Evaluations of secondary hypertension and laboratory data in the elderly population.

Secondary hypertension in the elderly poses many challenges and requires a comprehensive diagnostic and management approach. This review explores the prevalence, diagnostic strategies, and treatment modalities for secondary hypertension in elderly patients, focusing on etiologies including primary aldosteronism, renal vascular disease, renal parenchymal disease, obstructive sleep apnea, thyroid disorders, Cushing's syndrome, pheochromocytomas and paragangliomas, and drug-induced hypertension. Key considerations include age-related changes in physiology and atypical presentations of underlying conditions necessitating thorough screening with a combination of clinical evaluation, laboratory tests, and imaging studies. Collaboration among healthcare providers is essential to ensure a timely diagnosis and personalized management tailored to the unique needs of elderly patients. Further research is needed to address knowledge gaps and optimize clinical strategies for managing secondary hypertension in this population.

Comparative Anti-Cancer and Anti-Inflammatory Activities of Essential Oils from the Bark and Flower of Magnolia officinalis Rehd. et Wils.

This study was designed to compare the antioxidant, antitumor and anti-inflammatory effects of essential oils from the bark and flower of Magnolia officinalis Rehd. et Wils. Distillation extraction and steam distillation were used to extract EOs from the bark and flower. The results showed that the contents of EOs of SDE-F and SDE-B were much higher than that of SD-F and SD-B. EOs from the bark were rich in eudesmol (especially α-eudesmol) and exhibited a stronger antioxidant effect than the flower. The anti-tumor effects of SD-B and SD-F on HepG2 and MDA-MB-231 cells were better than that of SDE-B and SDE-F. The inhibitory rates of SD-B and SD-F on MDA-MB-231 cells were 59.21% and 48.27%, exceeding that of positive control 5-fluorouracil (47.04%) at 50 µg/mL. All four EOs exhibited excellent anti-inflammatory activities through the regulation of nitric oxide production and pro-inflammation cytokines in LPS-induced RAW 264.7 cells and they also remarkably suppressed the mRNA expressions of nitric oxide synthase, IL-6 and TNF-α at the concentration higher than that of positive control dexamethasone. These results indicated significant differences in the composition, and anti-inflammatory and anti-tumor activities of EOs extracted by different methods and provided a theoretical basis for their development and utilization.

Incidence and progression of diabetic retinopathy in patients treated with glucagon-like peptide-1 receptor agonists versus sodium-glucose cotransporter 2 inhibitors: A population-based cohort study.

AIM: Glucagon-like peptide 1 receptor agonists (GLP1RA) and sodium-glucose cotransporter 2 inhibitors (SGLT2i) are both recommended for patients with diabetes, yet their effects on the development or progression of diabetic retinopathy (DR) are largely unknown. METHODS: In this retrospective cohort study, data were collected from a nationwide database. Patients with diabetes who initiated treatment with a GLP1RA or SGLT2i between 1 May 2016 and 31 December 2017, were identified. Patients were divided into those with or without a previous diagnosis of DR and then categorized into the GLP1RA and the SGLT2i groups according to drug use. The primary outcome of interest in the DR group was the composite of new-onset proliferative DR, vitreous haemorrhage and tractional retinal detachment (RD). In the non-DR group, the primary outcome was the composite of newly diagnosed DR of any severity, vitreous haemorrhage and RD. RESULTS: In total, 97 413 patients were identified. After matching, 1517 patients were treated with a GLP1RA and 3034 with an SGLT2i in the DR cohort. In the non-DR cohort, 9549 initiated a GLP1RA and 19 098 initiated an SGLT2i. In patients with pre-existing DR, the incidence of any DR progression event was significantly higher in the GLP1RA group than the SGLT2i group (subdistribution hazard ratio 1.50, 95% confidence interval 1.01-2.23), primarily because of the increased risk of tractional RD. In patients without DR at baseline, the risks of all ocular outcomes were similar between the GLP1RA and SGLT2i groups. CONCLUSIONS: In patients with diabetes mellitus and established DR, GLP1RA treatment was associated with increased risks of DR progression compared with SGLT2i use.

SFN promotes renal fibrosis via binding with MYH9 in chronic kidney disease.

