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Chronic atrophic gastritis is a significant premalignant lesion of gastric carcinoma. There is a great need to prevent the progression to gastric carcinoma through early intervention and treatment for chronic atrophic gastritis. Weifuchun, a famous Chinese patent drug, has been widely used for chronic atrophic gastritis in China. However, it remains unclear whether Weifuchun is effective for atrophic gastritis. To determine the effectiveness and safety of Weifuchun for chronic atrophic gastritis. We systematically retrieved seven databases (Cochrane Library, EMBASE, PubMed, China National Knowledge Infrastructure, Wanfang database, Chinese Scientific Journals Database, and Chinese Biological Medical Database) from their inception to October 5, 2022. Methodological quality was examined using the Cochrane Risk of bias tool. We also used RevMan 5.4 software for statistical analysis to examine the effectiveness and safety of Weifuchun. Fifteen studies with 1,488 patients were enrolled in this meta-analysis. The study indicated that Weifuchun was more effective (RR 1.52; 95% CI 1.41, 1.63; p<0.00001) than Western medicine and other Chinese patent medicine. In addition, Weifuchun was more effective in improving gastric mucosal under gastroscopy, improving histopathologic changes of gastric mucosal, and inhibiting Helicobacter pylori. However, no significant difference in safety was examined between Weifuchun and the control group (RR 2.83; 95% CI 0.85, 9.38; P = 0.09). The meta-analysis revealed a significant statistical difference with Weifuchun in effectiveness compared to the control group. However, there was no significant difference in safety. Thus, more high-quality clinical studies are needed in the future. Registration number CRD42022365703.

The association of gut microbial features with type 2 diabetes (T2D) has been inconsistent due in part to the complexity of this disease and variation in study design. Even in cases in which individual microbial species have been associated with T2D, mechanisms have been unable to be attributed to these associations based on specific microbial strains. We conducted a comprehensive study of the T2D microbiome, analyzing 8,117 shotgun metagenomes from 10 cohorts of individuals with T2D, prediabetes, and normoglycemic status in the United States, Europe, Israel and China. Dysbiosis in 19 phylogenetically diverse species was associated with T2D (false discovery rate < 0.10), for example, enriched Clostridium bolteae and depleted Butyrivibrio crossotus . These microorganisms also contributed to community-level functional changes potentially underlying T2D pathogenesis, for example, perturbations in glucose metabolism. Our study identifies within-species phylogenetic diversity for strains of 27 species that explain inter-individual differences in T2D risk, such as Eubacterium rectale . In some cases, these were explained by strain-specific gene carriage, including loci involved in various mechanisms of horizontal gene transfer and novel biological processes underlying metabolic risk, for example, quorum sensing. In summary, our study provides robust cross-cohort microbial signatures in a strain-resolved manner and offers new mechanistic insights into T2D. Using an international collection of population-based microbiome studies of participants in the MicroCardio Consortium, a cross-cohort meta-analysis identified several phylogenetically diverse, species- and strain-level microbial features as well as community-level functional shifts encompassing diverse pathways to type 2 diabetes.

Accurate semantic segmentation of remote sensing imagery requires both fine-grained boundary modelling and long-range contextual reasoning. To address this challenge, we propose TransDeepUNet, a hierarchical multimodal fusion network that integrates RGB imagery and DSM elevation data. The framework employs a parameter-sharing dual-branch encoder to preserve modality-specific representations. A shallow cross-modal attention module enhances structural details, while a deep cross-modal Transformer models global dependencies and semantic alignment. A cascaded decoder progressively reconstructs high-resolution segmentation maps. Experiments on the ISPRS Vaihingen and Potsdam datasets and a high-resolution Swiss dataset demonstrate consistent performance improvements over strong CNN and hybrid baselines. On the Potsdam dataset, TransDeepUNet achieves an mIoU of 85.64% and an mF1-score of 92.07%, outperforming comparable multimodal models while maintaining competitive computational complexity. The code is publicly available at: https://github.com/yingning01/TransDeepUNet.

Experimental studies reported biochemical actions underpinning aging processes and mortality, but the relevant metabolic alterations in humans are not well understood. Here we examine the associations of 243 plasma metabolites with mortality and longevity (attaining age 85 years) in 11,634 US (median follow-up of 22.6 years, with 4288 deaths) and 1878 Spanish participants (median follow-up of 14.5 years, with 525 deaths). We find that, higher levels of N2,N2-dimethylguanosine, pseudouridine, N4-acetylcytidine, 4-acetamidobutanoic acid, N1-acetylspermidine, and lipids with fewer double bonds are associated with increased risk of all-cause mortality and reduced odds of longevity; whereas L-serine and lipids with more double bonds are associated with lower mortality risk and a higher likelihood of longevity. We further develop a multi-metabolite profile score that is associated with higher mortality risk. Our findings suggest that differences in levels of nucleosides, amino acids, and several lipid subclasses can predict mortality. The underlying mechanisms remain to be determined. The metabolic alterations underpinning aging processes and mortality in humans are not well understood. Here, the authors show that differences in levels of nucleosides, amino acids, and several lipid subclasses can predict mortality and longevity.

