Improving the quality of water intake monitoring data is an urgent issue in current water management. The industrial water intake monitoring data obtained during the National Water Resources Monitoring Capacity Building Project promotion project was taken as a sample, and the common abnormal categories of water intake monitoring data were summarized, and the strategy of "rough screening-fine identification-reconstruction" was proposed. Considering the seasonal fluctuation law of water monitoring data, the multiscale industrial water monitoring abnormal data identification models were constructed based on segmented 3σ criterion, wavelet transform, and Fourier function. Moreover, the least squares support vector machine (LSSVM) model with adaptive inertia function and particle swarm optimization (PSO) was used to reconstruct the recovered anomaly data. The results indicate that the segmented 3σ criterion performs well for the rough processing of water intake monitoring data, identifying 26 data points that fall outside the corresponding threshold intervals. The Fourier function can effectively reduce the information loss associated with the wavelet transform, thereby improving the accuracy of abnormal data identification; based on verification feedback from monitoring users, 31 of the 38 detected abnormal points were confirmed as "demand-driven anomalies," yielding an identification accuracy of 81.6%. Furthermore, the inertia function-particle swarm optimization LSSVM model meets the high-precision requirements for abnormal data reconstruction and recovery, and its reconstruction accuracy is higher than that of the LSSVM, the PSO-LSSVM, and the traditional curve fitting method. Specifically, the inertia function-particle swarm optimization LSSVM achieves an average fitting error of 0.0286, representing reductions of 46.2% and 44.4% compared with the LSSVM (0.0532) and PSO-LSSVM (0.0514), respectively; moreover, when compared with the ground-truth values obtained from verification feedback, the reconstruction error rate is below 5%. Overall, the proposed multiscale mining and reconstruction strategy for industrial water intake monitoring abnormal data can provide a valuable methodological reference for enhancing the decision support capability of data in the National Water Resources Monitoring Capacity Building Project.
Narrative time is essential for understanding and remembering stories. Reconstructive memory theory posits that retrieving past events is not a mere reactivation of the original memory trace but involves a reorganization process informed by a combination of stored memories, general knowledge, and interpretative elements. Recent studies have shown that humans are remarkably accurate in judging the time-of-occurrence of fragments from a previously encoded narrative, but also that expectations and assumptions can compromise their performance. Here, we investigated the mechanism underlying the ability to infer the time-of-occurrence of videoclips extracted from a previously unencoded movie to elucidate the gradual integration of episodic and semantic information during the construction of narrative time. Across four experiments performed by different groups of human participants, we progressively manipulated the amount of available episodic information for the time-estimation task. Compared with the high precision observed for a known (i.e., previously encoded) movie, a robust decrease in performance was observed in the absence of prior encoding, irrespective of task repetitions. Exposing participants to additional episodic information (movie fragments) between task repetitions produced a gradual enhancement in task performance, especially when episodic cues were presented in chronological order. These results suggest that the temporal information provided by episodic cues can be exploited to gradually form a temporal scaffolding of the narrative, filling in the gaps between encoded pieces of information. This temporal representation, in turn, enables the dating of movie fragments, almost as if the movie had been encoded in its entirety.
The implementation of humanistic care services in nursing homes is of great importance for improving the quality of life of older people, enhancing their sense of self-worth, and meeting their spiritual and psychological needs. However, there is currently a lack of standardized criteria and validated tools for the systematic assessment of the quality of humanistic care in nursing homes. Therefore, the aim of this study was to develop a comprehensive indicator system for evaluating the quality of humanistic care in nursing homes. Guided by the Quality Caring Model and Maslow's hierarchy of needs theory as the theoretical framework, an initial evaluation indicator system was developed through a literature review and semi-structured interviews. Based on the preliminary indicator system, an expert consultation questionnaire was designed, and two rounds of Delphi expert consultation were conducted with 32 experts from relevant fields to further refine and optimize the evaluation indicator system by integrating expert opinions. Subsequently, the Analytic Hierarchy Process was applied to determine the weight of each indicator, and consistency testing was performed. The effective recovery rates of the two rounds of expert consultation questionnaires were 94.12% and 100.00%, respectively, and the expert authority coefficients were 0.850 and 0.859. The Kendall's coefficients of concordance for the two rounds of expert consultation were 0.167 and 0.269, respectively (P < 0.001). Ultimately, a quality evaluation indicator system for humanistic care in nursing homes was established, comprising 3 primary-level indicators, 10 secondary-level indicators, and 38 tertiary-level indicators. The quality evaluation indicator system for humanistic care in nursing homes developed in this study demonstrates a certain degree of scientific rigor and rationality. It can provide a theoretical reference for promoting the standardization of humanistic care processes and the systematization of quality management in nursing homes in China, and offers a systematic framework for the subsequent evaluation and improvement of the quality of humanistic care.
