AIM:To assess the efficacy of intravitreal conbercept for treating neovascular age-related macular degeneration(nAMD)under a treat-and-extend(T & E)regimen.METHODS: A retrospective analysis was conducted on nAMD patients followed over a 2-year period(May 2020 to May 2022). All eyes received three monthly loading intravitreal injections of conbercept, followed by a T& E regimen in which the injection interval was extended by 2 or 4 wk according to disease activity, up to a maximum of 16 wk. When disease activity recurred, the interval was shortened. Patients were divided into initial and non-initial treatment groups based on treatment history. Best-corrected visual acuity(BCVA), central macular thickness(CMT), injection frequency, and intervals between injections over the 24-month follow-up were compared.RESULTS:Totally 27 patients(15 males and 12 females, 33 eyes)were enrolled. In the initial treatment group(18 eyes, mean age 65.72±12.32 y), BCVA significantly improved at 1, 3, and 6 mo(P<0.05), and CMT significantly improved at 1 and 3 mo(P<0.05). In the non-initial treatment group(15 eyes, mean age 69.00±9.21 y), BCVA improved significantly at 3 mo(P<0.05), whereas CMT remained stable(P >0.05). Baseline CMT was similar between the groups(P>0.05). However, significant differences were observed at multiple post-injection time points(P<0.05). The total number of injections did not differ between the groups(P>0.05). Intervals between injections varied, with the majority at 4 and 3-4 mo in the initial and non-initial treatment groups, respectively.CONCLUSION:Initiating intravitreal conbercept therapy under a T & E regimen results in superior visual and anatomical outcomes compared with non-initial treatment.

Objective:To screen the Alzheimer's disease(AD)-related genes and construct its diagnostic model using bioinformatics technology and machine learning(ML)algorithms,to discuss the immunological characteristics of AD patients,and to provide novel biomarkers for AD diagnosis.Methods:The AD-related gene expression dataset GSE125583 was downloaded from the Gene Expression Omnibus(GEO)database.Differentially expressed genes(DEGs)were identified through differential analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analyses were performed to explore the biological functions and signaling pathways of DEGs.A protein-protein interaction(PPI)network was constructed,and hub genes were screened using Cytoscape software combined with three ML algorithms:Least Absolute Shrinkage and Selection Operator(LASSO),eXtreme Gradient Boosting(XGBoost),and Random Forest(RF).The screened hub genes were utilized to build an AD diagnostic model via RF,followed by feature importance ranking.The model's efficacy and key genes were evaluated using a test set.Single-sample gene set enrichment analysis(ssGSEA)was used for immune cell infiltration analysis between AD group and control group.Results:Differential analysis identified 1 287 DEGs.The GO functional enrichment analysis results revealed that DEGs were primarily involved in biological functions related to neural signaling,synapses,and vesicles.KEGG signaling pathway enrichment analysis indicated significant enrichment of DEGs in ion transport,neurotransmitter,and ligand-gated channel pathways.Nine overlapping hub genes were screened by the three ML algorithms.In the AD diagnostic model,the top four key genes with highest diagnostic performance were adenylate cyclase-activating polypeptide 1(ADCYAP1),brain-derived neurotrophic factor(BDNF),platelet-derived growth factor receptor β(PDGFRB),and C-X-C motif chemokine receptor 4(CXCR4),with corresponding area under the curve(AUC)values of 0.852,0.795,0.820,and 0.756,respectively.The model achieved an AUC of 0.828,accuracy of 81.25%,sensitivity of 84.40%,and specificity of 71.43%.The immune cell infiltration analysis results demonstrated higher infiltration of macrophages,monocytes,natural killer(NK)cells,and lymphocytes in AD tissue.Among these,NK/natural killer T(NKT)cells and plasmacytoid dendritic cells showed significant correlations with the four key genes(P<0.05).Conclusion:The feature genes screened based on bioinformatics and ML exhibit diagnostic potential for AD.Genes such as ADCYAP1 may serve as potential biomarkers for AD diagnosis,offering significant implications for early prevention and treatment.

