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Background: Lymphovascular invasion (LVI) is a significant risk factor for lymph node metastasis in gastric cancer (GC) and is closely related to the prognosis and recurrence of GC. This study aimed to establish clinical models, radiomics models and combination models for the diagnosis of GC vascular invasion. Methods: This study enrolled 146 patients with GC proved by pathology and who underwent radical resection of GC. The patients were assigned to the training and validation cohorts. A total of 1,702 radiomic features were extracted from contrast-enhanced computed tomography images of GC. Logistic regression analyses were performed to establish a clinical model, a radiomics model and a combined model. The performance of the predictive models was measured by the receiver operating characteristic (ROC) curve. Results: In the training cohort, the age of LVI negative (-) patients and LVI positive (+) patients were 62.41 ± 8.41 and 63.76 ± 10.08 years, respectively, and there were more male (n = 63) than female (n = 19) patients in the LVI (+) group. Diameter and differentiation were the independent risk factors for determining LVI (-) and (+). A combined model was found to be relatively highly discriminative based on the area under the ROC curve for both the training (0.853, 95% CI: 0.784-0.920, sensitivity: 0.650 and specificity: 0.907) and the validation cohorts (0.742, 95% CI: 0.559-0.925, sensitivity: 0.736 and specificity: 0.700). Conclusions: The combined model had the highest diagnostic effectiveness, and the nomogram established by this model had good performance. It can provide a reliable prediction method for individual treatment of LVI in GC before surgery.

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BACKGROUND: Central obesity was considered as a risk factor for falls among the older population. Waist circumference (WC), lipid accumulation product (LAP), visceral adiposity index (VAI), and the Chinese visceral adiposity index (CVAI) are considered as surrogate markers for abdominal fat deposition in increasing studies. Nevertheless, the longitudinal relationship between these indices and falls among the older population remains indistinct. This study aimed to explore the association between abdominal obesity indices and falls among older community-dwellers. METHODS: Our study included 3501 individuals aged ≥ 65 years from the Guangzhou Falls and Health Status Tracking Cohort at baseline in 2021 and then prospectively followed up in 2022. The outcome of interest was the occurrence of falls. The Kaplan-Meier curves and multivariable Cox regression analysis were used to explore the associations between abdominal obesity indices and falls. Moreover, the restricted cubic spline analysis (RCS) was conducted to test the non-linear relationships between abdominal obesity indices and hazards of falls incident. RESULTS: After a median follow-up period of 551 days, a total of 1022 participants experienced falls. The cumulative incidence rate of falls was observed to be higher among individuals with central obesity and those falling within the fourth quartile (Q4) of LAP, VAI, and CVAI. Participants with central obesity and those in Q4 of LAP, VAI, and CVAI were associated with higher risk of falls, with hazard ratios (HRs) of 1.422 (HR 95%CI: 1.255-1.611), 1.346 (1.176-1.541), 1.270 (1.108-1.457), 1.322 (1.154-1.514), respectively. Each 1-SD increment in WC, LAP, VAI, and CVAI was a significant increased risk of falls among participants. Subgroup analysis further revealed these results were basically stable and appeared to be significantly stronger among those females, aged 65-69 years, and with body mass index (BMI) ≥ 28 kg/m2. Additionally, RCS curves showed an overall upward trend in the risk of falls as the abdominal indices increased. CONCLUSIONS: Abdominal obesity indices, as WC, LAP, VAI, and CVAI were significantly associated with falls among older community-dwellers. Reduction of abdominal obesity indices might be suggested as the strategy of falls prevention.