Chronic kidney disease (CKD) is a clinical syndrome characterized by persistent renal function decline. Renal fibrosis is the main pathological process in CKD, but an effective treatment does not exist. Stratifin (SFN) is a highly-conserved, multi-function soluble acidic protein. Therefore, this study explored the effects of SFN on renal fibrosis. First, we found that SFN was highly expressed in patients with CKD, as well as in renal fibrosis animal and cell models. Next, transforming growth factor-beta 1 (TGF-ß1) induced injury and fibrosis in human renal tubule epithelial cells, and SFN knockdown reversed these effects. Furthermore, SFN knockdown mitigated unilateral ureteral obstruction (UUO)-induced renal tubular dilatation and renal interstitial fibrosis in mice. Liquid chromatography-tandem mass spectrometry/mass spectrometry (LC-MS/MS), co-immunoprecipitation (Co-IP), and immunofluorescence co-localization assays demonstrated that SFN bound the non-muscle myosin-encoding gene, myosin heavy chain 9 (MYH9), in the cytoplasm of renal tubular epithelial cells. MYH9 knockdown also reduced Col-1 and α-SMA expression, which are fibrosis markers. Finally, silencing SFN decreased MYH9 expression, alleviating renal fibrosis. These results suggest that SFN promotes renal fibrosis in CKD by interacting with MYH9. This study may provide potential strategies for the treatment of CKD.

Neoadjuvant therapy of sequential TACE, camrelizumab, and apatinib for single huge hepatocellular carcinoma (NEO-START): study protocol for a randomized controlled trial.

BACKGROUND: The high recurrence rate after liver resection emphasizes the urgent need for neoadjuvant therapy in hepatocellular carcinoma (HCC) to enhance the overall prognosis for patients. Immune checkpoint inhibitors, camrelizumab combined with an anti-angiogenic tyrosine kinase inhibitor (TKI) apatinib, have emerged as a first-line treatment option for patients with unresectable HCC, yet its neoadjuvant application in combination with transarterial chemoembolization (TACE) in HCC remains unexplored. Therefore, this study aims to investigate the efficacy and safety of sequential TACE, camrelizumab, and apatinib as a neoadjuvant therapy for single, huge HCC. METHODS: This multi-center, open-label randomized phase 3 trial will be conducted at 7 tertiary hospitals. Patients with single huge (≥ 10 cm in diameter), resectable HCC will be randomly assigned in a 1:1 ratio to arm of surgery alone or arm of neoadjuvant therapy followed by surgery. In the neoadjuvant therapy group, patients will receive TACE within 1 week after randomization, followed by camrelizumab (200 mg q2w, 4 cycles), along with apatinib (250 mg qd, 2 months). Patients will receive liver resection after neoadjuvant therapy unless the disease is assessed as progressive. The primary outcome is recurrence-free survival (RFS) at 1 year. The planned sample size of 60 patients will be calculated to permit the accumulation of sufficient RFS events in 1 year to achieve 80% power for the RFS primary endpoint. DISCUSSION: Synergistic effects provided by multimodality therapy of locoregional treatment, TKI, and anti-programmed cell death 1 inhibitor significantly improved overall survival for patients with unresectable HCC. Our trial will investigate the efficacy and safety of the triple combination of TACE, camrelizumab, and apatinib as a neoadjuvant strategy for huge, resectable HCC. TRIAL REGISTRATION: www.chitr.org.cn ChiCTR2300078086. Registered on November 28, 2023. Start recruitment: 1st January 2024. Expected completion of recruitment: 15th June 2025.

[Clinical Features and Prognostic of Patients with Primary Central Nervous System Lymphoma].