This study aimed to construct and validate an individualized prediction model for poor prognosis in elderly patients with Aneurysmal subarachnoid hemorrhage (aSAH) based on multimodal indicators such as clinical, imaging, and laboratory data. We retrospectively enrolled 241 consecutive patients with aSAH from January 2017 to December 2020 as the training set. An independent temporal validation set included 104 consecutive patients from January to September 2024, with assignment strictly by chronological order. In the training set, univariate analysis identified candidate predictors of poor prognosis, which were refined via LASSO regression. Significant variables were then entered into multivariate logistic regression to define independent predictors. Using these predictors, we constructed random forest (RF), support vector machine (SVM), and k-nearest neighbor models (KNN). Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Model interpretability and variable contributions were analyzed with SHapley Additive exPlanations (SHAP). There were no statistically significant differences in the baseline clinical data between the training set and the validation set (all P > 0.05). The results of multivariate Logistic regression analysis showed that World Federation of Neurosurgical Societies (WFNS) score, modified Fisher grade, intracerebral hematoma volume, maximum thickness of subarachnoid blood clots, C-reactive protein, and duration of symptomatic cerebral vasospasm were identified as independent risk factors for poor prognosis (all P < 0.05). The performance evaluation of the machine-learning models showed that the SVM model had the best discrimination, with AUCs of 0.838 (95% CI: 0.764-0.912) and 0.791 (95% CI: 0.688-0.895) in the training set and the validation set, respectively. The calibration curve showed a high consistency between the predicted probability and the actual risk. DCA indicated that the model had clinical net benefits within a wide range of thresholds. SHAP analysis confirmed that C-reactive protein, maximum thickness of subarachnoid clot, and intracerebral hematoma volume were the most important contributing factors to the increased risk of poor prognosis. This study successfully constructed and validated a prediction model for poor prognosis in elderly patients with aSAH based on multimodal indicators. The model has robust performance, clinically accessible indicators, and good interpretability, providing a valuable quantitative tool for dynamic risk stratification and implementation of stratified management.
To investigate the relationship between serum adiponectin, components of metabolic syndrome, and susceptibility to prostate cancer (PCa), and to construct a predictive nomogram model. A retrospective, 1:1 individual-matched case-control study was conducted. A total of 152 patients diagnosed with PCa at our hospital between January 2018 and May 2025 were enrolled as the PCa group, and 152 age-matched healthy males were selected as the control group. Serum adiponectin levels, metabolic syndrome components, and general demographic data were compared between the two groups. A nomogram prediction model was constructed based on conditional logistic regression analysis. Multivariate conditional logistic regression analysis showed glycated haemoglobin (OR = 6.360), waist-to-hip ratio (OR = 2.394), waist circumference (OR = 1.457), and triglycerides (OR = 3.777) as independent risk factors for PCa susceptibility (all p < 0.05), whereas high-density lipoprotein (OR = 0.341) and adiponectin (OR = 0.513) were identified as independent protective factors (all p < 0.05). The nomogram model constructed based on these six indicators predicted PCa susceptibility with an area under the curve (AUC) of 0.869 (95% CI: 0.843-0.894). Bootstrap validation indicated good model fit, and decision curve analysis suggested a clinical net benefit of the model. Serum adiponectin and components of metabolic syndrome are closely associated with PCa susceptibi.