Very high resolution (VHR) remote sensing change detection (CD) is crucial for monitoring Earth’s dynamics but faces challenges in capturing fine-grained changes and distinguishing them from pseudo-changes due to varying acquisition conditions. Existing deep learning methods often suffer from information loss via downsampling, obscuring details, and lack filter adaptability to spatial heterogeneity. To address these issues, we introduce Information-Preserving Adaptive Convolutional Network (IPACN). IPACN features a novel Information-Preserving Backbone (IPB), leveraging principles adapted from reversible networks to minimize feature degradation during hierarchical bi-temporal feature extraction, enhancing the preservation of fine spatial details, essential for accurate change delineation. Crucially, IPACN incorporates a Frequency-Adaptive Difference Enhancement Module (FADEM) that applies adaptive filtering, informed by frequency analysis concepts, directly to the bi-temporal difference features. The FADEM dynamically refines change signals based on local spectral characteristics, improving discrimination. This synergistic approach, combining high-fidelity feature preservation (IPB) with adaptive difference refinement (FADEM), yields robust change representations. Comprehensive experiments on benchmark datasets demonstrate that IPACN achieves state-of-the-art performance, showing significant improvements in F1 score and IoU, enhanced boundary delineation, and improved robustness against pseudo-changes, offering an effective solution for very high resolution remote sensing CD.

The synergistic development of nanophotonics and machine learning has inspired tremendous innovations in both fields in the past decade. In diverse photonics research, deep-learning methods using artificial neural networks become the key game changer that greatly facilitates rapid nanophotonics design and the versatile processing of optical information. Moreover, optical computing platforms that perform calculations through light propagation are receiving tremendous interest as next-generation machine-learning hardware with advantages in computing speed, energy efficiency, and parallelism. This review summarizes the current state-of-the-art nanophotonic devices enabled by machine learning and analyzes the longstanding challenges that must be overcome to make an impact on technology. We also discuss the opportunities of intelligent photonics in applications such as computational imaging/sensing and machine vision. The intersection of nanophotonics with deep learning holds tremendous implications for transformative technologies ranging from internet of things to smart health. Lastly, we provide our perspective on the pressing challenges in intelligent photonics that must be tackled to advance this field to the next level and the vast opportunities for multidisciplinary collaboration.

Marine-derived n-3 polyunsaturated fatty acids (PUFA) may have a beneficial effect on inflammation via lowering pro-inflammatory eicosanoid concentrations. We aimed to assess the effect of marine-derived n-3 PUFA on prostaglandin E2 (PGE2), thromboxane B2 (TXB2), and leukotriene B4 (LTB4) through systematic review and meta-analysis of randomized controlled trials. A structured search strategy on PubMed, Web of Science and Cochrane up to November 2015 was undertaken in this meta-analysis. Standard mean difference was used to calculate the effect size of marine-derived n-3 PUFA on PGE2, TXB2 and LTB4 in a random-effect model. A total of 18 RCTs with 826 subjects were included in this systematic review and meta-analysis. Supplementation of marine-derived n-3 PUFA significantly decreased concentrations of TXB2 in serum/plasma in subjects with high risk of cardiovascular diseases (SMD:-1.26; 95% CI: -1.65, -0.86) and LTB4 in neutrophils in unhealthy subjects (subjects with non-autoimmune chronic diseases or auto-immune diseases) (SMD:-0.59: 95% CI: -1.02, -0.16). Subgroup analyses showed a significant reduction of LTB4 in subjects with rheumatoid arthritis (SMD: -0.83; 95% CI: -1.37, -0.29), but not in non-autoimmune chronic disease patients (SMD: -0.33; 95% CI: -0.97, 0.31). No significant publication bias was shown in the meta-analysis. Marine-derived n-3 PUFA had a beneficial effect on reducing the concentration of TXB2 in blood of subjects with high risk of CVD as well as LTB4 in neutrophils in unhealthy subjects, and that subjects with RA showed lower LTB4 content with supplementation of marine-derived n-3 PUFA.