OBJECTIVE: To explore the clinical features and prognosis of patients with primary central nervous system lymphoma（PCNSL）. METHODS: A retrospective analysis was performed on the relationship between clinical features, treatment regimen and prognosis in 46 newly diagnosed patients with primary central nervous system lymphoma who were diagnosed and treated in The Second Hospital of Lanzhou University from January 2015 to September 2022. Fisher's exact probability method was used to analyze the differences in clinical data of different subgroups. Kaplan-Meier survival curve was used to analyze the overall survival rate and progression-free survival rate of patients with different treatments, and the factors influencing survival were analyzed. RESULTS: Among 46 patients with PCNSL, which pathological type were diffuse large B-cell lymphoma（DLBCL）. There were 26(56.5%) cases of male and 20(43.5%) of female, with a median age of 54(17-71) years. In Hans subtypes, 14 cases (30.4%) of GCB subtype, 32 cases (69.6%) of non-GCB subtype. 32 cases (69.6%) of Ki-67≥80%. Among 36 patients who completed at least 2 cycles of treatment with follow-up data, the efficacy evaluation was as follows: overall response rate(ORR) was 63.9%, complete response(CR) rate was 47.2%, 17 cases of CR, 6 cases of PR. The 1-year progression-free survival rate and 1-year overall survival rate was 73.6% and 84.9%, respectively. The 2-year progression-free survival rate and 2-year overall survival rate was 52.2% and 68.9%, respectively. The ORR and CR rate of 17 patients treated with RMT regimen was 76.5% and 52.9% (9 cases CR and 4 cases PR), respectively. Univariate analysis of 3 groups of patients treated with RMT regimen, RM-BTKi regimen, and RM-TT regimen as first-line treament showed that deep brain infiltration was associated with adverse PFS(P =0.032), and treatment regimen was associated with adverse OS in PCNSL patients（P =0.025）. CONCLUSION: Different treatment modalities were independent prognosis predictors for OS, the deep brain infiltration of PCNSL is a poor predictive factor for PFS. Patients with relapse/refractory (R/R) PCNSL have a longer overall survival time because to the novel medication BTKi. They have strong toleration and therapeutic potential as a first-line therapy for high-risk patients.

[Whole Exome Sequencing Reveals Gene Mutation Characteristics of Primary Central Nervous System Lymphoma].

OBJECTIVE: To investigate gene mutation characteristics of primary central nervous system lymphoma (PCNSL) through whole exome sequencing (WES) to 18 patients with PCNSL. METHODS: Tumor tissues from 18 patients with diffuse large B-cell lymphoma who were diagnosed with PCNSL in Department of Hematology, Lanzhou University Second Hospital from September 2018 to December 2020 and had normal immune function, no history of HIV or immunosuppressant therapy were collected. High-throughput-based WES was performed on the tumor tissues, with an average sequencing depth of >100×. After data processing and bioinformatics analysis of sequencing results, the mutation maps and mutation characteristics of 18 PCNSL patients were obtained. RESULTS: Obvious somatic mutations were detected in all 18 patients. The median number of somatic mutations was 321. Missense mutations were most prominent (accounting for about 90%), and the mutation type was dominated by C>T (50.2%), reflecting the age-related mutation pattern. Among the top 15 frequently mutated genes, PSD3, DUSP5, MAGEB16, TELO2, FMO2, TRMT13, AOC1, PIGZ, SVEP1, IP6K3, and TIAM1 were the driver genes. The enrichment results of driver gene pathways showed that RTK-RAS, Wnt, NOTCH, Hippo and Cell-Cycle pathways were significantly enriched. The tumor mutation burden was between 3.558 48/Mb and 8.780 89/Mb, and the average was 4.953 32/Mb, which was significantly higher than other cancer research cohorts in the TCGA database. CONCLUSIONS: PCNSL occurs somatic missense mutations frequently, mainly point mutations, and the mutation type is mainly C>T. The driver genes are mainly involved in RTK-RAS, Wnt, NOTCH and Hippo pathways, indicating that the above pathways may be related to the pathogenesis of PCNSL. PCNSL has a significantly high tumor mutation burden, which may explain the efficacy of PD-1 inhibitors in PCNSL.

Impact of Deep Learning Denoising Algorithm on Diffusion Tensor Imaging of the Growth Plate on Different Spatial Resolutions.

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. A General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) was employed to acquire two DTI (diffusion tensor imaging) sequences of the left knee on each child at 3T: an in-plane 2.0 × 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel; neither had an intersection gap. For image acquisition, a multi-band DTI with a fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 s/mm2) was utilized. The MR vendor-provided a commercially available DL model which was applied with 75% noise reduction settings to the same subject DTI sequences at different spatial resolutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for the femur and tibia at each spatial resolution. Differences were evaluated using Wilcoxon-signed ranked test and Bland-Altman plots. When comparing DL versus non-denoised diffusion metrics in femur and tibia using the 2 mm × 2 mm × 3 mm voxel dimension, there were no significant differences between tract count (p = 0.1, p = 0.14) tract volume (p = 0.1, p = 0.29) or tibial tract length (p = 0.16); femur tract length exhibited a significant difference (p < 0.01). All diffusion metrics (tract count, volume, length, and fractional anisotropy (FA)) derived from the DL-reconstructed scans, were significantly different from the non-denoised scan DTI metrics in both the femur and tibial physes using the 2 mm3 voxel size (p < 0.001). DL reconstruction resulted in a significant decrease in femorotibial FA for both voxel dimensions (p < 0.01). Leveraging denoising algorithms could address the drawbacks of lower signal-to-noise ratios (SNRs) associated with smaller voxel volumes and capitalize on their better spatial resolutions, allowing for more accurate quantification of diffusion metrics.

