Dihydrophenanthropyrans derived from the pseudobulbs of Pholidota chinensis alleviates neutrophilic inflammation by inhibiting MAPKs and calcium.

Five dihydrophenanthropyrans (1-5) were isolated from the pseudobulbs of Pholidota chinensis, among which 1,3-di(4'-hydroxybenzy)-imbricatin (3) was isolated from the nature for the first time. Their structures were elucidated and established through various spectroscopic methods. These compounds exhibited a potent inhibition effect on both N-formyl-methionyl-leucyl-phenylalanine (fMLF)-induced superoxide anion generation and elastase release with IC50 values ranging from 0.23 to 7.63 µM. Furthermore, dihydrophenanthropyrans (1-3) also demonstrated a dose-dependent reactive oxygen species (ROS) scavenging effect. In addition, dihydrophenanthropyrans (2-3) exhibited a dose-dependent reduction in the intracellular Ca2+ concentration ([Ca2+]i) in fMLF-activated human neutrophils. Moreover, dihydrophenanthropyrans (1-3) selectively inhibited the phosphorylation of c-Jun N-terminal kinases (JNKs) and p38, while only dihydrophenanthropyran (1) inhibited the phosphorylation of extracellular signal-regulated kinases (ERKs) in fMLF-activated human neutrophils. Notably, dihydrophenanthropyrans (1-3) did not affect protein kinase B (AKT) activity in these cells. These findings highlight the potent anti-inflammatory capabilities of dihydrophenanthropyrans, manifested through their ability to inhibit superoxide anion generation, suppress elastase release, and selectively modulate key signaling pathways in human neutrophils. This suggests that dihydrophenanthropyrans hold significant promise as therapeutic agents for conditions associated with neutrophil-mediated inflammation.

Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model.

Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.

A Wearable Thin-Film Hydrogel Laser for Functional Sensing on Skin.

Flexible photonics offers the possibility of realizing wearable sensors by bridging the advantages of flexible materials and photonic sensing elements. Recently, optical resonators have emerged as a tool to improve their oversensitivity by integrating with flexible photonic sensors. However, direct monitoring of multiple psychological information on human skin remains challenging due to the subtle biological signals and complex tissue interface. To tackle the current challenges, here, we developed a functional thin film laser formed by encapsulating liquid crystal droplet lasers in a flexible hydrogel for monitoring metabolites in human sweat (lactate, glucose, and urea). The three-dimensional cross-linked hydrophilic polymer serves as the adhesive layer to allow small molecules to penetrate from human tissue to generate strong light--matter interactions on the interface of whispering gallery modes resonators. Both the hydrogel and cholesteric liquid crystal microdroplets were modified specifically to achieve high sensitivity and selectivity. As a proof of concept, wavelength-multiplexed sensing and a prototype were demonstrated on human skin to detect human metabolites from perspiration. These results present a significant advance in the fabrication and potential guidance for wearable and functional microlasers in healthcare.

A comprehensive examination of temporal-seasonal variations of PM1.0 and PM2.5 in taiwan before and during the COVID-19 lockdown.

COVID-19 has been a significant global concern due to its contagious nature. In May 2021, Taiwan experienced a severe outbreak, leading the government to enforce strict Pandemic Alert Level 3 restrictions in order to curtail its spread. Although previous studies in Taiwan have examined the effects of these measures on air quality, further research is required to compare different time periods and assess the health implications of reducing particulate matter during the Level 3 lockdown. Herein, we analyzed the mass concentrations, chemical compositions, seasonal variations, sources, and potential health risks of PM1.0 and PM2.5 in Central Taiwan before and during the Level 3 lockdown. As a result, coal-fired boilers (47%) and traffic emissions (53%) were identified as the predominant sources of polycyclic aromatic hydrocarbons (PAHs) in PM1.0, while in PM2.5, the dominant sources of PAHs were coal-fired boilers (28%), traffic emissions (50%), and iron and steel sinter plants (22.1%). Before the pandemic, a greater value of 20.9 ± 6.92 µg/m3 was observed for PM2.5, which decreased to 15.3 ± 2.51 µg/m3 during the pandemic due to a reduction in industrial and anthropogenic emissions. Additionally, prior to the pandemic, PM1.0 had a contribution rate of 79% to PM2.5, which changed to 89% during the pandemic. Similarly, BaPeq values in PM2.5 exhibited a comparable trend, with PM1.0 contributing 86% and 65% respectively. In both periods, the OC/EC ratios for PM1.0 and PM2.5 were above 2, due to secondary organic compounds. The incremental lifetime cancer risk (ILCR) of PAHs in PM2.5 decreased by 4.03 × 10-5 during the pandemic, with PM1.0 contributing 73% due to reduced anthropogenic activities.

Cytotoxicity and Multi-Enzyme Inhibition of Nepenthes miranda Stem Extract on H838 Human Non-Small Cell Lung Cancer Cells and RPA32, Elastase, Tyrosinase, and Hyaluronidase Proteins.