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It has been demonstrated that observers can accurately estimate their self-motion direction (i.e., heading) from optic flow, which can be affected by attention. However, it remains unclear how attention affects the serial dependence in the estimation. In the current study, participants conducted two experiments. The results showed that the estimation accuracy decreased when attentional resources allocated to the heading estimation task were reduced. Additionally, the estimates of currently presented headings were biased toward the headings of previously seen headings, showing serial dependence. Especially, this effect decreased (increased) when the attentional resources allocated to the previously (currently) seen headings were reduced. Furthermore, importantly, we developed a Bayesian inference model, which incorporated attention-modulated likelihoods and qualitatively predicted changes in the estimation accuracy and serial dependence. In summary, the current study shows that attention affects the serial dependence in heading estimation from optic flow and reveals the Bayesian computational mechanism behind the heading estimation.

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This study explores the effects of disinfectant and antibiotic exposure on gut health, focusing on gut microbiota balance and gut immune function. Our analysis indicates that disinfectants increase the proportion of Gram-positive bacteria, particularly increasing Staphylococcus levels, while antibiotics increase the proportion of Gram-negative bacteria, especially Bacteroides levels. These changes disrupt microbial harmony and affect the gut microbiome's functional capacity. Additionally, our research reveals that both disinfectants and antibiotics reduce colon length and cause mucosal damage. A significant finding is the downregulation of NLRC4, a key immune system regulator in the gut, accompanied by changes in immune factor expression. This interaction between chemical exposure and immune system dysfunction increases susceptibility to inflammatory bowel disease and other gut conditions. Given the importance of disinfectants in disease prevention, this study advocates for a balanced approach to their use, aiming to protect public health while minimizing adverse effects on the gut microbiome and immune function. IMPORTANCE: Disinfectants are extensively employed across various sectors, such as the food sector. Disinfectants are widely used in various sectors, including the food processing industry, animal husbandry, households, and pharmaceuticals. Their extensive application risks environmental contamination, impacting water and soil quality. However, the effect of disinfectant exposure on the gut microbiome and the immune function of animals remains a significant, unresolved issue with profound public health implications. This highlights the need for increased scrutiny and more regulated use of disinfectants to mitigate unintended consequences on gut health and maintain immune system integrity.

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Ionizing radiation exposure can cause damage to diverse tissues and organs, with the hematopoietic system being the most sensitive. However, limited information is available regarding the radiosensitivity of various hematopoietic cell populations in the bone marrow due to the high heterogeneity of the hematopoietic system. In this study, we observed that granulocyte-macrophage progenitors, hematopoietic stem/progenitor cells, and B cells within the bone marrow showed the highest sensitivity, exhibiting a rapid decrease in cell numbers following irradiation. Nonetheless, neutrophils, natural killer (NK) cells, T cells, and dendritic cells demonstrated a certain degree of radioresistance, with neutrophils exhibiting the most pronounced resistance. By employing single-cell transcriptome sequencing, we investigated the early responsive genes in various cell types following irradiation, revealing that distinct gene expression profiles emerged between radiosensitive and radioresistant cells. In B cells, radiation exposure led to a specific upregulation of genes associated with mitochondrial respiratory chain complexes, suggesting a connection between these complexes and cell radiosensitivity. In neutrophils, radiation exposure resulted in fewer gene alterations, indicating their potential for distinct mechanisms in radiation resistance. Collectively, this study provides insights into the molecular mechanism for the heterogeneity of radiosensitivity among the various bone marrow hematopoietic cell populations.

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Al-Mg alloys are widely used as important engineering structural materials in aerospace engineering, transportation systems, and structural constructions due to their low density, high specific strength, corrosion resistance, welding capability, fatigue strength, and cost-effectiveness. However, the conventional Al-Mg alloys can no longer fully satisfy the demands of practical production due to difficulties caused by many defects. The high strength of Al-Mg alloys as non-heat treatment precipitation-strengthened alloys is achieved primarily by solid solution strengthening along with work hardening rather than precipitation strengthening. Therefore, severe plastic deformation (SPD) techniques can be often used to produce ultrafine-grained structures to fabricate ultra-high strength aluminum alloys. However, this approach often achieves the strengthening of material at the cost of reduced ductility. This paper comprehensively summarizes the various approaches of ultrafine/nanocrystalline materials for enhancing their plasticity, elaborates on the creation of a bimodal microstructure within the alloy, and discusses the formation of a nanotwin microstructure within the alloy and the incorporation of dispersed nanoparticles. The mechanisms underlying both the strengthening and toughening during large plastic deformation in aluminum alloys are summarized, and the future research direction of high-performance ultrafine crystalline and nanocrystalline Al-Mg aluminum alloys is prospected.

