Moving Object Detection in Freely Moving Camera via Global Motion Compensation and Local Spatial Information Fusion.

Motion object detection (MOD) with freely moving cameras is a challenging task in computer vision. To extract moving objects, most studies have focused on the difference in motion features between foreground and background, which works well for dynamic scenes with relatively regular movements and variations. However, abrupt illumination changes and occlusions often occur in real-world scenes, and the camera may also pan, tilt, rotate, and jitter, etc., resulting in local irregular variations and global discontinuities in motion features. Such complex and changing scenes bring great difficulty in detecting moving objects. To solve this problem, this paper proposes a new MOD method that effectively leverages local and global visual information for foreground/background segmentation. Specifically, on the global side, to support a wider range of camera motion, the relative inter-frame transformations are optimized to absolute transformations referenced to intermediate frames in a global form after enriching the inter-frame matching pairs. The global transformation is fine-tuned using the spatial transformer network (STN). On the local side, to address the problem of dynamic background scenes, foreground object detection is optimized by utilizing the pixel differences between the current frame and the local background model, as well as the consistency of local spatial variations. Then, the spatial information is combined using optical flow segmentation methods, enhancing the precision of the object information. The experimental results show that our method achieves a detection accuracy improvement of over 1.5% compared with the state-of-the-art methods on the datasets of CDNET2014, FBMS-59, and CBD. It demonstrates significant effectiveness in challenging scenarios such as shadows, abrupt changes in illumination, camera jitter, occlusion, and moving backgrounds.

Zwitterion-Modified MXene Quantum Dot as a Nanocarrier for Traditional Chinese Medicine Sanguinarine Delivery and Its Application for Photothermal-Chemotherapy Synergistic Antibacterial and Wound Healing.

The nanomaterialization of traditional Chinese medicine (TCM) has aroused widespread interest among researchers. Sanguinarine (SAN) is a kind of TCM with good antibacterial properties, which has important applications in anti-infection of wounds. Additionally, the combination of photothermal therapy and chemotherapy can overcome bacterial resistance, further improving bactericidal and wound healing efficiency. In this paper, we prepared an antibacterial agent by loading SAN on the zwitterion-modified MXene quantum dot nanocarrier (SAN@AHEP@Ta4C3), realizing pH/NIR controlled drug release and photothermal/chemotherapy synergistic antibacterial and wound healing. The particle size of SAN@AHEP@Ta4C3 is about 120 nm, and it has a good water solubility and stability. In addition, it also has excellent photothermal conversion performance (η = 39.2%), which can effectively convert light energy into heat energy under near-infrared (NIR) laser irradiation, further promoting drug release and achieving bactericidal effects by synergistic chemotherapy and photothermal therapy. The in vitro and in vivo experiments show that SAN@AHEP@Ta4C3 exhibits an excellent antibacterial effect against Staphylococcus aureus and Escherichia coli, and it can effectively promote the wound healing of mice. Moreover, the SAN@AHEP@Ta4C3 also has good biocompatibility and has no side effects on normal tissue and organs. This work introduces a multifunctional antibacterial agent based on TCM and hot-spot material MXene, which will have considerable application prospects in biomedical fields.

Depression in Polycystic Ovary Syndrome: Focusing on Pathogenesis and Treatment.

Polycystic ovary syndrome (PCOS) is one of the most prevalent gynecological endocrine conditions affecting reproductive women. It can feature a variety of symptoms, such as obesity, insulin resistance, skin conditions, and infertility. Women with PCOS are susceptible to illnesses including mood disorders, diabetes, hypertension, and dyslipidemia. Among them, depression is the most common in PCOS and has a detrimental effect on quality of life. Depression may occasionally develop due to the pathological traits of PCOS, but its exact pathogenesis in PCOS have eluded researchers to date. Therefore, there is an urgent need to explore the pathogenesis and treatments of depression in PCOS. The present review discusses the epidemiology of depression in PCOS, potential pathogenic mechanisms underlying PCOS and depression, as well as some potential factors causing depression in PCOS, including obesity, insulin resistance, hyperandrogenism, inflammation, and infertility. Meanwhile, some common treatment strategies for depression in PCOS, such as lifestyle intervention, acupuncture, oral contraceptive pills, psychological intervention, and insulin-sensitizer, are also reviewed. To fully understand the pathogenesis and treatment of depression in PCOS, a need remains for future large-scale multi-center randomized controlled trials and in-depth mechanism studies. Appeared originally in Front Psychiatry 2022; 13:1001484.