The carnivorous pitcher plants of the genus Nepenthes have long been known for their ethnobotanical applications. In this study, we prepared various extracts from the pitcher, stem, and leaf of Nepenthes miranda using 100% ethanol and assessed their inhibitory effects on key enzymes related to skin aging, including elastase, tyrosinase, and hyaluronidase. The cytotoxicity of the stem extract of N. miranda on H838 human lung carcinoma cells were also characterized by effects on cell survival, migration, proliferation, apoptosis induction, and DNA damage. The cytotoxic efficacy of the extract was enhanced when combined with the chemotherapeutic agent 5-fluorouracil (5-FU), indicating a synergistic effect. Flow cytometry analysis suggested that the stem extract might suppress H838 cell proliferation by inducing G2 cell cycle arrest, thereby inhibiting carcinoma cell proliferation. Gas chromatography-mass spectrometry (GC-MS) enabled the tentative identification of the 15 most abundant compounds in the stem extract of N. miranda. Notably, the extract showed a potent inhibition of the human RPA32 protein (huRPA32), critical for DNA replication, suggesting a novel mechanism for its anticancer action. Molecular docking studies further substantiated the interaction between the extract and huRPA32, highlighting bioactive compounds, especially the two most abundant constituents, stigmast-5-en-3-ol and plumbagin, as potential inhibitors of huRPA32's DNA-binding activity, offering promising avenues for cancer therapy. Overall, our findings position the stem extract of N. miranda as a promising source of natural compounds for anticancer therapeutics and anti-skin-aging treatments, warranting further investigation into its molecular mechanisms and potential clinical applications.

Novel Cervical Endoscopic Unilateral Laminoforaminotomy for Bilateral Decompression in Cervical Spondylosis Myeloradiculopathy: A Technical Note and Clinical Results.

Background: This study investigates the efficacy of the Cervical Endoscopic Unilateral Laminoforaminotomy for Bilateral Decompression (CE-ULFBD) technique in treating cervical myeloradiculopathy, primarily caused by degenerative spondylosis. Traditionally managed through multisegmental anterior cervical discectomy and fusion (ACDF) or laminoplasty combined with foraminotomy, this condition has recently experienced a promising shift towards minimally invasive approaches, particularly endoscopic spinal decompression. While empirical evidence is still emerging, these techniques show potential for effective treatment. Method: The objective was to evaluate the outcomes of CE-ULFBD in achieving single or multilevel bilateral foraminal and central decompression, emphasizing the reduction of injury to posterior cervical muscles and the associated postoperative neck soreness common in conventional procedures. This paper delineates the surgical procedures involved in CE-ULFBD and presents the clinical outcomes of nine patients diagnosed with myeloradiculopathy due to severe cervical stenosis. Result: Assessments were conducted using the Visual Analogue Scale (VAS) for neck and arm pain and the Modified Japanese Orthopaedic Association scale (mJOA) for the activity measurement of daily living. Results indicated a considerable decrease in pain levels according to the VAS, coupled with significant improvements in functional capacities as measured by the mJOA scale. Additionally, no major postoperative complications were noted during the follow-up period. Conclusion: The study concludes that CE-ULFBD is a safe and effective approach for the treatment of cervical myeloradiculopathy resulting from severe cervical stenosis, offering a viable and less invasive alternative to traditional decompressive surgeries.

Residence time distribution in continuous virus filtration.

Regulatory authorities recommend using residence time distribution (RTD) to address material traceability in continuous manufacturing. Continuous virus filtration is an essential but poorly understood step in biologics manufacturing in respect to fluid dynamics and scale-up. Here we describe a model that considers nonideal mixing and film resistance for RTD prediction in continuous virus filtration, and its experimental validation using the inert tracer NaNO3. The model was successfully calibrated through pulse injection experiments, yielding good agreement between model prediction and experiment ( R 2 > ${R}^{2}\gt $ 0.90). The model enabled the prediction of RTD with variations-for example, in injection volumes, flow rates, tracer concentrations, and filter surface areas-and was validated using stepwise experiments and combined stepwise and pulse injection experiments. All validation experiments achieved R 2 > ${R}^{2}\gt $ 0.97. Notably, if the process includes a porous material-such as a porous chromatography material, ultrafilter, or virus filter-it must be considered whether the molecule size affects the RTD, as tracers with different sizes may penetrate the pore space differently. Calibration of the model with NaNO3 enabled extrapolation to RTD of recombinant antibodies, which will promote significant savings in antibody consumption. This RTD model is ready for further application in end-to-end integrated continuous downstream processes, such as addressing material traceability during continuous virus filtration processes.

Standardized approach for accurate and reliable model development of ion-exchange chromatography based on parameter-by-parameter method and consideration of extra-column effects.

