Prostate cancer is a prevalent malignant tumor of the male genitourinary system in China, with a persistently elevated incidence rate.Given the insidious onset, propensity for metastasis in advanced stages, and poor efficacy of traditional treatments, new hormone therapies have garnered increasing attention in recent years.A number of clinical studies have demonstrated that second-generation androgen receptor inhibitors (SGARIs), including enzalutamide, apalutamide, darolutamide, and rezvilutamide, can be beneficial in different stages of advanced prostate cancer.Furthermore, their early application has been shown to delay disease progression while maintaining a controlled safety profile.At present, SGARIs have increasingly become the primary therapeutic agents for advanced prostate cancer in various stages.This article reviews the clinical research progress of SGARIs in recent years, with the aim of offering valuable clinical reference for clinicians.

Synthetic biology is an emerging interdisciplinary field at the convergence of biology, engineering, and computer science. It employs a bottom-up approach to progressively design biological parts, devices, and circuits, aiming to create artificial biological systems not found in nature or to redesign existing biological systems for specific purposes. With the rapid development of the synthetic biology industry, there is an increasing demand for large complex genetic circuits. However, the traditional trial-and-error methods, heavily reliant on empirical knowledge, have limited efficiency and success rates of parts/circuits construction, thereby impeding the innovation and technology translation for synthetic biology. These limitations have prompted a paradigm shift from labor-intensive, experience-driven trial-and-error models towards standardized, intelligent engineering approaches. Machine learning, capable of uncovering hidden structures and relationships within biological data, offers robust support for the intelligent design of synthetic biological parts and genetic circuits. Here, we review commonly used machine learning algorithms and analyze their typical applications in designing biological parts (e.g., synthetic promoters, RNA regulatory elements, and transcription factors) and simple genetic circuits. Additionally, we discuss the primary challenges in machine learning-aided design and propose potential solutions. Lastly, we envision the future trend of integrating machine learning with synthetic biological system design, highlighting the importance of interdisciplinary collaboration.
Synthetic Biology/methods* ; Machine Learning ; Gene Regulatory Networks ; Algorithms

Background: and Purpose Guillain–Barré syndrome (GBS) is an autoimmune-mediated disorder characterized by demyelinating or axonal injury of the peripheral nerve. Our aim is to determine whether serum amyloid A (SAA) is a biomarker of demyelinating injury and disease severity in patients with GBS. Methods: This study retrospectively enrolled 40 patients with either the demyelinating or axonal GBS and sex- and age-matched controls with other neurological diseases as well as healthy subjects. The demographic and clinical features at entry were collected. The serum levels of the SAA isoforms SAA1, SAA2, and SAA4 were determined in the patients with GBS and the controls using the enzyme-linked immunosorbent assay and analyzed for the associations between levels of different SAA isoforms and the clinical features of the patients. Results: The levels of SAA2 and SAA4 were significantly higher in patients with GBS than in both the other neurological disease controls and the healthy subjects (p<0.05 for all). The level of SAA1 did not differ between patients with GBS and the controls. The level of SAA2 was considerably higher in GBS patients with antecedent infection than in those without infection (p=0.020). The levels of different SAA isoforms were not associated with the disease severity or other clinical features of patients with GBS (p>0.05 for all). Conclusions Increased levels of SAA2 and SAA4 may only represent the acute inflammatory status and so cannot be utilized as biomarkers of the disease severity or demyelinating injury in patients with GBS.

Background: and Purpose Guillain–Barré syndrome (GBS) is an autoimmune-mediated disorder characterized by demyelinating or axonal injury of the peripheral nerve. Our aim is to determine whether serum amyloid A (SAA) is a biomarker of demyelinating injury and disease severity in patients with GBS. Methods: This study retrospectively enrolled 40 patients with either the demyelinating or axonal GBS and sex- and age-matched controls with other neurological diseases as well as healthy subjects. The demographic and clinical features at entry were collected. The serum levels of the SAA isoforms SAA1, SAA2, and SAA4 were determined in the patients with GBS and the controls using the enzyme-linked immunosorbent assay and analyzed for the associations between levels of different SAA isoforms and the clinical features of the patients. Results: The levels of SAA2 and SAA4 were significantly higher in patients with GBS than in both the other neurological disease controls and the healthy subjects (p<0.05 for all). The level of SAA1 did not differ between patients with GBS and the controls. The level of SAA2 was considerably higher in GBS patients with antecedent infection than in those without infection (p=0.020). The levels of different SAA isoforms were not associated with the disease severity or other clinical features of patients with GBS (p>0.05 for all). Conclusions Increased levels of SAA2 and SAA4 may only represent the acute inflammatory status and so cannot be utilized as biomarkers of the disease severity or demyelinating injury in patients with GBS.