To identify risk factors for postoperative complications in Stanford Type A aortic dissection (TAAD) patients, a risk prediction model was developed and validated. The study included a specific cohort of TAAD patients (aged 32-66 years) who underwent surgery with general anesthesia and cardiopulmonary bypass. A risk model was created using multivariate logistic regression analysis. Of the 522 patients, 174 (33.33%) experienced at least one major postoperative complication, including neurological, respiratory, renal, or other organ dysfunction. Univariate analysis identified several preoperative and intraoperative factors significantly associated with complications. Five independent predictors-age, BMI, stroke history, aortic cross-clamp time, and deep hypothermic circulatory arrest time-were retained in the multivariate logistic regression model as risk factors for postoperative complications in TAAD patients. Based on the logistic regression equation, logistic (P) = -7.458 + 0.056 × 1 + 0.102 × 2 + 0.374 × 3 + 0.018 × 4 + 0.026 × 5, a multivariate logistic regression risk model was developed to calculate the risk of postoperative complications, p = 1/1 + exp (-7.458 + 0.056 × 1 + 0.102 × 2 + 0.374 × 3 + 0.018 × 4 + 0.026 × 5). Model validation results showed that the area under the ROC curve was 0.879, the Hosmer-Lemeshow test p-value was 0.178, with a sensitivity of 77.59% and specificity of 85.34%.The incidence of postoperative complications following surgery for TAAD is relatively high. Both patient physical condition and surgical factors are associated with these complications. Establishing a risk model can help assess the risk of major postoperative complications in TAAD, providing clinically relevant information for perioperative monitoring and early intervention. Due to the specific age distribution of the study population, the results should be applied with caution to elderly patients (> 66 years).
The excessive consumption of fossil fuels, coupled with the severe shortage of freshwater resources, has precipitated grave energy and environmental challenges for society, the urgent need for sustainable solar-driven water evaporation technologies. This study draws inspiration from the microstructure of velvet ant cuticles, employing structural synergy to extend light propagation paths, thereby devising a novel strategy to enhance light absorption capabilities. Herein, a ZIF-67-derived composite supported on functionalized boron nitride nanosheets (f-BNNS) is fabricated to achieve efficient interfacial solar steam generation. Under light irradiation, f-BNNS may interact with ZIF-67 through their conjugated structures, which could facilitate the conversion of π-π* electronic transitions into thermal energy. In addition, cobalt nanoparticles derived from ZIF-67 are considered to act as localized photothermal centers. Their localized surface plasmon resonance (LSPR) effect and possible phonon-phonon coupling may contribute to interfacial heat localization, thereby promoting water evaporation. Benefiting from broadband light absorption, improved thermal management, and efficient photothermal conversion, the composite exhibits an evaporation rate of 1.38 kg·m-2·h-1 and an energy efficiency of 95.58% under simulated sunlight. This work presents a sustainable and magnetically recoverable photothermal system integrating MOF-derived photothermal components and thermally conductive scaffolds for high-efficiency solar-driven water evaporation.
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In stimulated emission depletion (STED) nanoscopy, high 3D resolution requires harnessing a 4Pi architecture using two opposing objectives. Here, we provide the step-by-step process for the construction and alignment of a 4Pi-STED nanoscope, commonly referred to as an 'isoSTED nanoscope'. The procedure guides interested researchers through the assembly of the optomechanical components, the configuration of the electronic and control devices, the alignment of the optical beam path and the assessment of the instrument's performance. The protocol offers a detailed roadmap for constructing an isoSTED nanoscope with adaptive optics in about 12 months and is designed for users with expertise in optical instrumentation builds. Once the instrument is finely calibrated, researchers can expect to achieve 3D biological images with isotropic sub-50-nm resolution in thick samples ≤35 µm in depth.
