery* ; Middle Aged ; Adult ; Aged ; Aged, 80 and over ; Tomography, X-Ray Computed/methods* ; Retrospective Studies ; Software ; Wrist Fractures

Objective To investigate the efficacy of varying doses of methylprednisolone(MP)on mice with acute exacerbations of chronic obstructive pulmonary disease(AECOPD)induced with influenza A virus(IAV).Methods Mouse model of COPD was established using LPS combined with smoking for 12 weeks,and then these COPD mice were treated with administration of 40 μL IAV via nasal drip to establish a AECOPD model.A total of 15 AECOPD mice were randomly divided into low-,medium-and high-dose MP groups,oseltamivir group and blank group.The body weight and survival time were monitored within 10 d after IAV infection.On days 1,3,and 5 post-treatment,lung function was assessed using whole-body plethysmography(WBP),inflammatory factors in bronchoalveolar lavage fluid(BALF)were quantified with ELISA,viral titers in BALF were determined using plaque assays,and colony-forming units were evaluated with blood agar plates.Immunofluorescence analysis:① Pulmonary immunofluorescence assay:Mice were randomly categorized into(n=4):LPS 1-day group,LPS 3-day group,and LPS+MP treatment group.All groups received an initial dose of LPS via atomization;subsequently,the LPS+MP treatment group received a single gavage dose of MP.Lung tissues were harvested from the 1-day LPS group on 1 d post-treatment,and from the 3-day LPS and LPS+MP groups on 3 d for immunofluorescence staining.② Cellular immunofluorescence assay:Mouse bone marrow neutrophils were classified into blank control(no intervention),LPS stimulation(LPS group),MP intervention with LPS stimulation(LPS+MP group),and MP intervention alone(MP group).The above cells were collected in 4 h after corresponding interventions for subsequent cellular immunofluorescence analysis.Results ①The medium-dose MP group demonstrated the most significant improvement in survival rate,weight recovery,and lung function when compared to other groups(P＜0.05).② Treatment of medium-dose MP obviously reduced the levels of IL-6 and neutrophil extracellular traps(NETs)(P＜0.05),while,elevated inflammatory factors and NETs were observed in the high-dose MP group on day 5 post-treatment.③ Notable decline in the lung injury score was found in the medium-dose MP group than the other groups(P＜0.05).④The high-dose MP group exhibited substantial bacterial proliferation and delayed viral clearance since day 5 after treatment.Conclusion Medium-dose MP shows best efficacy in treatment of IAV-induced AECOPD,and the dose neither delays viral clearance nor increases the risk of bacterial infection following viral infection.