Periodontal disease increases the severity of chronic obstructive pulmonary disease: a Mendelian randomization study.

BACKGROUND: Recent research suggests that periodontitis can increase the risk of chronic obstructive pulmonary disease (COPD). In this study, we performed two-sample Mendelian randomization (MR) and investigated the causal effect of periodontitis (PD) on the genetic prediction of COPD. The study aimed to estimate how exposures affected outcomes. METHODS: Published data from the Gene-Lifestyle Interaction in the Dental Endpoints (GLIDE) Consortium's genome-wide association studies (GWAS) for periodontitis (17,353 cases and 28,210 controls) and COPD (16,488 cases and 169,688 controls) from European ancestry were utilized. This study employed a two-sample MR analysis approach and applied several complementary methods, including weighted median, inverse variance weighted (IVW), and MR-Egger regression. Multivariable Mendelian randomization (MVMR) analysis was further conducted to mitigate the influence of smoking on COPD. RESULTS: We chose five single-nucleotide polymorphisms (SNPs) as instrumental variables for periodontitis. A strong genetically predicted causal link between periodontitis and COPD, that is, periodontitis as an independent risk factor for COPD was detected. PD (OR = 1.102951, 95% CI: 1.005-1.211, p = 0.039) MR-Egger regression and weighted median analysis results were coincident with those of the IVW method. According to the sensitivity analysis, horizontal pleiotropy's effect on causal estimations seemed unlikely. However, reverse MR analysis revealed no significant genetic causal association between COPD and periodontitis. IVW (OR = 1.048 > 1, 95%CI: 0.973-1.128, p = 0.2082) MR Egger (OR = 0.826, 95%CI:0.658-1.037, p = 0.1104) and weighted median (OR = 1.043, 95%CI: 0.941-1.156, p = 0.4239). The results of multivariable Mendelian randomization (MVMR) analysis, after adjusting for the confounding effect of smoking, suggest a potential causal relationship between periodontitis and COPD (P = 0.035). CONCLUSION: In this study, periodontitis was found to be independent of COPD and a significant risk factor, providing new insights into periodontitis-mediated mechanisms underlying COPD development.

Naringin attenuates Actinobacillus pleuropneumoniae-induced acute lung injury via MAPK/NF-κB and Keap1/Nrf2/HO-1 pathway.

Actinobacillus pleuropneumoniae (APP) causes porcine pleuropneumonia (PCP), which is clinically characterized by acute hemorrhagic, necrotizing pneumonia, and chronic fibrinous pneumonia. Although many measures have been taken to prevent the disease, prevention and control of the disease are becoming increasingly difficult due to the abundance of APP sera, weak vaccine cross-protection, and increasing antibiotic resistance in APP. Therefore, there is an urgent need to develop novel drugs against APP infection to prevent the spread of APP. Naringin (NAR) has been reported to have an excellent therapeutic effect on pulmonary diseases, but its therapeutic effect on lung injury caused by APP is not apparent. Our research has shown that NAR was able to alleviate APP-induced weight loss and quantity of food taken and reduce the number of WBCs and NEs in peripheral blood in mice; pathological tissue sections showed that NAR was able to prevent and control APP-induced pathological lung injury effectively; based on the establishment of an in vivo/in vitro model of APP inflammation, it was found that NAR was able to play an anti-inflammatory role through inhibiting the MAPK/NF-κB signaling pathway and exerting anti-inflammatory effects; additionally, NAR activating the Nrf2 signalling pathway, increasing the secretion of antioxidant enzymes Nqo1, CAT, and SOD1, inhibiting the secretion of oxidative damage factors NOS2 and COX2, and enhancing the antioxidant stress ability, thus playing an antioxidant role. In summary, NAR can relieve severe lung injury caused by APP by reducing excessive inflammatory response and improving antioxidant capacity.

Unravelling the molecular landscape of endometrial cancer subtypes: insights from multi-omics analysis.