Anaplasma phagocytophilum is an obligate intracellular, tick-borne bacterial pathogen capable of causing disease and even mortality in various mammals, including humans. Non-coding RNAs play important regulatory roles in multicellular organisms, including innate and adaptive immune pathways, which control bacterial, parasitic, and viral infections. However, the global transcriptomic landscape encompassing both ncRNAs and mRNAs in HL-60 cells invaded by A. phagocytophilum remains unexplored. Cell apoptosis was evaluated by flow cytometry at multiple time points after HL-60 cell infection with A. phagocytophilum. Total RNA was extracted and analyzed by RNA sequencing (RNA-seq) to delineate expression alterations of long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) at 24 h post-infection (hpi). Bioinformatics methods were employed for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to elucidate the potential functions of these differentially expressed genes. Furthermore, an integrated bioinformatics approach was applied to systematically construct a competing endogenous RNA (ceRNA) network involving lncRNAs, miRNAs, and mRNAs. A. phagocytophilum infection accelerated HL-60 cell apoptosis at multiple time points, with the most significant effect observed at 24 hpi. Transcriptome profiling at 24 hpi identified substantial differential expression, including 487 lncRNAs, 550 mRNAs, and 22 miRNAs with statistically significant changes in expression. Then, expression patterns of eight lncRNAs, eight mRNAs, and seven miRNAs were experimentally validated through reverse transcription quantitative polymerase chain reaction (RT-qPCR), demonstrating strong correlation with RNA-seq results. Bioinformatics analyses revealed significant enrichment of differentially expressed mRNAs in three key pathways: the PI3K/Akt signaling pathway, the actin cytoskeleton regulation pathway and the p53 signaling pathway. Differentially expressed lncRNAs were largely related to the phospholipase D signaling pathway and pathways related to cortisol and aldosterone synthesis/secretion. The altered miRNAs showed predominant enrichment in Rap1 and NF-κB signaling pathways. Notably, computational reconstruction of the lncRNA-miRNA-mRNA ceRNA network identified hsa-miR-4518 and hsa-miR-3609 as central regulatory nodes. This comprehensive transcriptome study elucidates complex gene regulatory networks activated in HL-60 cells after A. phagocytophilum invasion, with particular emphasis on pathogen-modulated miRNA signatures that coordinate critical pathways governing host immune responses and microbial survival strategies. These findings elucidate previously uncharacterized molecular mechanisms underlying A. phagocytophilum pathogenesis and may provide actionable targets for novel therapeutics.
To conduct an epidemiological investigation on a case of severe fever with thrombocytopenia syndrome (SFTS) reported in Longyou County, Zhejiang Province in April 2025, and to perform nucleic acid detection and genetic characterization of Dabie bandavirus (DBV) in the patient's serum sample, aiming to provide scientific evidence and technical support for local SFTS prevention and control. An epidemiological investigation was conducted on the case. Serum samples from the confirmed case and close contacts, as well as related vector host specimens, were collected. DBV-specific nucleic acid was detected using real-time fluorescent quantitative PCR. The viral genome was amplified by RT-PCR and subjected to whole-genome sequencing. Professional computer software (SeqMan Ultra 17.2, Clustal X 2.1, BioEdit V7.2.6.1, and MEGA V7.0.26, etc.) was used for nucleotide and amino acid sequence alignment, phylogenetic tree construction, and calculation of genetic distances and homology percentages. The case had a history of tick bite and no history of travel outside the area. Fluorescence quantitative PCR detection showed that only the case's serum sample was positive for DBV nucleic acid, while serum samples from close contacts and related vector host specimens were all negative for DBV nucleic acid. After amplification and sequencing, the three fragments L, M, and S were successfully obtained. Genetic evolution analysis showed that they belonged to genotypes L (C), M (C), and S (A), respectively. Compared with the closest reference strain HZ2023-16 in the phylogenetic tree, the nucleotide homologies of the L, M, NS, and NP genes were 99.74%, 99.53%, 99.66%, and 99.19%, respectively, and the amino acid homologies were 100%, 99.72%, 99.66%, and 99.59%, respectively. Compared with the type C reference strain AHL/China/2011 (L(C)/M(C)/S(D)), the nucleotide homologies of the L, M, NS, and NP genes were 96.87%, 96%, 93.76%, and 95.66%, respectively, and the amino acid homologies were 99.42%, 98.51%, 98.29%, and 99.18%, respectively. Compared with the type A reference strain JX23XSH (L(A)/M(A)/S(A)), the nucleotide homologies of the L, M, NS, and NP genes were 96.24%, 95.97%, 95.35%, and 97.02%, respectively, and the amino acid homologies were 99.52%, 98.14%, 99.32%, and 99.18%, respectively. The average genetic distances of the L, M, and S genes compared with the HZ2023-16 reference strain were 0.003, 0.005, and 0.005, respectively; compared with the type C reference strain AHL/China/2011 (L(C)/M(C)/S(D)), they were 0.031, 0.04, and 0.053, respectively; compared with the type A reference strain JX23XSH (L(A)/M(A)/S(A)), they were 0.038, 0.04, and 0.038, respectively. Amino acid variation analysis showed that compared with the reference strains, this strain had 0-12 amino acid substitutions in the L protein (e.g., V68I, A140V), 3-20 substitutions in the M protein (e.g., D151E, G863S), 1-5 substitutions in the NS protein, and 1-2 substitutions in the NP protein. Combining the clinical manifestations, epidemiological history, and laboratory test results of the case, this outbreak can be determined as a local case of severe fever with thrombocytopenia syndrome. The strain is genetically closely related to human-derived reference strains and possesses specific genomic characteristics.