Background: and Purpose Guillain–Barré syndrome (GBS) is an autoimmune-mediated disorder characterized by demyelinating or axonal injury of the peripheral nerve. Our aim is to determine whether serum amyloid A (SAA) is a biomarker of demyelinating injury and disease severity in patients with GBS. Methods: This study retrospectively enrolled 40 patients with either the demyelinating or axonal GBS and sex- and age-matched controls with other neurological diseases as well as healthy subjects. The demographic and clinical features at entry were collected. The serum levels of the SAA isoforms SAA1, SAA2, and SAA4 were determined in the patients with GBS and the controls using the enzyme-linked immunosorbent assay and analyzed for the associations between levels of different SAA isoforms and the clinical features of the patients. Results: The levels of SAA2 and SAA4 were significantly higher in patients with GBS than in both the other neurological disease controls and the healthy subjects (p<0.05 for all). The level of SAA1 did not differ between patients with GBS and the controls. The level of SAA2 was considerably higher in GBS patients with antecedent infection than in those without infection (p=0.020). The levels of different SAA isoforms were not associated with the disease severity or other clinical features of patients with GBS (p>0.05 for all). Conclusions Increased levels of SAA2 and SAA4 may only represent the acute inflammatory status and so cannot be utilized as biomarkers of the disease severity or demyelinating injury in patients with GBS.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Objective To observe the predictability of the corneal stromal reduction with smart pulse technology-as-sisted transperitoneal photorefractive keratomileusis(SPT-TPRK)and femtosecond laser-assisted in situ keratomileusis(FS-LASIK).Methods Patients undergoing laser surgery in the Department of Ophthalmology of the First Affiliated Hos-pital of Xinxiang Medical College from February to September 2023 were selected and divided by surgical modalities into an SPT-TPRK group(21 cases,37 eyes)and an FS-LASIK group(18 cases,32 eyes).The uncorrected visual acuity(UCVA),intraocular pressure,corneal thickness,and corneal epithelial thickness were measured before surgery,1 week,1 month and 3 months after surgery.The cutting deviation was calculated,and the change of corneal stromal thickness was ob-served.The correlation between the predicted corneal stromal reduction and the cutting deviation was analyzed.Results There were significant differences in UCVA among different time points in both SPT-TPRK and FS-LASIK groups(all P＜0.001).The UCVA of patients in the FS-LASIK group was significantly higher than that in the SPT-TPRK group 1 week after surgery(P＜0.001).The difference in UCVA was not statistically significant between the two groups 1 month and 3 months after surgery(all P＞0.05).There were significant differences in intraocular pressure among different time points in both SPT-TPRK and FS-LASIK groups(all P＜0.001).The FS-LASIK group had a lower intraocular pressure than the SPT-TPRK group 1 week after surgery(P＜0.05),but the difference in intraocular pressure was not statistically significant between the two groups 1 month and 3 months after surgery(all P＞0.05).There was significant difference between predicted cor-neal stromal reductions and actual corneal stromal reductions measured at different time points postoperatively in both SPT-TPRK and FS-LASIK groups(all P＜0.001).The actual corneal stromal reductions were higher than the predicted ones in the SPT-TPRK group at all postoperative time points(all P＜0.05).In the FS-LASIK group,the actual corneal stromal re-ductions were higher than the predicted ones 1 week and 1 month after surgery(all P＜0.05),but the actual and predicted corneal stromal reductions were not significantly different 3 months after surgery(P＞0.05).The cutting deviations were not significantly different between SPT-TPRK and FS-LASIK groups 1 month after surgery(P＞0.05),while the cutting de-viations were significantly different between the two groups 1 week and 3 months after surgery(all P＜0.05).There were significant differences in the change of the stromal thickness among different time points in both SPT-TPRK and FS-LASIK groups(all P＜0.05).There was no significant difference in the change of the stromal thickness among different time points in the SPT-TPRK group(all P＞0.05).The stromal thickness showed greater changes 1 month after surgery than that 1 week after surgery in the FS-LASIK group(P＜0.05).There were no significant differences in the change of the stromal thickness between 1 month and 3 months after surgery in the FS-LASIK group(P＞0.05).Correlation analysis showed a positive correlation between the predicted corneal stromal reduction and the cutting deviation in the SPT-TPRK group 1 week after surgery(P＜0.05),but they had no correlation 1 month and 3 months after surgery(all P＞0.05).There was no correlation between the predicted corneal stromal reduction and the cutting deviation in the FS-LASIK group at all post-operative time points(all P＞0.05).Conclusion Patients who receive SPT-TPRK or FS-LASIK can both achieve good visual acuity and a reduction in intraocular pressure.FS-LASIK has better predictability in refractive error correction than SPT-TPRK.

Prostate cancer(PCa)is the most common malignant tumor of the male urinary and reproductive systems,with its incidence and mortality rates steadily increasing worldwide.Enhancing early detection of PCa and refining surgical treatment methods are crucial for improving cure rates,extending survival,and enhancing quality of life.Prostate biopsy remains the gold standard for diagnosing PCa,utilizing approaches such as transrectal,transperineal,and multi-imaging guided fusion-targeted techniques.Surgical intervention is a key treatment modality for PCa,with radical prostatectomy(RP)serving as the gold standard for managing localized disease.This paper reviews recent advancements in surgical techniques for prostate cancer,aiming to provide clinical practitioners with updated guidance.

