Browse Articles

Mitosis detection in histopathological images using customized deep learning and hybrid optimization algorithms

Identifying mitosis is crucial for cancer diagnosis, but accurate detection remains difficult because of class imbalance and complex morphological variations in histopathological images. To overcome this challenge, we propose a Customized Deep Learning (CDL) model, which integrates advanced deep-learning techniques for better mitosis detection. The CDL model utilizes transfer learning to counter the effects of class imbalance and speed up convergence, while skip connections are also employed to improve the localization of mitosis. Furthermore, we have established an innovative selection mechanism by the hybrid of Jellyfish Search Optimizer (JSO) and Walrus Optimization Algorithm (WOA) to maximize the momentum of the model. The proposed approach is rigorously evaluated on multiple publicly available mitosis detection datasets, including Mitosis WSI CCMCT Training Set, Mitosis-AIC, Mitosis Detection, and Mitosis and Non-Mitosis datasets. To tackle these issues, we hereby bring forth a specifically tailored Custom Deep Learning model, that assimilates hybrid CNN architecture into transfer learning and feature selection for improved mitotic detection. The CDL model comprises a Transfer Learning-based Mitosis Detection module under which extracted features from pre-trained deep networks are used to bolster feature extraction and alleviate class imbalance through skip connections to better localize mitosis. The robust assessment on a benchmark dataset displays the outstanding efficacy of the CDL model, reaching an excellent F1 score of 0.994 and accuracy of 98.8% thus proving its strength for the detection of mitotic figures. This proposed methodology can greatly empower pathologists for accurate appraisal of cancer diagnosis and prognosis. Future lines of exploration will include fusion methodologies and time efficiency for real-time applications, as well as extending CDL to various histopathological analyses.

The joint effect of feedback order and reward schemes on prevalence-induced perceptual decisions

Whipple’s Disease with Endocarditis

Nanoplastic concentrations across the North Atlantic

Abstract Plastic pollution of the marine realm is widespread, with most scientific attention given to macroplastics and microplastics 1,2 . By contrast, ocean nanoplastics (<1 μm) remain largely unquantified, leaving gaps in our understanding of the mass budget of this plastic size class 3–5 . Here we measure nanoplastic concentrations on an ocean-basin scale along a transect crossing the North Atlantic from the subtropical gyre to the northern European shelf. We find approximately 1.5–32.0 mg m −3 of polyethylene terephthalate (PET), polystyrene (PS) and polyvinyl chloride (PVC) nanoplastics throughout the entire water column. On average, we observe a 1.4-fold higher concentration of nanoplastics in the mixed layer when compared with intermediate water depth, with highest mixed-layer nanoplastic concentrations near the European continent. Nanoplastic concentrations at intermediate water depth are 1.8-fold higher in the subtropical gyre compared with the open North Atlantic outside the gyre. The lowest nanoplastic concentrations, with about 5.5 mg m −3 on average and predominantly composed of PET, are present in bottom waters. For the mixed layer of the temperate to subtropical North Atlantic, we estimate that the mass of nanoplastic may amount to 27 million tonnes (Mt). This is in the same range or exceeding previous budget estimates of macroplastics/microplastics for the entire Atlantic 6,7 or the global ocean 1,8 . Our findings suggest that nanoplastics comprise the dominant fraction of marine plastic pollution.

Hypomyelination in autism-associated neuroligin-3 mutant mice impairs parvalbumin interneuron excitability, gamma oscillations, and sensory discrimination

Factors associated with death from COVID-19 in traditional peoples and communities in Brazil

Introduction COVID-19 has disproportionately impacted vulnerable populations, including traditional communities in Brazil, who face socioeconomic and health disparities, increasing the risk of severe outcomes. This study aims to identify factors associated with mortality among hospitalized COVID-19 patients from traditional communities in Brazil. Methods This cross-sectional study analyzed data from the System of Epidemiological Surveillance of Influenza (SIVEP-Gripe) on hospitalizations for COVID-19 between 2021 and 2023. Individuals from traditional communities were included. The variables analyzed included demographic characteristics, clinical symptoms, comorbidities, and the need for hospital support. Logistic regressions were performed to assess associations with mortality, considering p < 0.05 significant. Results Of the 7,101 cases analyzed, males showed a higher risk of death (OR = 1.39; 95% CI: 1.18–1.63). Among ethnic groups, blacks presented with an OR = 2.92 (95% CI: 1.68–5.08) and indigenous, OR = 2.25 (95% CI: 1.73–2.94). Older age increased the risk, with OR = 6.57 (95% CI: 2.16–28.5) for ages 60–79 and OR = 12.8 (95% CI: 4.18–55.8) for ≥80 years. Dyspnea (OR = 2.16; 95% CI: 1.77–2.65) and low saturation (OR = 2.13; 95% CI: 1.78–2.55) were associated with death, while loss of taste was protective (OR = 0.62; 95% CI: 0.51–0.75). Immunosuppression (OR = 2.14; 95% CI: 1.23–3.79) and chronic renal disease (OR = 1.64; 95% CI: 1.10–2.46) increased the risk. Patients on invasive ventilation had the highest risk of death (OR = 19.4; 95% CI: 15.2–25.0), followed by non-invasive ventilation (OR = 2.65; 95% CI: 2.18–3.23) and ICU (OR = 2.15; 95% CI: 1.85–2.49). Conclusion Risk factors for mortality among hospitalized patients from traditional communities include male sex, older age, race/color, severe respiratory symptoms, comorbidities, and the need for invasive ventilation. These findings reinforce the importance of targeted health strategies to reduce the risk of mortality in these vulnerable populations.