This study was aimed at investigating differentially expressed genes(DEGs)in Clonorchis sinensis(C.sinensis)meta-cercariae and newly excysted juveniles(NEJs)cultured in vitro for 1 hour or 3 hours,through transcriptomic analysis.Our objective was to explore the mechanisms underlying host invasion.Metacercariae were digested and isolated from Pseudorasbora parva infected with C.sinensis.The metacercariae excysted and developed into NEJs in vitro.Subsequently,the mRNA of metacercariae and NEJs cultured in vitro for 1 hour or 3 hours was extracted for transcriptomic sequencing analysis to screen for DEGs,and to conduct GO and KEGG analyses.A protein-protein interaction network(PPI)was constructed to identify hub genes.A total of 1 218 DEGs were de-tected.The main enriched GO terms of DEGs included transcription regulator activity and gated channel activity(primarily K+).The KEGG pathways significantly enriched in DEGs included cholesterol metabolism,lysosome,synthesis,secretion,and action of para-thyroid hormone.ZFAND4-2,BIRC6,and other genes were screened and identified as hub genes through PPI network analysis.Addi-tionally,abundant differential expression of cathepsin-related genes,including Cathepsin L and Cathepsin F,were observed before and after excystment in C.sinensis.Therefore,significant transcriptional level changes occurred in the metacercariae of C.sinensis be-fore and after excystation,and enrichment was observed primarily in signaling pathways,such as activation of growth and material me-tabolism,that regulate parasite growth and development.Meanwhile,biological events conducive to parasite invasion,migration,and adhesion were triggered.

Objective:To investigate the predictive value of machine learning-based CT radiomics model for radioresistance in patients with esophageal squamous cell carcinoma (ESCC).Methods:Clinical data of 185 patients with ESCC treated with radical radiotherapy in the First Affiliated Hospital of Anhui Medical University from December 2015 to July 2022 were retrospectively analyzed, and all patients were randomly divided into a training set ( n=129) and a validation set ( n=56) at a ratio of 7 : 3. The radiomics parameters of the primary lesion of esophageal cancer and the surrounding 5 cm region in the patients' CT arterial phase images were extracted, and 6 machine learning methods were used to screen the optimal radiomics model to obtain the optimal radiomics score (Radscore). Independent prognostic predictors of radioresistance in ESCC were obtained by univariate and multivariate logistic regression analyses, which was used as the basis for constructing the nomogram. The predictive performance of different models was compared by the area under the receiver operating characteristic (ROC) curve (AUC). The predictive efficacy and clinical value of the combined model were evaluated using calibration curve, decision curve analysis and clinical impact curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results:The combined intratumoral and peritumoral radiomics model based on naive Bayesian classifier yielded the optimal prediction performance, with AUC of 0.859 and 0.936 in the training set and validation set, respectively. Multivariate logistic regression analysis showed that Radscore and T stage were the independent prognostic predictors of radioresistance in ESCC patients, and the AUC of the combined model constructed based on these predictors in the training and validation sets were 0.942 and 0.959, respectively. Calibration curve, decision curve analysis and clinical impact curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) all indicated higher clinical benefit and more consistent predictive efficacy of the combined model.Conclusions:Machine learning-based CT radiomics model is useful for the prediction of radioresistance in ESCC. The nomogram of radiomics and clinical parameters can further improve the prediction accuracy and provide novel reference for individualized treatment of patients with ESCC.

Objective:To explore the predictive value of models based on clinical parameters, deep learning radiomics (DLR) from CT images, and traditional handcrafted radiomics (HCR) in assessing pathological complete response (pCR) after neoadjuvant radiotherapy combined with medical therapy in patients with esophageal cancer.Methods:A retrospective study was conducted on 130 patients with locally advanced esophageal cancer who underwent neoadjuvant radiotherapy combined with medical therapy followed by surgery at the First Affiliated Hospital of the University of Science and Technology of China from August 1, 2018, to August 31, 2024. Patients were randomly divided into a training set ( n=91) and a validation set ( n=39) at a ratio of 7:3. Logistic regression analysis was performed to identify clinical independent risk factors associated with pCR. DLR and HCR features were extracted from the tumor and the 5 mm peritumoral region on planning CT images. Features for modeling were selected using t-test, Mann-Whitney U test or Fisher exact probability method, least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (Rad-score). A nomogram was then constructed by integrating the clinical risk factors. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) to assess clinical benefits. Results:Multivariate logistic regression analysis identified body weight ( OR=1.101, 95% CI: 1.029-1.177, P=0.005) and lymph node positivity ( OR=0.100, 95% CI: 0.014-0.727, P=0.023) as independent predictors of pCR. The peritumoral DLR-HCR model showed superior predictive performance, with AUCs of 0.870 (95% CI: 0.799-0.942) in the training set and 0.866 (95% CI: 0.750-0.982) in the validation set. The combined model incorporating clinical parameters achieved the best performance, with AUCs of 0.903 (95% CI: 0.845-0.962) and 0.888 (95% CI: 0.782-0.994) in the training and validation sets, respectively. Conclusions:The combined model integrating peritumoral DLR-HCR features with clinical parameters provides excellent predictive value for pCR after neoadjuvant radiotherapy combined with medical therapy in esophageal cancer and offers valuable guidance for personalized treatment strategies.