The strategy of pelvic lymph node dissection in patients with localized intermediate- and high-risk prostate cancer（PCa）has been one of the research hotspots in recent years. This review synthesizes recent progress in clinical，diagnostic imaging and immunological aspects of pelvic lymph node dissection.It is found that the detection rate of metastatic lymph nodes in patients with localized intermediate-risk PCa is relatively low，and the survival benefit after dissection is not significant. PSMA PET/CT examination can be used instead of lymph node dissection for staging. Extended lymph node dissection in patients with localized high-risk PCa can achieve a higher detection rate of metastatic lymph nodes and possible survival benefits. However，excessive lymph node dissection can weaken the response ability to immunotherapy. The strategy for pelvic lymph node dissection in patients with localized intermediate-high-risk PCa needs further study.

Objective:This study investigated the independent risk factors for lymph node metastasis（LNM）in high-risk prostate cancer（HRPCa）patients undergoing robot-assisted radical prostatectomy（RARP），and constructed a nomogram model based on clinical data to improve the accuracy and clinical practicality of preoperative prediction of LNM.Methods:A retrospective analysis was conducted on the clinical data of 218 HRPCa patients who received RARP treatment at the First Medical Center of the PLA General Hospital from January 2020 to March 2025 as the modeling group. The age of the modeling group was（66.91±6.94）years old. 75 cases（34.40%）had a history of smoking，and 48 cases（22.02%）had a history of drinking. There were a body mass index（BMI）of 25.55（23.58，27.00）kg/m 2，a total prostate-specific antigen（tPSA）of 20.59（10.42，30.61）ng/ml，a free prostate-specific antigen（fPSA）of 1.87（1.04，3.26）ng/ml，a prostate volume（PV）of（41.19±21.00）ml，a prostate-specific antigen density（PSAD）of 0.52（0.30，0.84）ng/ml 2. Among the patients，60 cases（27.52%）had a preoperative biopsy Gleason score >8，and the percentage of positive biopsy cores（PPBC）was 50%（31%，80%）. Thirty-one patients（14.22%）were staged clinically as >T 2c. The diagnostic criteria for high-risk prostate cancer（HRPCa）were defined as meeting any one of the following：PSA >20 ng/ml，Gleason score on prostate biopsy ≥8，or clinical stage ≥T 3. Among the 218 patients in the modeling cohort，67 cases（30.73%）met two of the criteria，and 7 cases（3.21%）met all three criteria. All 218 patients underwent RARP，and based on postoperative pathology，they were divided into the LNM group and the non-LNM group. The relationship between the number of diagnostic criteria met and the occurrence of LNM was analyzed. An external validation cohort included 42 HRPCa patients who underwent RARP at the Third，Fifth Medical Centers of the PLA General Hospital between January 2023 and May 2025. Their mean age was（66.79±5.92）years. Eighteen patients（42.86%）had a smoking history，and nine（21.43%）had a history of alcohol consumption. The median BMI was 26.00（23.80，27.13）kg/m 2. The median tPSA level was 17.34（8.97，27.30）ng/ml. The median fPSA was 1.51（0.83，2.52）ng/ml，and the median PV was（35.57 ± 15.25）ml. The median PSAD was 0.57（0.23，0.87）ng/ml 2，and the median PPBC was 58%（36%，71%）. Three patients（7.14%）had a clinical stage >T 2c，and 12 patients（28.57%）had a Gleason score >8 on preoperative biopsy. Univariate and multivariate binary logistic regression analyses were used to identify independent risk factors for LNM，and a nomogram model was constructed based on these factors. The predictive performance of the model was evaluated using receiver operating characteristic（ROC）curves and calibration plots，and the model was validated in the external cohort. Result:According to postoperative pathology，45 patients were classified into the LNM group，and 173 into the non-LNM group. The probability of LNM increased proportionally with the number of diagnostic criteria met for HRPCa（meeting two criteria： OR = 4.762，95% CI 2.323-9.761， P < 0.01；meeting three criteria： OR = 10.667，95% CI 2.187-52.025， P=0.003）. Binary logistic regression analysis revealed that age（ OR=0.913，95% CI 0.859-0.971， P = 0.004），tPSA（ OR=1.039，95% CI 1.018-1.061， P<0.01），PPBC（ OR = 5.656，95% CI 1.101-29.056， P = 0.038），and clinical T stage（T 2c stage： OR=2.945，95% CI 0.888-9.769， P=0.077；>T 2c stage OR = 18.351，95% CI 4.790-70.306， P < 0.01）were independent risk factors for postoperative LNM in HRPCa patients after RARP. The ROC curve of the nomogram model based on these factors showed an area under the curve（AUC）of 0.853（95% CI 0.790-0.917）. In the external validation cohort，the nomogram achieved an AUC of 0.743（95% CI 0.556-0.929）. The calibration plots demonstrated good agreement between the predicted probabilities and actual observations. Conclusions:Age，tPSA，PPBC，and clinical T stage were independent predictors of postoperative LNM in HRPCa patients undergoing RARP. The greater the number of HRPCa diagnostic criteria met，the higher the likelihood of postoperative LNM. The nomogram developed in this study could effectively predict the risk of LNM in HRPCa patients after RARP.