Doubting the power of prestige: obedience to authority beyond institutional and research justifications

Your Combination Asthma Inhaler — Don’t Leave Home without It!

A high-performance genetically encoded sensor for cellular imaging of PKC activity in vivo

Research on composite foundation rheology using the combined rheological element model

The safety and stability of the dam are affected by the rheology created by the deep soft soil layer following composite foundation reinforcement. Based on the classical combined element modeling, a new combined rheological element model is proposed in this paper. The consolidated rheological and fluid-solid coupling model is established by COMSOL to simulate the deep soft soil composite dam foundation. The results demonstrate that the model can better respond to the actual situation of stress, deformation, and seepage in each stage of the dam, and the error is within 5%. The strength and stiffness of every component of the dam are most affected by the consolidation rheology and fluid-solid coupling of the dam foundation during the impoundment phase, and the growth rate of each index gradually slows down with the extension of the running time. The rheology of the deep soft soil composite foundation is continuous. During the normal operation of the reservoir, the consolidation and settlement of the composite foundation are basically completed in 4–5 years, and the subsequent settlement changes are affected by the rheology for decades. The research results theoretically support the construction of safe and reliable system of deep soft soil composite foundation including clay core rockfill dam.

Effects of long-term very high-altitude exposure on cardiopulmonary function of healthy adults in plain areas

Incarcerated Femoral Hernia

Human de novo mutation rates from a four-generation pedigree reference

Abstract Understanding the human de novo mutation (DNM) rate requires complete sequence information1. Here using five complementary short-read and long-read sequencing technologies, we phased and assembled more than 95% of each diploid human genome in a four-generation, twenty-eight-member family (CEPH 1463). We estimate 98–206 DNMs per transmission, including 74.5 de novo single-nucleotide variants, 7.4 non-tandem repeat indels, 65.3 de novo indels or structural variants originating from tandem repeats, and 4.4 centromeric DNMs. Among male individuals, we find 12.4 de novo Y chromosome events per generation. Short tandem repeats and variable-number tandem repeats are the most mutable, with 32 loci exhibiting recurrent mutation through the generations. We accurately assemble 288 centromeres and six Y chromosomes across the generations and demonstrate that the DNM rate varies by an order of magnitude depending on repeat content, length and sequence identity. We show a strong paternal bias (75–81%) for all forms of germline DNM, yet we estimate that 16% of de novo single-nucleotide variants are postzygotic in origin with no paternal bias, including early germline mosaic mutations. We place all this variation in the context of a high-resolution recombination map (~3.4 kb breakpoint resolution) and find no correlation between meiotic crossover and de novo structural variants. These near-telomere-to-telomere familial genomes provide a truth set to understand the most fundamental processes underlying human genetic variation.

Regulation of partial endothelial-to-mesenchymal transition by circATXN1 in ischemic diseases

Contemporaneous symptom networks for multidimensional symptom experience in lung cancer survivors of immunotherapy: A network analysis