BACKGROUND: Endometrial cancer (EC) as one of the most common gynecologic malignancies is increasing in incidence during the past 10 years. Genome-Wide Association Studies (GWAS) extended to metabolic and protein phenotypes inspired us to employ multi-omics methods to analyze the causal relationships of plasma metabolites and proteins with EC to advance our understanding of EC biology and pave the way for more targeted approaches to its diagnosis and treatment by comparing the molecular profiles of different EC subtypes. METHODS: Two-sample Mendelian randomization (MR) was performed to investigate the effects of plasma metabolites and proteins on risks of different subtypes of EC (endometrioid and non-endometrioid). Pathway analysis, transcriptomic analysis, and network analysis were further employed to illustrate gene-protein-metabolites interactions underlying the pathogenesis of distinct EC histological types. RESULTS: We identified 66 causal relationships between plasma metabolites and endometrioid EC, and 132 causal relationships between plasma proteins and endometrioid EC. Additionally, 40 causal relationships between plasma metabolites and non-endometrioid EC, and 125 causal relationships between plasma proteins and non-endometrioid EC were observed. Substantial differences were observed between endometrioid and non-endometrioid histological types of EC at both the metabolite and protein levels. We identified 7 overlapping proteins (RGMA, NRXN2, EVA1C, SLC14A1, SLC6A14, SCUBE1, FGF8) in endometrioid subtype and 6 overlapping proteins (IL32, GRB7, L1CAM, CCL25, GGT2, PSG5) in non-endometrioid subtype and network analysis of above proteins and metabolites to identify coregulated nodes. CONCLUSIONS: Our findings observed substantial differences between endometrioid and non-endometrioid EC at the metabolite and protein levels, providing novel insights into gene-protein-metabolites interactions that could influence future EC treatments.

Evaluating factors associated with telehealth appropriateness in outpatient rheumatoid arthritis encounters using the Encounter Appropriateness Score for You (EASY).

OBJECTIVE: Telehealth has been proposed as a safe and effective alternative to in-person care for rheumatoid arthritis (RA). The purpose of this study was to evaluate factors associated with telehealth appropriateness in outpatient RA encounters. METHODS: A prospective cohort study (1/1/21-8/31/21) was conducted using electronic health record data from outpatient RA encounters in a single academic rheumatology practice. Rheumatology providers rated the telehealth appropriateness of their own encounters using the Encounter Appropriateness Score for You (EASY) immediately following each encounter. Robust Poisson regression with Generalized Estimating Equations (GEE) modeling was used to evaluate the association of telehealth appropriateness with patient demographics, RA clinical characteristics, comorbid non-inflammatory causes of joint pain, previous and current encounter characteristics, and provider characteristics. RESULTS: During the study period, 1,823 outpatient encounters with 1,177 unique RA patients received an EASY score from 25 rheumatology providers. In the final multivariate model [Relative Risk (95% Confidence Interval)], factors associated with increased telehealth appropriateness included higher average provider preference for telehealth in prior encounters [1.26 (1.21-1.31)], telehealth as the current encounter modality [2.27 (1.95-2.64)], and increased patient age [1.05 (1.01-1.09)]. Factors associated with decreased telehealth appropriateness included moderate [0.81 (0.68-0.96)] and high [0.57 (0.46-0.70)] RA disease activity and if the previous encounter were conducted via telehealth [0.83 (0.73-0.95)]. CONCLUSION: In this study, telehealth appropriateness was most associated with provider preference, the current and previous encounter modality, and RA disease activity. Other factors like patient demographics, RA medications, and comorbid non-inflammatory causes of joint pain were not associated with telehealth appropriateness.

Advancing brain-inspired computing with hybrid neural networks.

Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain-computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network (HNN), which integrates computer-science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.

Comparison of left- and right-sided colorectal cancer to explore prognostic signatures related to pyroptosis.

Background: Colorectal cancer (CRC) is one of the most common malignancies, and pyroptosis exerts an immunoregulatory role in CRC. Although the location of the primary tumor is a prognostic factor for patients with CRC, the mechanisms of pyroptosis in left- and right-sided CRC remain unclear. Methods: Expression and clinical data were collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Differences in clinical characteristics, immune cell infiltration, and somatic mutations between left- and right-sided CRC were then compared. After screening for differentially expressed genes, Pearson correlation analysis was performed to select pyroptosis-related genes, followed by a gene set enrichment analysis. Univariate and multivariate Cox regression analyses were used to construct and validate the prognostic model and nomogram for predicting prognosis. Collected left- and right-sided CRC samples were subjected to reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to validate the expression of key pyroptosis-related genes. Results: Left- and right-sided CRC exhibited significant differences in clinical features and immune cell infiltration. Five prognostic signatures were identified from among 134 pyroptosis-related differentially expressed genes to construct a risk score-based prognostic model, and adverse outcomes for high-risk patients were further verified using an external cohort. A nomogram was also generated based on three independent prognostic factors to predict survival probabilities, while calibration curves confirmed the consistency between the predicted and actual survival. Experiment data confirmed the significant differential expression of five genes between left- and right-sided CRC. Conclusion: The five identified pyroptosis-related gene signatures may be potential biomarkers for predicting prognosis in left- and right-sided CRC and may help improve the clinical outcomes of patients with CRC.

Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio predict the prognosis in patients with type B aortic dissection receiving thoracic endovascular aortic repair.

Background: The triglyceride-glucose (TyG) index and triglyceride to high-density lipoprotein cholesterol (TG/HDL-c) ratio are both reliable surrogate indicator of insulin resistance and have been shown to be valuable in predicting various cardiovascular diseases. However, few studies have explored its association with the prognosis of type B aortic dissection (TBAD) patients receiving thoracic endovascular aortic repair (TEVAR). Methods: A total of 1,425 consecutive patients who underwent TEVAR were included. Data from 935 patients were analyzed in the study. The endpoint was defined as 30-day and 1-year aortic-related adverse events (ARAEs), all-cause mortality, and major adverse cardiovascular and cerebrovascular events (MACCEs). Results: There were 935 patients included during a mean follow-up time of 2.8 years. After adjusting for multiple confounding factors, continuous TG/HDL-c [hazard ratio (HR) =1.07; 95% confidence interval (CI): 1.00-1.15; P=0.041] was independently associated with 1-year all-cause mortality. Both a high (Quintile 5: TG/HDL-c ratio ≥4.11) (HR =4.84; 95% CI: 1.55-15.13; P=0.007) and low TG/HDL-c ratio (Quintile 1: TG/HDL-c ratio <1.44) (HR =4.67; 95% CI: 1.46-14.94; P=0.001) were still independent risk factors for 1-year all-cause mortality. Conclusions: Elevated baseline TG/HDL-c ratio and TG/HDL-c ≥4.11 were significantly related to a higher risk of 1-year all-cause mortality among TBAD patients undergoing TEVAR. At the same time, the low TG/HDL-c ratio was also independently associated with 1-year all-cause mortality. Special attention should be paid to TBAD patients with a higher or an overly low TG/HDL-c ratio.

Microplastics change soil properties, plant performance, and bacterial communities in salt-affected soils.

Microplastics (MPs) are emerging contaminants found globally. However, their effects on soil-plant systems in salt-affected habitats remain unknown. Here, we examined the effects of polyethylene (PE) and polylactic acid (PLA) on soil properties, maize performance, and bacterial communities in soils with different salinity levels. Overall, MPs decreased soil electrical conductivity and increased NH4+-N and NO3--N contents. Adding NaCl alone had promoting and inhibitive effects on plant growth in a concentration-dependent manner. Overall, the addition of 0.2% PLA increased shoot biomass, while 2% PLA decreased it. Salinity increased Na content and decreased K/Na ratio in plant tissues (particularly roots), which were further modified by MPs. NaCl and MPs singly and jointly regulated the expression of functional genes related to salt tolerance in leaves, including ZMSOS1, ZMHKT1, and ZMHAK1. Exposure to NaCl alone had a slight effect on soil bacterial α-diversity, but in most cases, MPs increased ACE, Chao1, and Shannon indexes. Both MPs and NaCl altered bacterial community composition, although the specific effects varied depending on the type and concentration of MPs and the salinity level. Overall, PLA had more pronounced effects on soil-plant systems compared to PE. These findings bridge knowledge gaps in the risks of MPs in salt-affected habitats.

Best acupuncture method for mammary gland hyperplasia: Evaluation of randomized controlled trials and Bayesian network meta-analysis.