Purpose To determine the risk of mydriasis-induced angle closure in patients with intumescent age-related cataracts with different anterior chamber angle widths through a comparative analysis of anterior segment biometric parameters before and after mydriasis. Methods A total of 195 patients with cortical intumescent cataracts were grouped into four according to the mean TIA in the four quadrants before mydriasis as follows: extremely narrow-angle group, 0° < TIA ≤ 10°; narrow-angle group, 10° < TIA ≤ 20°; wide-angle group, 20° < TIA ≤ 30°; and ultra-wide-angle group, TIA > 30°. The biological parameters obtained with UBM before and after mydriasis were compared. Results For the extremely narrow-angle group, the TIA and AOD500 values could increase or decrease after mydriasis, indicating that the anterior chamber angle could widen or narrow, and there was a slight but statistically significant increase in IOP after dilation. For the other groups, the AOD500 and TIA significantly increased after mydriasis, signifying a more open anterior chamber angle. Conclusion Patients with 0° < TIA ≤ 10° have a high risk of angle closure and should undergo laser peripheral iridotomy before mydriasis to prevent acute elevation of intraocular pressure. Patients with 10° < TIA ≤ 20° have a low risk, and they can undergo routine mydriasis, but changes in their anterior chamber morphology should be closely monitored. When TIA > 20°, the risk is almost negligible, and mydriasis can be safely induced.

Excessive maternal gestational weight gain (GWG) contributes to generational obesity. Our aim was to explore efficacy and intervention characteristics (trimester, duration, frequency, intensity, and delivery method) of interventions to prevent excessive GWG. CINAHL, Cochrane, EMBASE, LILACS, MEDLINE, PsycINFO, and Scopus were searched up to May 2018 (no date or language restrictions). Keywords and MeSH terms for diet, GWG, intervention, lifestyle, maternal, physical activity, and pregnancy were used to locate randomized-controlled trials (RCTs). The Cochrane Collaboration tool for assessing risk of bias was applied. Eighty-nine RCTs were included. Meta-analysis (60 trials) estimated that women in diet only (WMD: −3.27; 95% CI: −4.96, −1.58, p < 0.01), physical activity (PA) (WMD: −1.02; 95% CI: −1.56, −0.49, p < 0.01), and lifestyle interventions (combining diet and PA) (WMD: −0.84; 95% CI: −1.29, −0.39, p < 0.01) gained significantly less weight than controls. The three eHealth interventions favored neither intervention nor control (WMD: −1.06; 95% CI: −4.13, 2.00, p = 0.50). Meta-regression demonstrated no optimal duration, frequency, intensity, setting, or diet type. Traditional face to face delivery of weight management interventions during pregnancy can be successful. Delivery via eHealth has potential to extend its reach to younger women but needs further evaluation of its success.

Background The role of plant-based dietary patterns in preventing cardiovascular disease (CVD) among individuals with prediabetes and diabetes remains unclear. We aimed to evaluate the associations of plant-based diet index (PDI), healthful PDI (hPDI), and unhealthful PDI (uPDI) with cardiovascular disease (CVD) risk and explore potential contributing factors among people with prediabetes and diabetes. Methods A total of 17,926 participants with prediabetes and 7798 with diabetes were enrolled from the UK Biobank between 2006 and 2010 and followed until the end of 2020. We calculated the PDI, hPDI, and uPDI based on 18 major food groups including plant-based foods and animal-based foods and applied Cox proportional hazard models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD risk related to PDI, hPDI, and uPDI. Decomposition analysis was performed to assess the role of dietary components, and mediation analysis was performed to assess the potential mediating role of serum biomarkers underlying these associations. Results A total of 2324 CVD events were documented among individuals with prediabetes, while 1461 events occurred among patients with diabetes. An inverse association was found between hPDI and CVD risk among individuals with prediabetes (HR T3 vs. T1 = 0.88, 95% CI = 0.79–0.98, P trend  = 0.025) but not those with diabetes. A positive association was found between uPDI and CVD risk among individuals with prediabetes (HR T3 vs. T1 = 1.17, 95% CI = 1.05–1.30, P trend  = 0.005) and those with diabetes (HR T3 vs. T1 = 1.14, 95% CI = 1.00–1.29, P trend  = 0.043). High-sugar-sweetened beverages (SSB) intake accounted for 35% of the hPDI-CVD association and 15% of the uPDI-CVD association among individuals with prediabetes, whereas low intake of whole grain accounted for 36% of the association among patients with diabetes. Elevated cystatin C levels explained the largest proportion of the association between uPDI and CVD risk among individuals with prediabetes (15%, 95% CI = 7–30%) and diabetes (44%, 95% CI = 9–86%). Conclusions Adherence to an unhealthy plant-based diet is associated with a higher CVD risk in people with prediabetes or diabetes, which may be partially attributed to low consumption of whole grains, high intake of SSB, and high blood cystatin C levels.