Background Immunotherapy dramatically increases patient survival and radically changes the way lung cancer is treated. Through intricate network analysis of disease-related symptoms, symptom networks are able to statistically analyze and depict the links between different symptoms. Through symptom network analysis, this study seeks to pinpoint the primary symptoms that immunotherapy-treated lung cancer survivors encounter. Furthermore, by leveraging the synergistic relationships between symptoms, it aims to investigate the target actions for precise interventions, offering important insights for the creation of a successful symptom management program. Methods We assessed the symptoms of 249 lung cancer patients undergoing immunotherapy at Zhejiang Cancer Hospital between February and October of 2024. The evaluation was conducted using the Lung Cancer-Specific Module and the Anderson Symptom Assessment Scale (Chinese version). Following the use of exploratory factor analysis to discover symptom clusters, we calculated the centrality indices and created a network structure using the R programming language that displayed the connections between the symptoms. After controlling for factors, we constructed contemporaneous networks that had all 17 symptoms. Results Four symptom clusters—respiratory, emotional, gastrointestinal, and neuro-perceptual—were shown to be generalized. The three most prevalent symptoms, as determined by nodal intensity, were sadness (rs = 7.43), cough (rs = 6.65) and nausea (rs = 6.73). The most common symptoms at bridge intensity were nausea (rs = 4.69), cough (rs = 4.72), and sadness (rs = 5.69). The network’s overall strength and structure did not significantly differ between the male and female groups, or between those who had or did not have a history of smoking. Conclusion This study demonstrates that the symptom burden is significant among survivors of lung cancer immunotherapy, with sadness, cough, and nausea playing crucial roles in the multidimensional symptom network. Interventions focused on addressing sadness can effectively reduce the severity of the entire symptom network, while early intervention for cough and nausea can alleviate the symptom management burden for patients. Additionally, identifying more predictable symptoms can aid in selecting appropriate targets for symptom management. Healthcare professionals can utilize these symptom patterns to deliver evidence-based and precise symptom management for survivors of lung cancer immunotherapy.

Altered microvasculature in patients with adult-onset dermatomyositis using optical coherence tomography angiography

Abstract To assess ocular microvascular vessel density (VD) in patients with adult-onset dermatomyositis (DM) measured by optical coherence tomography angiography (OCTA). 10 eyes of 10 patients with DM without interstitial lung disease were included in this cross-sectional study. All patients underwent ophthalmological and systemic evaluation, including nailfold capillaroscopy and OCTA examination (RTVue XR Avanti, Optovue Inc., Fremont, California, USA). The VD data of the 3 × 3 mm OCT angiogram of the superficial and deep capillary plexus (SCP, DCP), the choriocapillaris (CC) and radial peripapillary capillary density (RPC) of the optic nerve head (ONH) were extracted and analyzed. The data were compared with a gender- and age-matched healthy control group (10 eyes from 10 healthy individuals). The mean age of the patient group was 56.7 ± 3.11 years and 55.5 ± 10.76 years of the control group (p = 0.68) with 8 female and 2 male subjects per patient group and control group respectively. The VD in the inside disc area of the ONH was significantly reduced in the patient group compared to healthy controls (p = 0.028). A significant positive correlation was found between CC VD and nailfold capillary density (rₛₚ = 0.673, p = 0.033). This exploratory study identifies reduced VD in the ONH region of adult DM patients and suggests a link between ocular and systemic microvascular involvement. OCTA may offer non-invasive insights into microcirculatory changes in DM. Larger, longitudinal studies are warranted to validate these findings and evaluate the utility of OCTA-derived parameters as potential biomarkers in this rare disease.

Wealth and Mortality in the United States and Europe

Ultra-depleted mantle source of basalts from the South Pole–Aitken basin

Probing interplay of topological properties and electron correlation in TaIrTe4 via nonlinear Hall effect

Preparation of high-flowability ultra-early-strength grouting material and investigation of its mechanical properties in reinforcing fractured sandstone

This study systematically investigated the effects of modified ultrafine fly ash, ultrafine silica fume, nano-SiO2, and polycarboxylate superplasticizer on the performance of cement-based grouting materials. Using a five-level orthogonal experimental, the optimal mix ratio was determined: 5.00 wt.% modified ultrafine fly ash, 10.00 wt.% ultrafine silica fume, 9.00 wt.% nano-SiO2, and 0.05 wt.% polycarboxylate superplasticizer. This formulation enabled the preparation of a cement-based grouting material with high-flowability and ultra-early-strength (HFUES) characteristics. Experimental results demonstrated that, compared to ordinary silicate grouting materials, the HFUES grouting material significantly enhanced the reinforcement of fractured sandstone. The reinforced specimens exhibited an average 67.42% increment in tensile strength, 123.17% increment in the uniaxial compressive strength, and 94.00% increment in elastic modulus compared to intact specimens. Microstructural analysis revealed that the grouting material was uniformly distributed within the sandstone matrix, forming a dense interfacial bond and generating a substantial amount of fibrous network-like hydration products. The volume fraction of the consolidated matrix was 1.33–1.47 times that of ordinary silicate grouting materials, while the fracture volume fraction was only 0.36–0.73 times. The study demonstrates that the HFUES grouting material exhibits excellent mechanical properties and interfacial bonding characteristics, significantly improving the reinforcement effectiveness of fractured sandstone.