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We present the application of N-difluoroacetylglucosamine (GlcNDFA) in a chemical evolution strategy to synthesize oligosaccharides. In comparison to conventional N-trifluoroacetylglucosamine, GlcNDFA exhibits superior substrate compatibility with glycosyltransferases as well as stability in aqueous environments. Using our 16-step assembly line, GlcNDFA can be used to produce homogeneous dekaparin, a heparin-like medication, with a yield of 62.2%. This underscores the significant potential of GlcNDFA as a chemical evolution precursor in the precise synthesis of structurally defined polysaccharides.

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BACKGROUND: Obesity, a multifactorial and complex health condition, has emerged as a significant global public health concern. Integrating machine learning techniques into obesity research offers great promise as an interdisciplinary field, particularly in the screening, diagnosis, and analysis of obesity. Nevertheless, the publications on using machine learning methods in obesity research have not been systematically evaluated. Hence, this study aimed to quantitatively examine, visualize, and analyze the publications concerning the use of machine learning methods in obesity research by means of bibliometrics. METHODS: The Web of Science core collection was the primary database source for this study, which collected publications on obesity research using machine learning methods over the last 20 years from January 1, 2004, to December 31, 2023. Only articles and reviews that fit the criteria were selected for bibliometric analysis, and in terms of language, only English was accepted. VOSviewer, CiteSpace, and Excel were the primary software utilized. RESULTS: Between 2004 and 2023, the number of publications on obesity research using machine learning methods increased exponentially. Eventually, 3286 publications that met the eligibility criteria were searched. According to the collaborative network analysis, the United States has the greatest volume of publications, indicating a significant influence on this research. coauthor's analysis showed the authoritative one in this field is Leo Breiman. Scientific Reports is the most widely published journal. The most referenced publication is "R: a language and environment for statistical computing." An analysis of keywords shows that deep learning, support vector machines, predictive models, gut microbiota, energy expenditure, and genome are hot topics in this field. Future research directions may include the relationship between obesity and its consequences, such as diabetic retinopathy, as well as the interaction between obesity and epidemiology, such as COVID-19. CONCLUSION: Utilizing bibliometrics as a research tool and methodology, this study, for the first time, reveals the intrinsic relationship and developmental pattern among obesity research using machine learning methods, which provides academic references for clinicians and researchers in understanding the hotspots and cutting-edge issues as well as the developmental trend in this field to detect patients' obesity problems early and develop personalized treatment plans.

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Kidney Stone Disease (KSD) constitutes a multifaceted disorder, emerging from a confluence of environmental and genetic determinants, and is characterized by a high frequency of occurrence and recurrence. Our objective is to elucidate potential causative proteins and identify prospective pharmacological targets within the context of KSD. This investigation harnessed the unparalleled breadth of plasma protein and KSD pooled genome-wide association study (GWAS) data, sourced from the United Kingdom Biobank Pharma Proteomics Project (UKBPPP) and the FinnGen database version R10. Through Mendelian randomization analysis, proteins exhibiting a causal influence on KSD were pinpointed. Subsequent co-localization analyses affirmed the stability of these findings, while enrichment analyses evaluated their potential for pharmacological intervention. Culminating the study, a phenome-wide association study (PheWAS) was executed, encompassing all phenotypes (2408 phenotypes) catalogued in the FinnGen database version R10. Our MR analysis identified a significant association between elevated plasma levels of proteins FKBPL, ITIH3, and SERPINC1 and increased risk of KSD based on genetic predictors. Conversely, proteins CACYBP, DAG1, ITIH1, and SEMA6C showed a protective effect against KSD, documented with statistical significance (PFDR<0.05). Co-localization analysis confirmed these seven proteins share genetic variants with KSD, signaling a shared genetic basis (PPH3 + PPH4 > 0.8). Enrichment analysis revealed key pathways including hyaluronan metabolism, collagen-rich extracellular matrix, and serine-type endopeptidase inhibition. Additionally, our PheWAS connected the associated proteins with 356 distinct diseases (PFDR<0.05), highlighting intricate disease interrelations. In conclusion, our research elucidated a causal nexus between seven plasma proteins and KSD, enriching our grasp of prospective therapeutic targets.