Causal relationship between feelings and cognitive decline: An univariable and multivariable Mendelian randomization study.

BACKGROUND: While the impact of depression on cognition is well-documented, the relationship between feelings and cognition has received limited attention. AIM: To explore the potential association between feelings and cognition with a two-sample Mendelian randomization (MR) analysis. METHODS: Our analysis utilized genome-wide association data on various feelings (fed-up feelings, n = 453071; worrier/anxious feelings, n = 450765; guilty feelings, n = 450704; nervous feelings, n = 450700; sensitivity/hurt feelings, n = 449419; miserableness, n = 454982; loneliness/isolation, n = 455364; happiness, n = 152348) in the European population and their impact on cognitive functions (intelligence, n = 269867). Conducting a univariable MR (UVMR) analysis to assess the relationship between feelings and cognition. In this analysis, we applied the inverse variance weighting (IVW), weighted median, and MR Egger methods. Additionally, we performed sensitivity analysis (leave-one-out analysis), assessed heterogeneity (using MR-PRESSO and Cochran's Q test), and conducted multiple validity test (employing MR-Egger regression). Subsequently, a multivariable MR (MVMR) analysis was employed to examine the impact of feelings on cognition. IVW served as the primary method in the multivariable analysis, complemented by median-based and MR-Egger methods. RESULTS: In this study, UVMR indicated that sensitivity/hurt feelings may have a negative causal effect on cognition (OR = 0.63, 95%CI: 0.43-0.92, P = 0.017). After adjustment of other feelings using MVMR, a direct adverse causal effect on cognition was observed (ORMVMR = 0.39, 95%CI: 0.17-0.90, PMVMR = 0.027). While a potential increased risk of cognitive decline was observed for fed-up feelings in the UVMR analysis (ORUVMR = 0.64, 95%CI: 0.42-0.97, PUVMR = 0.037), this effect disappeared after adjusting for other feelings (ORMVMR = 1.42, 95%CI: 0.43-4.74, PMVMR = 0.569). These findings were generally consistent across MV-IVW, median-based, and MR-Egger analyses. MR-Egger regression revealed pleiotropy in the impact of worrier/anxious feelings on cognition, presenting a challenge in identifying the effect. Notably, this study did not demonstrate any significant impact of guilty feelings, nervous feelings, miserableness, or loneliness/isolation on cognition. Due to a limited number of instrumental variables for happiness, this study was unable to analyze the relationship between happiness and cognition. CONCLUSION: This MR study finds that sensitivity/hurt feelings are associated with cognitive decline, while the link between worrier/anxious feelings and cognition remains inconclusive. Insufficient evidence supports direct associations between happiness, guilty feelings, nervous feelings, miserableness, loneliness/isolation, and cognition.

Actual and Virtual Parks Benefit Quality of Life and Physical Activity: A Cluster Trial.

Urban parks provide connectedness to nature as a health resilience environment for promoting health. Virtual reality can provide opportunities for urban citizens to be exposed to natural elements with health benefits. The purpose was to explore the effects of actual and virtual parks on the quality of life and physical activity of urban residents. The study design was a cluster trial. Participants were healthy adults aged 20-50 years, recruited from three college campuses, and randomly assigned to two experimental groups (n = 30, 32) and one control group (n = 30). The intervention with virtual or actual parks was conducted for 30 min a session once a week for 12 weeks. Outcomes were measured using self-reported questionnaires, including the World Health Organization Quality-of-Life Scale-BREF and International Physical Activity Questionnaire-Short Form. In total, 84 participants completed the interventions and post-intervention measures. Results showed that participants who experienced actual parks had significant increases in the social quality of life and light-intensity physical activity and had decreased body weight. Participants who experienced the virtual parks experienced a significant increase in their mental quality of life. Participants in the experimental groups of both kinds of parks had significant improvements in their self-rated health, physical and environmental quality of life, and sedentary time after the intervention. Urban parks are an important natural resource for citizens' health and physical activity promotion. Virtual parks can simulate actual parks and have similar health benefits and are thus are recommended for citizens who lack opportunities and motivation to go to actual parks.