We aimed to develop and validate a radiopathomics model for predicting extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC). This retrospective study included 388 PTC patients with preoperative ultrasound and 400× cytology images from five medical centers between June 2017 and April 2024. We analyzed ultrasound and cytology images using Python and CellProfiler to extract features. Feature selection was performed using univariate analysis, Spearman correlation, and LASSO regression. The XGBoost algorithm was then used to build radiomics, pathomics, and combined radiopathomics models. The diagnostic performance of the radiopathomics model was compared with that of radiologists in an external validation cohort. Model and radiologist performance was evaluated using the area under the receiver operating characteristic curve (AUC). The radiopathomics model was visualized and interpreted through SHAP analysis. The radiopathomics model selected 21 features for construction. The AUC of the radiopathomics model was 0.887, 0.857, and 0.873 in the training, internal validation, and external validation cohorts, respectively, exceeding those of the single radiomics model (0.824, 0.787, and 0.804) and the pathomics model (0.809, 0.811, and 0.794). Compared with radiologists, the radiopathomics model improved the mean accuracy from 0.661 to 0.821. SHAP analysis showed that radiomics features played a major role in diagnosing ETE, while pathomics features provided additional support. The radiopathomics model serves as a promising auxiliary tool for preoperative ETE risk stratification and can help improve radiologists' diagnostic performance. Not Applicable.
This study explored the effects of inflammatory microenvironment on stem cells from the apical papilla (SCAP) for cell homing strategy-based pulp regeneration. Dental pulp stem cells (DPSCs) were treated with lipopolysaccharide (LPS) for 48 h, creating a conditioned medium (LPS-CM). The influence of LPS-CM on SCAP proliferation, migration, odontogenic and neurogenic differentiation, pro-angiogenetic effects, cell apoptosis and senescence were assessed. Following construction of the ectopic pulp regeneration model, treated dentin matrix (TDM) specimens were harvested after a 2-month implantation period and subjected to histological examination to assess changes in the regenerated tissues. We found that a moderate inflammatory microenvironment (LPS-5 CM) significantly enhanced SCAP proliferation, migration, odontogenic differentiation, and the formation of neuron-like cells. In contrast, a high-inflammatory microenvironment (LPS-10 CM) exerted inhibitory effects on these processes and concurrently induced cellular apoptosis and senescence. All LPS-CM groups promoted angiogenesis in vitro. Critically, only the LPS-5 CM group successfully facilitated the regeneration of well-vascularized pulp-like tissue in vivo. Inflammatory microenvironment performed a dual role in pulp regeneration. A moderate inflammatory stimulus enhances the regenerative functions of SCAP, while excessive inflammation is detrimental. This underscores the importance of inflammatory signals for successful cell homing-based pulp regeneration.
Longitudinal microbial interactions within a host are challenging to study, leading to a focus on constructed microbial communities in vitro settings. Here, we take advantage of a naturally defined microbial community within a spider host to study how elevated temperatures influence microbial dynamics and phenotypes across host generations. The spider Mermessus fradeorum hosts up to five endosymbionts, including a Wolbachia strain, W1, which induces feminisation, causing genetic males to develop as phenotypic females, skewing sex ratios and promoting symbiont spread. Despite this, Wolbachia 1 persists at intermediate frequencies in wild populations. We hypothesised that elevated temperatures might reduce penetration of the feminisation phenotype, potentially by altering symbiont dynamics and maternal transmission. We exposed spiderlings co-infected with Wolbachia 1 to elevated temperatures for one generation and measured feminisation rate, symbiont transmission, and titre across three generations. Feminisation was unaffected in the exposed (F1) generation but declined in subsequent generations (F2, F3) that were not directly exposed. This multigenerational effect was linked to shifts in symbiont community dynamics: low feminisation coincided with high abundance of one symbiont, Rickettsiella, a decline in Wolbachia 1 transmission, and complete loss of another symbiont, Tisiphia. Our findings demonstrate how environmental history shapes the evolutionary stability of microbial communities and their induced phenotype in their natural host.