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Neurodevelopmental disorders are currently one of the major complications faced by patients with congenital heart disease (CHD). Chronic hypoxia in the prenatal and postnatal preoperative brain may be associated with neurological damage and impaired long-term cognitive function, but the exact mechanisms are unknown. In this study, we find that delayed neuronal migration and impaired synaptic development are attributed to altered Atoh1 under chronic hypoxia. This is due to the fact that excessive Atoh1 facilitates expression of Kif21b, which causes excess in free-state α-tubulin, leading to disrupted microtubule dynamic stability. Furthermore, the delay in neonatal brain maturation induces cognitive disabilities in adult mice. Then, by down-regulating Atoh1 we alleviate the impairment of cell migration and synaptic development, improving the cognitive behavior of mice to some extent. Taken together, our work unveil that Atoh1 may be one of the targets to ameliorate hypoxia-induced neurodevelopmental disabilities and cognitive impairment in CHD.

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BACKGROUND & AIMS: Malnutrition is prevalent among hospitalised patients, and increases the morbidity, mortality, and medical costs; yet nutritional assessments on admission are not routine. This study assessed the clinical and economic benefits of using an artificial intelligence (AI)-based rapid nutritional diagnostic system for routine nutritional screening of hospitalised patients. METHODS: A nationwide multicentre randomised controlled trial was conducted at 11 centres in 10 provinces. Hospitalised patients were randomised to either receive an assessment using an AI-based rapid nutritional diagnostic system as part of routine care (experimental group), or not (control group). The overall medical resource costs were calculated for each participant and a decision-tree was generated based on an intention-to-treat analysis to analyse the cost-effectiveness of various treatment modalities. Subgroup analyses were performed according to clinical characteristics and a probabilistic sensitivity analysis was performed to evaluate the influence of parameter variations on the incremental cost-effectiveness ratio (ICER). RESULTS: In total, 5763 patients participated in the study, 2830 in the experimental arm and 2933 in the control arm. The experimental arm had a significantly higher cure rate than the control arm (23.24% versus 20.18%; p = 0.005). The experimental arm incurred an incremental cost of 276.52 CNY, leading to an additional 3.06 cures, yielding an ICER of 90.37 CNY. Sensitivity analysis revealed that the decision-tree model was relatively stable. CONCLUSION: The integration of the AI-based rapid nutritional diagnostic system into routine inpatient care substantially enhanced the cure rate among hospitalised patients and was cost-effective. REGISTRATION: NCT04776070 (https://clinicaltrials.gov/study/NCT04776070).