Precise Regulation on the Bond Dissociation Energy of Exocyclic C-N Bonds in Various N-Heterocycle Electron Donors via Machine Learning.

Heterocycles with saturated N atoms (HetSNs) are widely used electron donors in organic light-emitting diode (OLED) materials. Their relatively low bond dissociation energy (BDE) of exocyclic C-N bonds has been closely related to material intrinsic stability and even device lifetime. Thus, it is imperative to realize fast prediction and precise regulation of those C-N BDEs, which demands a deep understanding of the relationship between the molecular structure and BDE. Herein, via machine learning (ML), we rapidly and accurately predicted C-N BDEs in various HetSNs and found that five-membered HetSNs (5-HetSNs) have much higher BDEs than almost all 6-HetSNs, except emerging boron-N blocks. Thorough analysis disclosed that high aromaticity is the foremost factor accounting for the high BDE of 5-HetSNs, and introducing intramolecular hydrogen-bond or electron-withdrawing moieties could also increase BDE. Importantly, the ML models performed well in various realistic OLED materials, showing great potential in characterizing material intrinsic stability for high-throughput virtual-screening and material design efforts.

The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research.

BACKGROUND: As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. OBJECTIVE: We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. METHODS: We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. RESULTS: AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. CONCLUSIONS: AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

Deep Learning Sequence Models for Transcriptional Regulation.

Deciphering the regulatory code of gene expression and interpreting the transcriptional effects of genome variation are critical challenges in human genetics. Modern experimental technologies have resulted in an abundance of data, enabling the development of sequence-based deep learning models that link patterns embedded in DNA to the biochemical and regulatory properties contributing to transcriptional regulation, including modeling epigenetic marks, 3D genome organization, and gene expression, with tissue and cell-type specificity. Such methods can predict the functional consequences of any noncoding variant in the human genome, even rare or never-before-observed variants, and systematically characterize their consequences beyond what is tractable from experiments or quantitative genetics studies alone. Recently, the development and application of interpretability approaches have led to the identification of key sequence patterns contributing to the predicted tasks, providing insights into the underlying biological mechanisms learned and revealing opportunities for improvement in future models.

Charge-mode Neural Stimulator with a Capacitor-reuse Residual Charge Detector and Active Charge Balancing for Epileptic Seizure Suppression.

This study proposes a charge-mode neural stimulator for electrical stimulation systems that utilizes a capacitor-reuse technique with a residual charge detector and achieves active charge balancing simultaneously. The design is mainly used for epilepsy suppression systems to achieve real-time symptom relief during seizures. A charge-mode stimulator is adopted in consideration of the complexity of circuit design, the high voltage tolerance of transistors, and system integration requirements in the future. The residual charge detector allows users to understand the current stimulus situation, enabling them to make optimal adjustments to the stimulation parameters. On the basis of the information on actual stimulation charge, active charge balancing can effectively prevent the accumulation of mismatched charges on electrode impedance. The capacitor- and phase-reuse techniques help realize high integration of the overall stimulator circuit in consideration of the commonality of the use of a capacitor and charging/discharging phase in the stimulation circuit and charge detector. The proposed charge-mode neural stimulator is implemented in a TSMC 0.18 µm 1P6M CMOS process with a core area of 0.2127 mm2. Measurement results demonstrate the accuracy of the stimulation's functionality and the programmable stimulus parameters. The effectiveness of the proposed charge-mode neural stimulator for epileptic seizure suppression is verified through animal experiments.

Deep Learning-Assisted Diffusion Tensor Imaging for Evaluation of the Physis and Metaphysis.

Diffusion tensor imaging of physis and metaphysis can be used as a biomarker to predict height change in the pediatric population. Current application of this technique requires manual segmentation of the physis which is time-consuming and introduces interobserver variability. UNET Transformers (UNETR) can be used for automatic segmentation to optimize workflow. Three hundred and eighty-five DTI scans from 191 subjects with mean age of 12.6 years ± 2.01 years were retrospectively used for training and validation. The mean Dice correlation coefficient was 0.81 for the UNETR model and 0.68 for the UNET. Manual extraction and segmentation took 15 min per volume, whereas both deep learning segmentation techniques took < 1 s per volume and were deterministic, always producing the same result for a given input. Intraclass correlation coefficient (ICC) for ROI-derived femur diffusion metrics was excellent for tract count (0.95), volume (0.95), and FA (0.97), and good for tract length (0.87). The results support the hypothesis that a hybrid UNETR model can be trained to replace the manual segmentation of physeal DTI images, therefore automating the process.