Climate change is increasingly recognized as a psychological stressor, with older adults representing a particularly vulnerable yet understudied group. This study evaluated the psychometric performance of three climate anxiety measures-the Hogge Climate Anxiety Scale (HCAS), Clayton Climate Change Anxiety Scale (CCCA), and Simon Climate Anxiety Scale (SCAS)-among 279 Persian-speaking older adults in Iran. Using a cross-sectional design, we examined structural validity, diagnostic accuracy, measurement invariance, and reliability, employing the GAI-SF as the criterion measure. All three instruments demonstrated acceptable construct validity based on exploratory and confirmatory factor analyses. SCAS showed a stable three-factor structure and excellent internal consistency (ω = 0.97). In classification analyses, SCAS achieved the highest sensitivity (0.89) and overall diagnostic accuracy (DOR = 3.09), whereas CCCA demonstrated slightly higher specificity (0.36). Bland-Altman analysis indicated that HCAS had the lowest measurement bias relative to GAI-SF scores. Measurement invariance testing supported full scalar invariance for SCAS across gender and anxiety subgroups, while HCAS and CCCA achieved only partial invariance. Mokken scale analysis further confirmed strong scalability (H > 0.40) and satisfactory reliability (α > 0.85) across all measures, with CCCA showing the highest Loevinger's H (0.52). Age significantly predicted climate anxiety scores across all three scales (p < 0.001). Overall, SCAS emerged as the most robust and reliable instrument for assessing climate anxiety in Persian-speaking older adults, while HCAS showed the closest agreement with the criterion measure. These findings highlight the importance of culturally adapted, psychometrically sound tools for capturing climate anxiety in aging populations.
Despite anxiety and coronary heart disease being associated, longitudinal research investigating the bidirectional relationship between the formal diagnosis of anxiety disorders and myocardial infarction (MI) remains scarce. To investigate the bidirectional relationship between anxiety disorders and MI through a 15-year (2002-2016) longitudinal population-based retrospective cohort study using the National Health Insurance Research Database. We selected 34,979 patients diagnosed with an anxiety disorder based on their claim records during 2002-2004 and 5,189 patients with a diagnosis of MI based on their claim records during 2002-2004. In both analyses, the size of the comparison group was four times larger than that of the exposed group. A Cox proportional hazards model was used to estimate adjusted hazard ratios for developing anxiety disorders or MI after adjusting for sociodemographic factors. In the first analysis, patients with anxiety disorders had a statistically significant 1.28-fold higher risk of MI than those without. Among the patients with anxiety, those with higher age, male sex, or lower comorbidity had a significantly higher risk of MI after adjusting for sociodemographic variables. In the second analysis, patients with MI had a statistically significantly 2.08-fold higher risk of anxiety disorders than those without MI. Among the patients with MI, women and patients with lower comorbidities had a significantly higher risk of anxiety disorders after adjusting for sociodemographic variables. Our results demonstrate a meaningful connection between anxiety disorders and MI. By recognizing this relationship, healthcare providers can develop constructive strategies to effectively manage both conditions and improve patient outcomes.
To develop hemoglobin (Hb) percentiles and thresholds for defining anemia among infants aged 0-5 months in China. The National Nutrition and Health Systematic Survey for children aged 0 -17 years in China, a nationwide cross-sectional study, was conducted between 2019 and 2021. Hb levels were measured in infants using the HemoCue 201+ analyzer. Age- and sex-specific Hb distributions were constructed for "healthy infants", defined as those with adequate iron reserves at birth, exclusive breastfeeding, normal weight-for-age Z-score and weight growth velocity, normal neuropsychological development, and absence of acute or chronic diseases. A generalized additive model for location, scale, and shape was applied to fit the Hb percentiles. The 5th percentile of the Hb distribution was defined as the threshold for anemia. A total of 10,174 infants aged 0-5 months participated in the study, among whom 2,155 healthy infants were included in the analysis. Hb levels peaked at birth, gradually decreased to a nadir around 60 days after birth, and then rose to a plateau. The Hb thresholds defining anemia were 102.7 g/L, 96.3 g/L, 92.8 g/L, 95.4 g/L, 97.1 g/L, and 95.8 g/L for the 0-, 1-, 2-, 3-, 4-, and 5-month age groups, respectively. This study establishes hemoglobin thresholds for defining anemia in infants aged 0-5 months based on a nationwide, population-based dataset in China.