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Exploring the physical fractions of organic carbon and influencing mechanisms in grassland, forest, and farmland soils in wind erosion area can provide scientific basis for carbon sequestration, land utilization, wind prevention measure making, and fertility restoration of sloping farmland in the region. We examined the differentiation of aggregate organic carbon and density fractionation organic carbon in 0-15 cm soil layer across grassland, forest, and sloping farmland with 350 m long and 5° slope gradient in the wind erosion area of Meilisi District, Qiqihar, Heilongjiang, as well as the sloping farmland in the downhill section, middle section, and uphill section with every 100 m apart from the bottom to the top. The results showed that soil aggregates >2 mm were all destroyed across grassland, forest, and farmland soils, while the percentage of aggregates <0.053 mm was significantly higher than that of other sizes. The percentage of various soil aggregates, organic carbon content from density fractionations, and the proportion of organic carbon in the heavy fraction aggregates in farmland were significantly lower than that in grassland and forest soils. Soil aggregates in the uphill section of farmland were completely destroyed, and organic carbon content in various size aggregates and density fractionations gradually decreased with increasing slope. The proportion of organic carbon in the heavy fraction aggregates decreased, but that in light fraction aggregates increased gradually. Soil organic carbon and available potassium were key factors affecting aggregate stability, aggregate organic carbon content, and organic carbon content in density fractionations, while the loss of organic carbon in aggregate led to a decrease in aggregate stability. In summary, compared with grassland and forest soils, the stability of soil aggregates, the aggregate organic carbon content, the organic carbon content in density fractionations, and the proportion of organic carbon in heavy fraction aggregates in farmland all decreased in the wind erosion area of Northeast China. With the increases of slope, the aggregate organic carbon content, the organic carbon content in density fractionations, and the proportion of organic carbon in the heavy fraction aggregates in sloping farmland all decreased. Planting trees, conserving and expanding grassland area, and increasing the application of organic materials in sloping farmland in wind erosion area are effective approaches to stabilize and increase carbon storage, improve soil structure, and enhance soil quality.

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Background: This study aimed to assess the impact of the COVID-19 pandemic on the disease burden of Taiwan's notifiable infectious diseases (NIDs). We compared disease burdens between the pandemic and pre-pandemic year of 2020 (with non-pharmaceutical interventions (NPIs)) and 2010 (without NPIs), respectively, to understand the overall pandemic impact on NIDs in Taiwan. Methods: Forty-three national NIDs were analyzed using the Statistics of Communicable Diseases and Surveillance Report by estimating the premature death and disability via different transmission categories, sex, and age groups. The study evaluated the impact of diseases by assessing the years lost due to death (YLLs), the duration of living with disability (YLDs), and the overall disability-adjusted life years (DALYs) by measuring both the severity of the illness and its duration. Results: Taiwan recorded 1,577 (2010) and 1,260 (2020) DALYs per million population and lost 43 NIDs, decreasing 317 DALYs per million population. Tuberculosis, HIV/AIDS and acute hepatitis B/D were the leading causes of DALYs, accounting for 89% (2010) and 77% (2020). Conclusion: Overall, this study provided the first insight of changes in disease burdens in NIDs between pre- and post-COVID-19 based on a nationwide viewpoint for further preventive measures and interventions to be focused on specific diseases by associated health administrations and policies.

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BACKGROUND: This study aims to evaluate the therapeutic efficacy of combined treatment with pulsed electromagnetic fields (PEMFs) and platelet-rich plasma (PRP) injection in improving pain and functional mobility among patients with early-stage knee osteoarthritis (KOA). We hypothesize that this combined therapy can yield superior treatment outcomes. METHODS: Based on the different treatment regimens, we divided 48 patients diagnosed with Kellgren-Lawrence grades I-III KOA into 3 groups: the PRP group, the PEMFs group, and the PRP + PEMFs group. Each subtype of KOA patients was randomly assigned to different treatment groups. In the PRP group, patients received intra-articular injections of leukocyte-rich platelet-rich plasma once a month for 3 consecutive months. In the PEMFs group, patients receive low-frequency PEMFs irradiation therapy with a frequency of 30 Hz and intensity of 1.5 mT, once daily, 5 times a week, for a consecutive treatment period of 12 weeks. In the PRP + PEMFs group, patients receive both of the aforementioned treatment protocol. The treatment effects on patients are evaluated at baseline and at weeks 4, 8, and 12 post-treatment. Assessment parameters include visual analog scale for pain, Western Ontario and McMaster Universities Osteoarthritis Index, Lequesne Index score, and knee joint range of motion. RESULTS: From the 4th to the 12th week of treatment, the visual analog scale scores, Western Ontario and McMaster Universities Osteoarthritis Index scores, and Lequesne index scores of patients in all 3 groups gradually decreased, while knee joint mobility gradually increased (P < .05). At weeks 4, 8, and 12 after treatment, the PRP combined with PEMFs group showed significantly better scores compared to the PRP group and the PEMFs group, with statistically significant differences (P < .05). A total of 7 patients experienced adverse reactions such as knee joint swelling, low-grade fever, and worsening knee joint pain after treatment, all of which disappeared within 1 week after treatment. The incidence of complications did not differ significantly among the 3 groups (P = .67). CONCLUSION: PRP, PEMFs, and the combination of PRP and PEMFs therapy all effectively alleviate knee joint pain and improve joint function. However, compared to single treatment modalities, the combined therapy of PRP and PEMFs demonstrates more pronounced efficacy.