Synthesis and Antifungal Properties of 1,2,4-Triazole Schiff Base Agents Based on a 3D-QSAR Model.

Based on our previous research, a 3D-QSAR model (q2=0.51, ONC=5, r2=0.982, F=271.887, SEE=0.052) was established to predict the inhibitory effects of triazole Schiff base compounds on Fusarium graminearum, and its predictive ability was also confirmed through the statistical parameters. According to the results of the model design, 30 compounds with superior bioactivity compared to the template molecule 4 were obtained. Seven of these compounds (DES2-6, DES9-10) with improved biological activity and readily available raw materials were successfully synthesized. Their structures were confirmed through HRMS, NMR, and single crystal X-ray diffraction analysis (DES-5). The bioactivity of the final products was investigated through an in vitro antifungal assay. There was little difference in the EC50 values between the experimental and predicted values of the model, demonstrating the reliability of the model. Especially, DES-3 (EC50=9.915 mg/L) and DES-5 (EC50=9.384 mg/L) exhibited better inhibitory effects on Fusarium graminearum compared to the standard drug (SD) triadimenol (EC50=10.820 mg/L). These compounds could serve as potential new fungicides for future research. The interaction between the final products and isocitrate lyase (ICL) was investigated through molecular docking. Compounds with R groups that have a higher electron-donating capacity were found to be biologically active.

High-intensity interval training versus moderate-intensity continuous training for localized prostate cancer under active surveillance: a systematic review and network meta-analysis.

BACKGROUND: High-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) have been increasingly adopted for localized prostate cancer (PCa) under active surveillance (AS). However, it is unclear which training modality is the most favorable in terms of cardiorespiratory fitness and biochemical progression. METHODS: We searched PubMed, Cochrane and Embase for relevant RCTs. PRISMA guideline was adopted to ensure optimal conduct of this study. Serum prostate specific antigen (PSA) and peak VO2 were selected as primary outcomes and PSA doubling time (PSADT) and testosterone were selected as secondary outcomes. Only articles written in English were included. Cochrane risk-of-bias tool was used for risk of bias evaluation. RESULTS: A total of 501 studies were selected. Six RCTs with 222 patients were included for data extraction and analysis. High-intensity interval training (HIIT) group demonstrated significantly lower PSA compared with usual care (UC) (MD = -1.4; 95%CI = -2.77 to -0.03) and moderate-intensity continuous training (MICT) group (MD = -1.67; 95%CI = -3.30 to -0.05). Both HIIT and MICT showed significantly improved peak VO2 compared with UC. No significant difference was observed in PSADT and testosterone among different training modalities and UC. Regarding peak VO2, MICT had the highest surface under cumulative ranking curve (SUCRA) scores (98.1%). For serum PSA, HIIT had the highest probability (97.8%) to be the training with the highest efficacy. The potential source of bias mainly came from poorly performed allocation concealment and blinding strategies. CONCLUSIONS: The present study indicated that HIIT and MICT showed considerable cardiorespiratory benefits for localized PCa. HIIT was preferred over MICT in biochemical progression control in terms of decreasing serum PSA levels. However, MICT was favored over HIIT regarding cardiorespiratory benefits. The findings of this study may facilitate future lifestyle intervention, particularly in the form of physical training, for individuals diagnosed with localized PCa under AS.

Lung nodule malignancy classification with associated pulmonary fibrosis using 3D attention-gated convolutional network with CT scans.