Objective: To evaluate the effectiveness of different acupuncture treatments for mammary gland hyperplasia (MGH) using a network meta-analysis. Methods: Several databases were searched without language restrictions from 2000 to February 2023, including PubMed, Embase, Web of Science, Cochrane Library, China Science and Technology Journal Database, China Biology Medicine Database, Wanfang Database, China National Knowledge Infrastructure Database, and other professional websites and gray literature. Inclusion criteria were adult women diagnosed with MGH; intervention measures included acupuncture and related therapies; the control group was treated with simple drugs; and the research type was a randomized controlled trial (RCT). The primary outcomes were treatment effectiveness and estradiol and progesterone levels. Secondary outcomes were breast lump size and visual analog scale (VAS) score of breast pain. Exclusion criteria were studies unrelated to MGH, incorrect study populations, control measures or interventions, incomplete data, non-RCTs, case reports, and animal experiments. Cochrane tools were used to assess the risk of bias. The R software (x64 version 4.2.1), Review Manager 5.3 software and STATA 16.0 software were used for data analysis. Results: Following a rigorous screening process, data extraction, and quality assessment, 48 eligible RCTs encompassing 4,500 patients with MGH and 16 interventions were included. The results indicated that acupuncture, alone or in combination with traditional Chinese or Western medicine, had better therapeutic effects than conventional therapy. In terms of effectiveness, warm needle acupuncture was the best choice (94.6%). Bloodletting pricking was the most effective method (85.7%) for lowering progesterone levels. Bloodletting pricking was the most effective method (98.3%) for lowering estradiol levels. Manual acupuncture combined with traditional Chinese medicine was the most effective (74.5%) treatment to improve the size of the breast lump. Warm needle acupuncture was the most effective (69.8%) in improving the VAS score. Conclusion: Acupuncture therapy was more effective in treating MGH than drug therapy alone, and warm needle acupuncture and bloodletting pricking were the two best options. However, larger sample sizes and high-quality RCTs are required.

A novel entropy-driven dual-output mode integrated with DNAzyme for enhanced microRNA detection.

Accurate microRNA (miRNA) detection is pivotal in the diagnosis and monitoring of cancer. Entropy-driven catalysis (EDC) has attracted widespread attention as an enzyme-free, isothermal technique for miRNA detection owing to its inherent simplicity and reliability. However, conventional EDC is a single-output mode, limiting the efficiency of signal amplification. In this study, a novel EDC dual-output mode was employed in conjunction with DNAzyme, resulting in the development of an EDC dual-end DNAzyme (EDC-DED) approach for highly sensitive miRNA detection. In this system, miRNA-21 initiated the EDC reaction, producing a large amount of catalytically active dual-end Mg2+-dependent DNAzyme. The DNAzyme further cleaved the reporter cyclically, generating a notably amplified fluorescence signal. The proposed method achieved a low detection limit of 2 pM. Compared with the traditional EDC single-end DNAzyme (EDC-SED) strategy, the present method exhibited superior amplification efficiency, enhancing detection sensitivity by approximately 46.5-fold. Furthermore, this platform demonstrated ideal specificity, satisfactory reproducibility and acceptable detection capabilities in clinical serum samples. Therefore, the straightforward and convenient strategy is a potential tool for miRNA analysis, which may provide a new perspective for biological analysis and clinical application.

Levothyroxine treatment in pregnant women with thyroid stimulating hormone levels ranging between 2.5 and 10 mIU/L: A propensity score matched analysis.

OBJECTIVE: To clarify the association between levothyroxine (LT-4) treatment and various adverse pregnancy outcomes in pregnant women with thyroid stimulating hormone (TSH) levels ranging between 2.5 to 10.0 mIU/L in the first trimester, stratified according to thyroid peroxidase antibody (TPOAb) positivity and TSH level. METHODS: This retrospective analysis of retrospectively and prospectively collected cohort data included Chinese pregnant women with TSH levels of 2.5-10 mIU/L and normal free thyroxine levels (11.8-18.4 pmol/L) in the first trimester. All participants were followed up until the completion of pregnancy, and information on LT-4 treatment, pregnancy complications, and pregnancy outcomes was recorded. A 1:1 nearest-neighbor propensity score matching (PSM) between the LT-4-treated and -untreated groups with a caliper distance of 0.02 was performed using a multivariable logistic regression model. Multivariable-adjusted modified Poisson regression was used to estimate the relative risk (RR) and 95% confidence interval (CI) of LT-4 treatment for adverse pregnancy outcomes. Subgroup analyses were also performed in four subgroups simultaneously stratified by TPOAb status (negative or positive) and TSH levels (2.5-4.0 mIU/L as high-normal group and 4.0-10.0 mIU/L as SCH group). The study was registered in the Chinese Clinical Trial Registry (ChiCTR2100047394). RESULTS: Among the 4,370 pregnant women in the study, 1,342 received LT-4 treatment, and 3,028 did not. The 1:1 PSM yielded 668 pairs of individuals and revealed that LT-4 treatment was significantly associated with a decreased risk of pregnancy loss (RR=0.528, 95% CI: 0.344-0.812) and an increased risk of small-for-gestational-age infants (RR=1.595, 95% CI: 1.023-2.485). Subgroup analyses suggested that the above effects of LT-4 treatment were mainly from TPOAb-negative participants. LT-4 treatment was associated with an increased risk of preterm birth (RR=2.214, 95% CI: 1.016-4.825) in TPOAb-positive pregnant women with high-normal TSH levels. CONCLUSION: LT-4 treatment was significantly associated with a lower risk of pregnancy loss and a higher risk of small-for-gestational-age infants in pregnant women with TSH levels of 2.5-10 mIU/L. An increased risk of preterm birth was observed in the LT-4-treated group among TPOAb-positive participants with TSH levels of 2.5-4.0 mIU/L.