BACKGROUND: Severe stroke has high rates of mortality and morbidity. This study aimed to investigate the clinical course, causes of worsening, and outcomes of severe ischemic stroke. METHODS: This prospective, multicenter cohort study enrolled adult patients admitted ≤30 days after ischemic stroke from nine hospitals in China between September 2017 and December 2019. Severe stroke was defined as a score of ≥15 on the National Institutes of Health Stroke Scale (NIHSS). Clinical worsening was defined as an increase of 4 in the NIHSS score from baseline. Unfavorable functional outcome was defined as a modified Rankin scale score ≥3 at 3 months and 1 year after stroke onset, respectively. We performed Logistic regression to explore baseline features and reperfusion therapies associated with clinical worsening and functional outcomes. RESULTS: Among 4201 patients enrolled, 854 patients (20.33%) had severe stroke on admission. Of 3347 patients without severe stroke on admission, 142 (4.24%) patients developed severe stroke in hospital. Of 854 patients with severe stroke on admission, 33.95% (290/854) experienced clinical worsening (median time from stroke onset: 43 h, Q1-Q3: 20-88 h), with brain edema (54.83% [159/290]) as the leading cause; 24.59% (210/854) of these patients died by 30 days, and 81.47% (677/831) and 78.44% (633/807) had unfavorable functional outcomes at 3 months and 1 year respectively. Reperfusion reduced the risk of worsening (adjusted odds ratio [OR]: 0.24, 95% confidence interval [CI]: 0.12-0.49, P  <0.01), 30-day death (adjusted OR: 0.22, 95% CI: 0.11-0.41, P  <0.01), and unfavorable functional outcomes at 3 months (adjusted OR: 0.24, 95% CI: 0.08-0.68, P <0.01) and 1 year (adjusted OR: 0.17, 95% CI: 0.06-0.50, P  <0.01). CONCLUSIONS: Approximately one-fifth of patients with ischemic stroke had severe neurological deficits on admission. Clinical worsening mainly occurred in the first 3 to 4 days after stroke onset, with brain edema as the leading cause of worsening. Reperfusion reduced the risk of clinical worsening and improved functional outcomes. REGISTRATION ClinicalTrials.gov , NCT03222024.
Humans ; Male ; Female ; Prospective Studies ; Ischemic Stroke/mortality* ; Aged ; Middle Aged ; Aged, 80 and over ; Stroke ; Brain Ischemia

This study was aimed at investigating differentially expressed genes(DEGs)in Clonorchis sinensis(C.sinensis)meta-cercariae and newly excysted juveniles(NEJs)cultured in vitro for 1 hour or 3 hours,through transcriptomic analysis.Our objective was to explore the mechanisms underlying host invasion.Metacercariae were digested and isolated from Pseudorasbora parva infected with C.sinensis.The metacercariae excysted and developed into NEJs in vitro.Subsequently,the mRNA of metacercariae and NEJs cultured in vitro for 1 hour or 3 hours was extracted for transcriptomic sequencing analysis to screen for DEGs,and to conduct GO and KEGG analyses.A protein-protein interaction network(PPI)was constructed to identify hub genes.A total of 1 218 DEGs were de-tected.The main enriched GO terms of DEGs included transcription regulator activity and gated channel activity(primarily K+).The KEGG pathways significantly enriched in DEGs included cholesterol metabolism,lysosome,synthesis,secretion,and action of para-thyroid hormone.ZFAND4-2,BIRC6,and other genes were screened and identified as hub genes through PPI network analysis.Addi-tionally,abundant differential expression of cathepsin-related genes,including Cathepsin L and Cathepsin F,were observed before and after excystment in C.sinensis.Therefore,significant transcriptional level changes occurred in the metacercariae of C.sinensis be-fore and after excystation,and enrichment was observed primarily in signaling pathways,such as activation of growth and material me-tabolism,that regulate parasite growth and development.Meanwhile,biological events conducive to parasite invasion,migration,and adhesion were triggered.

Objective:To investigate the predictive value of machine learning-based CT radiomics model for radioresistance in patients with esophageal squamous cell carcinoma (ESCC).Methods:Clinical data of 185 patients with ESCC treated with radical radiotherapy in the First Affiliated Hospital of Anhui Medical University from December 2015 to July 2022 were retrospectively analyzed, and all patients were randomly divided into a training set ( n=129) and a validation set ( n=56) at a ratio of 7 : 3. The radiomics parameters of the primary lesion of esophageal cancer and the surrounding 5 cm region in the patients' CT arterial phase images were extracted, and 6 machine learning methods were used to screen the optimal radiomics model to obtain the optimal radiomics score (Radscore). Independent prognostic predictors of radioresistance in ESCC were obtained by univariate and multivariate logistic regression analyses, which was used as the basis for constructing the nomogram. The predictive performance of different models was compared by the area under the receiver operating characteristic (ROC) curve (AUC). The predictive efficacy and clinical value of the combined model were evaluated using calibration curve, decision curve analysis and clinical impact curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results:The combined intratumoral and peritumoral radiomics model based on naive Bayesian classifier yielded the optimal prediction performance, with AUC of 0.859 and 0.936 in the training set and validation set, respectively. Multivariate logistic regression analysis showed that Radscore and T stage were the independent prognostic predictors of radioresistance in ESCC patients, and the AUC of the combined model constructed based on these predictors in the training and validation sets were 0.942 and 0.959, respectively. Calibration curve, decision curve analysis and clinical impact curve, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) all indicated higher clinical benefit and more consistent predictive efficacy of the combined model.Conclusions:Machine learning-based CT radiomics model is useful for the prediction of radioresistance in ESCC. The nomogram of radiomics and clinical parameters can further improve the prediction accuracy and provide novel reference for individualized treatment of patients with ESCC.