BACKGROUND: Chest Computed tomography (CT) scans detect lung nodules and assess pulmonary fibrosis. While pulmonary fibrosis indicates increased lung cancer risk, current clinical practice characterizes nodule risk of malignancy based on nodule size and smoking history; little consideration is given to the fibrotic microenvironment. PURPOSE: To evaluate the effect of incorporating fibrotic microenvironment into classifying malignancy of lung nodules in chest CT images using deep learning techniques. MATERIALS AND METHODS: We developed a visualizable 3D classification model trained with in-house CT dataset for the nodule malignancy classification task. Three slightly-modified datasets were created: (1) nodule alone (microenvironment removed); (2) nodule with surrounding lung microenvironment; and (3) nodule in microenvironment with semantic fibrosis metadata. For each of the models, tenfold cross-validation was performed. Results were evaluated using quantitative measures, such as accuracy, sensitivity, specificity, and area-under-curve (AUC), as well as qualitative assessments, such as attention maps and class activation maps (CAM). RESULTS: The classification model trained with nodule alone achieved 75.61% accuracy, 50.00% sensitivity, 88.46% specificity, and 0.78 AUC; the model trained with nodule and microenvironment achieved 79.03% accuracy, 65.46% sensitivity, 85.86% specificity, and 0.84 AUC. The model trained with additional semantic fibrosis metadata achieved 80.84% accuracy, 74.67% sensitivity, 84.95% specificity, and 0.89 AUC. Our visual evaluation of attention maps and CAM suggested that both the nodules and the microenvironment contributed to the task. CONCLUSION: The nodule malignancy classification performance was found to be improving with microenvironment data. Further improvement was found when incorporating semantic fibrosis information.

Comparison of lung cancer occurring in fibrotic versus non-fibrotic lung on chest CT.

PURPOSE: Evaluate the behavior of lung nodules occurring in areas of pulmonary fibrosis and compare them to pulmonary nodules occurring in the non-fibrotic lung parenchyma. METHODS: This retrospective review of chest CT scans and electronic medical records received expedited IRB approval and a waiver of informed consent. 4500 consecutive patients with a chest CT scan report containing the word fibrosis or a specific type of fibrosis were identified using the system M*Model Catalyst (Maplewood, Minnesota, U.S.). The largest nodule was measured in the longest dimension and re-evaluated, in the same way, on the follow-up exam if multiple time points were available. The nodule doubling time was calculated. If the patient developed cancer, the histologic diagnosis was documented. RESULTS: Six hundred and nine patients were found to have at least one pulmonary nodule on either the first or the second CT scan. 274 of the largest pulmonary nodules were in the fibrotic tissue and 335 were in the non-fibrotic lung parenchyma. Pathology proven cancer was more common in nodules occurring in areas of pulmonary fibrosis compared to nodules occurring in areas of non-fibrotic lung (34% vs 15%, p < 0.01). Adenocarcinoma was the most common cell type in both groups but more frequent in cancers occurring in non-fibrotic tissue. In the non-fibrotic lung, 1 of 126 (0.8%) of nodules measuring 1 to 6 mm were cancer. In contrast, 5 of 49 (10.2%) of nodules in fibrosis measuring 1 to 6 mm represented biopsy-proven cancer (p < 0.01). The doubling time for squamous cell cancer was shorter in the fibrotic lung compared to non-fibrotic lung, however, the difference was not statistically significant (p = 0.24). 15 incident lung nodules on second CT obtained ≤ 18 months after first CT scan was found in fibrotic lung and eight (53%) were diagnosed as cancer. CONCLUSIONS: Nodules occurring in fibrotic lung tissue are more likely to be cancer than nodules in the nonfibrotic lung. Incident pulmonary nodules in pulmonary fibrosis have a high likelihood of being cancer.

The feasibility, efficacy, and safety of RDN procedure using CO2 angiography through radial artery in severe chronic kidney disease patients.

The recent evidence regarding the effectiveness of renal denervation (RDN) in blood pressure control is becoming increasingly substantial. However, many studies have excluded populations with severely impaired kidney function, even though these individuals have a higher prevalence of hypertension compared to the general population, and controlling their blood pressure is more challenging. The effectiveness and safety of RDN in patients with severe chronic kidney disease (CKD) lack strong evidence support. Concerns about worsening kidney function still exist, particularly in patients with CKD stage 5. We conducted an observational study involving 10 patients who were using at least 3 different antihypertensive medications and had an estimated glomerular filtration rate (eGFR) < 45 mL/min/1.73 m2 but had not undergone dialysis. For these patients, we performed RDN via the radial artery approach, with the assistance of carbon dioxide (CO2) angiography. Utilizing this approach, the systolic 24-hour ambulatory blood pressure monitoring did not exhibit a significant decrease at 3 months; however, a significant reduction was observed at 6 months after RDN. We also minimized contrast agent usage, observed no kidney function decline 3 months post-RDN, and experienced no vascular-related complications. Using the radial artery approach and CO2 angiography assistance for RDN may be an effective and safe blood pressure control method for patients with severe kidney impairment.