Partition refinement of WorldPop population spatial distribution data method: A case study of Zhuhai, China.

Currently, the core idea of the refined method of population spatial distribution is to establish a correlation between the population and auxiliary data at the administrative-unit level and, then, refine it to the grid unit. However, this method ignores the advantages of public population spatial distribution data. Given these problems, this study proposed a partition strategy using the natural break method at the grid-unit level, which adopts the population density to constrain the land class weight and redistributes the population under the dual constraints of land class and area weights. Accordingly, we used the dasymetric method to refine the population distribution data. The study established a partition model for public population spatial distribution data and auxiliary data at the grid-unit level and, then, refined it to smaller grid units. This method effectively utilizes the public population spatial distribution data and solves the problem of the dataset being not sufficiently accurate to describe small-scale regions and low resolutions. Taking the public WorldPop population spatial distribution dataset as an example, the results indicate that the proposed method has higher accuracy than other public datasets and can also describe the actual spatial distribution characteristics of the population accurately and intuitively. Simultaneously, this provides a new concept for research on population spatial distribution refinement methods.

Building a stable and robust anti-interference DNA dissipation system by eliminating the accumulation of systemic specified errors.

BACKGROUND: The emergence of DNA nanotechnology has enabled the systematic design of diverse bionic dissipative behaviors under the precise control of nucleic acid nanodevices. Nevertheless, when compared to the dissipation observed in robust living systems, it is highly desirable to enhance the anti-interference for artificial DNA dissipation to withstand perturbations and facilitate repairs within the complex biological environments. RESULTS: In this study, we introduce strategically designed "trash cans" to facilitate kinetic control over interferences, transforming the stochastic binding of individual components within a homogeneous solution into a competitive binding process. This approach effectively eliminates incorrect binding and the accumulation of systemic interferences while ensuring a consistent pattern of energy fluctuation from response to silence. Remarkably, even in the presence of numerous interferences differing by only one base, we successfully achieve complete system reset through multiple cycles, effectively restoring the energy level to a minimum. SIGNIFICANCE: The system was able to operate stably without any adverse effect under conditions of irregular interference, high-abundance interference, and even multiplex interferences including DNA and RNA crosstalk. This work not only provides an effective paradigm for constructing robust DNA dissipation systems but also greatly broadens the potential of DNA dissipation for applications in high-precision molecular recognition and complex biological reaction networks.

Apicomplexa micropore: history, function, and formation.

The micropore, a mysterious structure found in apicomplexan species, was recently shown to be essential for nutrient acquisition in Plasmodium falciparum and Toxoplasma gondii. However, the differences between the micropores of these two parasites questions the nature of a general apicomplexan micropore structure and whether the formation process model from Plasmodium can be applied to other apicomplexans. We analyzed the literature on different apicomplexan micropores and found that T. gondii probably harbors a more representative micropore type than the more widely studied ones in Plasmodium. Using recent knowledge of the Kelch 13 (K13) protein interactome and gene depletion phenotypes in the T. gondii micropore, we propose a model of micropore formation, thus enriching our wider understanding of micropore protein function.

[Determination of 24 elements migration in Yixing clay pottery by inductively coupled plasma mass spectrometry].

