Background/Aims: Whether angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARB) could benefit patients with diabetes and albuminuria remains controversial. A systematic review and meta-analysis were conducted to answer this question by comparing ACE inhibitors or ARB with placebo among these patients. Methods: In this meta-analysis, electronic data sources (Medline, the Cochrane Collaboration, and EMBASE) were searched. Randomized controlled trials (RCTs) comparing ACE inhibitors or ARB with placebo in subjects with diabetes and albuminuria (defined as urinary albumin-to-creatinine ratio, UACR≥30mg/g Cr) were included. Outcomes parameters were all-cause mortality, end stage renal disease (ESRD), doubling of serum creatinine levels, and cardiovascular events (CV). Results: Twenty-six RCTs (including 20 for ACE inhibitors and 6 for ARB) were included, comprising 10378 participants with diabetes and albuminuria. Compared to placebo, treatment with ACE inhibitors or ARBs did not reduce all-cause mortality or CV. For renal outcomes, ARBs significantly reduced the risk of ESRD by 23% (odds ratio 0.77, 95%CI 0.65-0.92), while ACE inhibitors were not associated with a decreased risk of ESRD (0.69, 0.43-1.10). Both ACE inhibitors and ARBs reduced the risk of doubling of the serum creatinine level (0.60, 0.39-0.91 for ACE inhibitors; 0.75, 0.64-0.88 for ARBs), and subgroup analyses for patients with macroalbuminuria or microalbuminuria showed similar results. Conclusion: In patients with diabetes and albuminuria, ARBs reduced risks of ESRD and doubling of the serum creatinine level. ACE inhibitors and ARBs failed to reduce all-cause mortality and CV. Based on the renoprotective effects, ARBs may be preferred for diabetic patients with albuminuria.

Cervical cytology is a critical screening strategy for early detection of pre-cancerous and cancerous cervical lesions. The challenge lies in accurately classifying various cervical cytology cell types. Existing automated cervical cytology methods are primarily trained on databases covering a narrow range of coarse-grained cell types, which fail to provide a comprehensive and detailed performance analysis that accurately represents real-world cytopathology conditions. To overcome these limitations, we introduce HiCervix, the most extensive, multi-center cervical cytology dataset currently available to the public. HiCervix includes 40,229 cervical cells from 4,496 whole slide images, categorized into 29 annotated classes. These classes are organized within a three-level hierarchical tree to capture fine-grained subtype information. To exploit the semantic correlation inherent in this hierarchical tree, we propose HierSwin, a hierarchical vision transformer-based classification network. HierSwin serves as a benchmark for detailed feature learning in both coarse-level and fine-level cervical cancer classification tasks. In our comprehensive experiments, HierSwin demonstrated remarkable performance, achieving 92.08% accuracy for coarse-level classification and 82.93% accuracy averaged across all three levels. When compared to board-certified cytopathologists, HierSwin achieved high classification performance (0.8293 versus 0.7359 averaged accuracy), highlighting its potential for clinical applications. This newly released HiCervix dataset, along with our benchmark HierSwin method, is poised to make a substantial impact on the advancement of deep learning algorithms for rapid cervical cancer screening and greatly improve cancer prevention and patient outcomes in real-world clinical settings.

Background and objective: Chronic pain may be an early indicator of cognitive decline, but previous studies have not systematically examined the population-level associations between widespread pain and adverse cognitive outcomes and stroke. This study was designed to determine the association between widespread pain, a common subtype of chronic pain, and subsequent dementia, Alzheimer’s disease dementia and stroke. Methods: This retrospective cohort study used data from the US community-based Framingham Heart Study. Pain status was assessed at a single time point between 1990 and 1994. Widespread pain was determined based on the Framingham Heart Study pain homunculus. Dementia follow-up occurred across a median of 10 years (IQR, 6–13 years) for persons who were dementia free at baseline. Proportional hazard models examined associations between widespread pain and incident dementia, Alzheimer’s disease dementia and stroke. Results: A total of 347 (14.1%) subjects fulfilled the criteria for widespread pain, whereas 2117 (85.9%) subjects did not. Of 188 cases of incident all-cause dementia, 128 were Alzheimer’s disease dementia. In addition, 139 patients suffered stroke during the follow-up period. After multivariate adjustment including age and sex, widespread pain was associated with 43% increase in all-cause dementia risk (HR: 1.43; 95% CI 1.06 to 1.92), 47% increase in Alzheimer’s disease dementia risk (HR: 1.47; 95% CI 1.13 to 2.20) and 29% increase in stroke risk (HR: 1.29; 95% CI 1.08 to 2.54). Comparable results were shown in the subgroup of individuals over 65 years old. Conclusion: Widespread pain was associated with an increased incidence of all-cause dementia, Alzheimer’s disease dementia and stroke. Trial registration number: NCT00005121 .

Diabetic foot ulcers (DFUs) are complex chronic wounds which have a major long-term impact on the morbidity, mortality and quality of patients. The objective of this study was to assess the efficacy and time sensitivity of human amnion/chorion membrane treatment in patients with chronic DFUs.

Cervical cancer is the fourth most common cancer in women and its subtyping requires examining histopathological slides or digital images, such as whole slide images (WSIs). However, manually inspecting WSIs with gigapixel sizes can be laborious and prone to errors for pathologists. To address this issue, computer-aided approaches based on weakly-supervised learning techniques have been proposed. These methods can predict disease types directly from WSIs and highlight diagnosis-relevant regions, which can help pathologists achieve faster and more accurate diagnoses. WSIs are divided into overlapping patches using a sliding window approach, and these patches are subsequently screened in a sequential zig-zag pattern to identify spatiotemporal dependencies. These dependencies are further analyzed to generate predictions at the WSI level. Therefore, effective patch feature learning and spatiotemporal aggregation are two key issues in the weakly-supervised WSI classification (WSWC) task. In this paper, we present a label-efficient WSWC method called spatiotemporal aggregation for cervical WSIs (SAC-Net), which jointly performs online feature extraction and feature aggregation to infer the WSI-level prediction in an end-to-end manner. The online feature extractor helps to learn cervical-cancer-specific features and obtain more accurate patch representations. The feature aggregator uses an online instance clustering method to learn proper weight parameters for each cluster, which generates the WSI embedding with enhanced spatiotemporal aggregation. SAC-Net is developed and evaluated on a public cervical WSI dataset (TissueNet) containing 1015 WSIs, which are also externally tested on three independent cervical WSI datasets. Our results demonstrate that SAC-Net achieves state-of-the-art classification performance and is robust. SAC-Net has the potential to be a useful tool for clinical cervical cancer detection.