Over the past three decades, cell sheet technology has evolved from its original scaffold-free approach developed for epithelial cells into a versatile platform applicable to mesenchymal stromal cells (MSC). Despite numerous experimental achievements and convincing preclinical outcomes, MSC-based cell sheets have yet to reach the stage of a clinically reproducible product. This gap reflects not so much the technological limitations of the method as an incomplete understanding of its biological nature. MSC-based cell sheets represent more than a vehicle for cell delivery; they are self-organizing systems governed by intrinsic biophysical and morphogenetic principles. Their structural maturation involves (1) cellular condensation, (2) extracellular matrix deposition, and (3) contractile remodeling-processes that mirror the early phases of granulation tissue formation. Viewing the cell sheet as an in vitro model of connective tissue regeneration opens new research avenues extending beyond its therapeutic applications. This review summarizes the key milestones in the development of MSC-derived cell sheet technology, identifies major challenges and conceptual inconsistencies, and discusses the potential of studying these constructs as autonomous biological systems. The integration of mechanobiology, spatial omics technologies, and tissue engineering approaches may help reconceptualize MSC-based cell sheets both as tools for translational therapy and as a fundamental model for studying self-organizing regenerative processes.
Smoking tobacco is proven to be associated with higher colorectal cancer (CRC) risk. However, the metabolic pathways of smoking and how they relate to CRC risk are unclear. A total of 227,898 participants from the UK Biobank were included. A two-stage strategy with a combination of linear regression and Mendelian randomization analysis was employed to identify smoking-related metabolites. Then the elastic net (EN) regression model was used to construct smoking-related metabolic signature. Multivariable Cox regression, Mediation analysis, and interaction analysis were employed to illustrate the associations of smoking and smoking related metabolic signature on the risk of CRC. Furthermore, the interaction between genetic susceptibility and smoking metabolic signature was estimated based on relative excess risk due to interaction (RERI) and multiplicative-scale interaction. During a follow-up of 12.5 years, 3,328 incident CRC patients were observed. We identified 71 smoking-related metabolites and screened 42 of them metabolites by EN model to construct smoking-related metabolic signature. We observed smoking and smoking metabolic signature were both associated with the elevated risk of CRC, with a hazard ratio (HR) value of 1.27 and 1.38, respectively. Mediation analysis revealed metabolic signature can mediate 10.03% in the association between smoking and CRC risk. The additive interaction between genetic susceptibility and metabolic signature was statistically significant (RERI = 0.70, 95% confidence interval [CI] = 0.29-1.10). The detrimental impact of smoking on CRC risk is mediated partially by smoking metabolic signature, which is synergistic with genetic susceptibility.
Information on the burden of disease caused by foodborne pathogens in Northwest China is limited. This study aims to estimate the burden of acute gastroenteritis (AGE) and foodborne salmonellosis, shigellosis and norovirus gastroenteritis in Gansu Province. Using data from population surveys conducted during 2011-2015, the burden of AGE was estimated in Gansu Province. By determining the number of pathogen-specific cases from sentinel hospital surveillance conducted during 2014-2019, with adjustments applied for healthcare-seeking behavior and stool specimen submission based on data from population surveys, the burden of foodborne gastroenteritis caused by non-typhoidal Salmonella enterica, Shigella and norovirus in Gansu Province was calculated. A multiplier calculation model was developed, using Monte Carlo simulation to perform uncertainty estimation. The adjusted monthly prevalence of AGE was 4.0%, equivalent to an average of 0.53 episodes of AGE per person-year in the region. The multiplier for salmonellosis was estimated at 1,739, for shigellosis at 1,646, and for norovirus gastroenteritis at 2,787. The estimated annual incidence of foodborne salmonellosis, shigellosis and norovirus gastroenteritis were 242, 36, and 567 cases per 100,000 population, respectively, which substantially exceeded the incidence of reported foodborne disease. We concluded that AGE and foodborne gastroenteritis caused by non-typhoidal S. enterica, Shigella and norovirus constitute a significant health burden in Gansu Province. By continuously implementing population survey and enhanced sentinel hospital surveillance, with constructing a multiplier calculation model to estimate the burden of disease, we may gain a better understanding of the food safety situation in Northwest China.