OBJECTIVE: To establish an analytical method for determining the migration of 24 elements in Yixing clay pottery in 4% acetic acid simulated solution by inductively coupled plasma mass spectrometry. METHODS: Four types of Yixing clay pottery, including Yixing clay teapot, Yixing clay kettle, Yixing clay pot, and Yixing clay electric stew pot, were immersed in 4% acetic acid as a food simulant for testing. The migration amount of 24 elements in the migration solution was determined using inductively coupled plasma mass spectrometry. RESULTS: Lithium, magnesium, aluminum, iron, and barium elements with a mass concentration of 1000 µg/L; Lead, cadmium, total arsenic, chromium, nickel, copper, vanadium, manganese, antimony, tin, zinc, cobalt, molybdenum, silver, beryllium, thallium, titanium, and strontium elements within 100 µg/L there was a linear relationship within, the r value was between 0.998 739 and 0.999 989. Total mercury at 5.0 µg/L, there was a linear relationship within, the r value of 0.995 056. The detection limit of the elements measured by this method was between 0.5 and 45.0 µg/L, the recovery rate was 80.6%-108.9%, and the relative standard deviation was 1.0%-4.8%(n=6). A total of 32 samples of four types of Yixing clay pottery sold on the market, including teapots, boiling kettles, casseroles, and electric stewing pots, were tested. It was found that the migration of 16 elements, including beryllium, titanium, chromium, nickel, cobalt, zinc, silver, cadmium, antimony, total mercury, thallium, tin, copper, total arsenic, molybdenum, and lead, were lower than the quantitative limit. The element with the highest migration volume teapot was aluminum, magnesium, and barium; The kettle was aluminum and magnesium; Casserole was aluminum, magnesium, and lithium; The electric stew pot was aluminum. CONCLUSION: This method is easy to operate and has high accuracy, providing an effective and feasible detection method for the determination and evaluation of element migration in Yixing clay pottery.

Multi-step validation of a deep learning-based system with visual explanations for optical diagnosis of polyps with advanced features.

Artificial intelligence (AI) has been found to assist in optical differentiation of hyperplastic and adenomatous colorectal polyps. We investigated whether AI can improve the accuracy of endoscopists' optical diagnosis of polyps with advanced features. We introduced our AI system distinguishing polyps with advanced features with more than 0.870 of accuracy in the internal and external validation datasets. All 19 endoscopists with different levels showed significantly lower diagnostic accuracy (0.410-0.580) than the AI. Prospective randomized controlled study involving 120 endoscopists into optical diagnosis of polyps with advanced features with or without AI demonstration identified that AI improved endoscopists' proportion of polyps with advanced features correctly sent for histological examination (0.960 versus 0.840, p < 0.001), and the proportion of polyps without advanced features resected and discarded (0.490 versus 0.380, p = 0.007). We thus developed an AI technique that significantly increases the accuracy of colorectal polyps with advanced features.

Deep analysis of skin molecular heterogeneities and their significance on the precise treatment of patients with psoriasis.

Background: Psoriasis is a highly heterogeneous autoinflammatory disease. At present, heterogeneity in disease has not been adequately translated into concrete treatment options. Our aim was to develop and verify a new stratification scheme that identifies the heterogeneity of psoriasis by the integration of large-scale transcriptomic profiles, thereby identifying patient subtypes and providing personalized treatment options whenever possible. Methods: We performed functional enrichment and network analysis of upregulated differentially expressed genes using microarray datasets of lesional and non-lesional skin samples from 250 psoriatic patients. Unsupervised clustering methods were used to identify the skin subtypes. Finally, an Xgboost classifier was utilized to predict the effects of methotrexate and commonly prescribed biologics on skin subtypes. Results: Based on the 163 upregulated differentially expressed genes, psoriasis patients were categorized into three subtypes (subtypes A-C). Immune cells and proinflammatory-related pathways were markedly activated in subtype A, named immune activation. Contrastingly, subtype C, named stroma proliferation, was enriched in integrated stroma cells and tissue proliferation-related signaling pathways. Subtype B was modestly activated in all the signaling pathways. Notably, subtypes A and B presented good responses to methotrexate and interleukin-12/23 inhibitors (ustekinumab) but inadequate responses to tumor necrosis factor-α inhibitors and interleukin-17A receptor inhibitors. Contrastly, subtype C exhibited excellent responses to tumor necrosis factor-α inhibitors (etanercept) and interleukin-17A receptor inhibitors (brodalumab) but not methotrexate and interleukin-12/23 inhibitors. Conclusions: Psoriasis patients can be assorted into three subtypes with different molecular and cellular characteristics based on the heterogeneity of the skin's immune cells and the stroma, determining the clinical responses of conventional therapies.