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Proteases play a crucial role in industrial enzyme formulations, with activity fluctuations significantly impacting product quality and yield. Therefore, developing a method for precise and rapid detection of protease activity is paramount. This study aimed to develop a rapid and accurate method for quantifying trypsin activity using integrated infrared (IR) and ultraviolet (UV) spectroscopy combined with data fusion techniques. The developed method evaluates the enzymatic activity of trypsin under varying conditions, including temperature, pH, and ionic strength. By comparing different data fusion methods, the study identifies the optimal model for accurate enzyme activity prediction. The results demonstrated significant improvements in predictive performance using the feature-level data fusion approach. Additionally, substituting the spectral data of the samples in the validation sets into the best prediction model resulted in a minimal residual difference between predicted and true values, further verifying the model's accuracy and reliability. This innovative approach offers a practical solution for the efficient and precise quantification of enzyme activity, with broad applications in industrial processes.

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Incorporating a sulfonyl group into parent molecules has been shown to effectively improve their synthetic applications and bioactivities. In this study, we present a straightforward and practical approach for the ring-opening reaction of alkenyl-aryl sulfonium salts with sodium sulfinates to produce a range of sulfur-containing alkyl sulfones. This method offers the benefits of mild reaction conditions, easily accessible raw materials, wide substrate applicability, good functional group compatibility, and operational simplicity. Importantly, the resulting products can be readily converted into sulfoxides, sulfones, sulfoximines, and some heterocyclic compounds.

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Pharyngitis can be caused by various pathogens, including viruses and bacteria. Group A streptococcus (GAS) is the most common bacterial cause of pharyngitis. However, distinguishing GAS pharyngitis from other types of upper respiratory tract infections is challenging in clinical settings. This often leads to empirical treatments and, consequently, the overuse of antimicrobial drugs. With the advancement of antimicrobial drug management and healthcare payment reform initiatives in China, reducing unnecessary testing and prescriptions of antimicrobial drugs is imperative. To promote standardized diagnosis and treatment of GAS pharyngitis, this article reviews various international guidelines on the clinical diagnosis and differential diagnosis of GAS pharyngitis, particularly focusing on clinical scoring systems guiding laboratory testing and antimicrobial treatment decisions for GAS pharyngitis and their application recommendations, providing a reference for domestic researchers and clinical practitioners.

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Understanding protein function is pivotal in comprehending the intricate mechanisms that underlie many crucial biological activities, with far-reaching implications in the fields of medicine, biotechnology, and drug development. However, more than 200 million proteins remain uncharacterized, and computational efforts heavily rely on protein structural information to predict annotations of varying quality. Here, we present a method that utilizes statistics-informed graph networks to predict protein functions solely from its sequence. Our method inherently characterizes evolutionary signatures, allowing for a quantitative assessment of the significance of residues that carry out specific functions. PhiGnet not only demonstrates superior performance compared to alternative approaches but also narrows the sequence-function gap, even in the absence of structural information. Our findings indicate that applying deep learning to evolutionary data can highlight functional sites at the residue level, providing valuable support for interpreting both existing properties and new functionalities of proteins in research and biomedicine.

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