Objective:To explore the predictive value of models based on clinical parameters, deep learning radiomics (DLR) from CT images, and traditional handcrafted radiomics (HCR) in assessing pathological complete response (pCR) after neoadjuvant radiotherapy combined with medical therapy in patients with esophageal cancer.Methods:A retrospective study was conducted on 130 patients with locally advanced esophageal cancer who underwent neoadjuvant radiotherapy combined with medical therapy followed by surgery at the First Affiliated Hospital of the University of Science and Technology of China from August 1, 2018, to August 31, 2024. Patients were randomly divided into a training set ( n=91) and a validation set ( n=39) at a ratio of 7:3. Logistic regression analysis was performed to identify clinical independent risk factors associated with pCR. DLR and HCR features were extracted from the tumor and the 5 mm peritumoral region on planning CT images. Features for modeling were selected using t-test, Mann-Whitney U test or Fisher exact probability method, least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (Rad-score). A nomogram was then constructed by integrating the clinical risk factors. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) to assess clinical benefits. Results:Multivariate logistic regression analysis identified body weight ( OR=1.101, 95% CI: 1.029-1.177, P=0.005) and lymph node positivity ( OR=0.100, 95% CI: 0.014-0.727, P=0.023) as independent predictors of pCR. The peritumoral DLR-HCR model showed superior predictive performance, with AUCs of 0.870 (95% CI: 0.799-0.942) in the training set and 0.866 (95% CI: 0.750-0.982) in the validation set. The combined model incorporating clinical parameters achieved the best performance, with AUCs of 0.903 (95% CI: 0.845-0.962) and 0.888 (95% CI: 0.782-0.994) in the training and validation sets, respectively. Conclusions:The combined model integrating peritumoral DLR-HCR features with clinical parameters provides excellent predictive value for pCR after neoadjuvant radiotherapy combined with medical therapy in esophageal cancer and offers valuable guidance for personalized treatment strategies.

Background::Magnetic resonance (MR)-guided ultra-hypofractionated radiotherapy with whole-pelvic irradiation (UHF-WPRT) is a novel approach to radiotherapy for patients with high-risk (HR) and very high-risk (VHR) prostate cancer (PCa). However, the inherent complexity of adaptive UHF-WPRT might inevitably result in longer on-couch time. We aimed to estimate the delivered dose, study the feasibility and safety of adaptive UHF-WPRT on a 1.5-Tesla MR-Linac.Methods::Ten patients with clinical stage T3a-4N0-1M0-1c PCa, who consecutively received UHF-WPRT, were enrolled prospectively. The contours of the target and organ-at-risks on the position verification-MR (PV-MR), beam-on 3D-MR(Bn-MR), and post-MR (after radiotherapy delivery) were derived from the pre-MR data by deformable image registration. The physician then manually adjusted them, and dose recalculation was performed accordingly. GraphPad Prism 9 (GraphPad Prism Software Inc.) was utilized for conducting statistical analyses.Results::In total, we collected 188 MR scans (50 pre-MR, 50 PV-MR, 44 Bn-MR, and 44 post-MR scans). With median 59 min, the mean prostate clinical target volume (CTV)-V 100% was 98.59% ± 2.74%, and the mean pelvic CTVp-V 100% relative percentages of all scans was 99.60% ± 1.18%. The median V 29 Gy change in the rectal wall was -2% (-18% to 20%). With a median follow-up of 9 months, no patient had acute Common Terminology Criteria for Adverse Events (CTCAE) grade 2 or more severe genitourinary (GU) or gastrointestinal (GI) toxicities (0%). Conclusion::UHF-RT to the prostate and the whole pelvis with concomitant boost to positive nodes using an Adapt-To-Shape (ATS) workflow was technically feasible for patients with HR and VHR PCa, presenting only mild GU and GI toxicities. The estimated target dose during the beam-on phase was clinically acceptable based on the 3D-MR–based dosimetry analysis.Clinical trial registration::Chinese Clinical Trial Registry ChiCTR2000033382.