Developing an accurate and reliable model for chromatographic separation that meets regulatory requirements and ensures consistency in model development remains challenging. In order to address this challenge, a standardized approach was proposed in this study with ion-exchange chromatography (IEC). The approach includes the following steps: liquid flow identification, system and column-specific parameters determination and validation, multi-component system identification, protein amount validation, steric mass action parameters determination and evaluation, and validation of the calibrated model's generalization ability. The parameter-by-parameter (PbP) calibration method and the consideration of extra-column effects were integrated to enhance the accuracy of the developed models. The experiments designed for implementing the PbP method (five gradient experiments for model calibration and one stepwise experiment for model validation) not only streamline the experimental workload but also ensure the extrapolation abilities of the model. The effectiveness of the standardized approach is successfully validated through an application about the IEC separation of industrial antibody variants, and satisfactory results were observed with R2 ≈ 0.9 for the majority of calibration and validation experiments. The standardized approach proposed in this work contributes significantly to improve the accuracy and reliability of the developed IEC models. Models developed using this standardized approach are ready to be applied to a broader range of industrial separation systems, and are likely find further applications in model-assisted decision-making of process development.

Parameter-by-parameter estimation method for adsorption isotherm in hydrophobic interaction chromatography.

Hydrophobic interaction chromatography (HIC) is used as a critical polishing step in the downstream processing of biopharmaceuticals. Normally the process development of HIC is a cumbersome and time-consuming task, and the mechanical models can provide a powerful tool to characterize the process, assist process design and accelerate process development. However, the current estimation of model parameters relies on the inverse method, which lacks an efficient and logical parameter estimation strategy. In this study, a parameter-by-parameter (PbP) method based on the theoretical derivation and simplifying assumptions was proposed to estimate the Mollerup isotherm parameters for HIC. The method involves three key steps: (1) linear regression (LR) to estimate the salt-protein interaction parameter and the equilibrium constant; (2) linear approximation (LA) to estimate the stoichiometric parameter and the maximum binding capacity; and (3) inverse method to estimate the protein-protein interaction parameter and the kinetic coefficient. The results indicated that the LR step should be used for dilution condition (loading factor below 5%), while the LA step should be conducted when the isotherm is in the transition or nonlinear regions. Six numerical experiments were conducted to implement the PbP method. The results demonstrated that the PbP method developed allows for the systematic estimation of HIC parameters one-by-one, effectively reducing the number of parameters required for inverse method estimation from six to two. This helps prevent non-identifiability of structural parameters. The feasibility of the PbP-HIC method was further validated by real-world experiments. Moreover, the PbP method enhances the mechanistic understanding of adsorption behavior of HIC and shows a promising application to other stoichiometric displacement model-derived isotherms.

A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan.

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health.

Estimating morning and evening commute period O3 concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology.

Elevated levels of ground-level ozone (O3) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R2 of 0.91. Internal and external validation procedures and verification of O3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies.

Exposure to PM2.5 Metal Constituents and Liver Cancer Risk in REVEAL-HBV.

BACKGROUND: Ambient particulate matter is classified as a human Class 1 carcinogen, and recent studies found a positive relationship between fine particulate matter (PM2.5) and liver cancer. Nevertheless, little is known about which specific metal constituent contributes to the development of liver cancer. OBJECTIVE: To evaluate the association of long-term exposure to metal constituents in PM2.5 with the risk of liver cancer using a Taiwanese cohort study. METHODS: A total of 13,511 Taiwanese participants were recruited from the REVEAL-HBV in 1991-1992. Participants' long-term exposure to eight metal constituents (Ba, Cu, Mn, Sb, Zn, Pb, Ni, and Cd) in PM2.5 was based on ambient measurement in 2002-2006 followed by a land-use regression model for spatial interpolation. We ascertained newly developed liver cancer (ie, hepatocellular carcinoma [HCC]) through data linkage with the Taiwan Cancer Registry and national health death certification in 1991-2014. A Cox proportional hazards model was utilized to assess the association between exposure to PM2.5 metal component and HCC. RESULTS: We identified 322 newly developed HCC with a median follow-up of 23.1 years. Long-term exposure to PM2.5 Cu was positively associated with a risk of liver cancer. The adjusted hazard ratio (HR) was 1.13 (95% confidence interval [CI], 1.02-1.25; P = 0.023) with one unit increment on Cu normalized by PM2.5 mass concentration in the logarithmic scale. The PM2.5 Cu-HCC association remained statistically significant with adjustment for co-exposures to other metal constituents in PM2.5. CONCLUSION: Our findings suggest PM2.5 containing Cu may attribute to the association of PM2.5 exposure with liver cancer.

Assessment of inhalation exposure to microplastic particles when disposable masks are repeatedly used.

Wearing masks to prevent infectious diseases, especially during the COVID-19 pandemic, is common. However, concerns arise about inhalation exposure to microplastics (MPs) when disposable masks are improperly reused. In this study, we assessed whether disposable masks release inhalable MPs when reused in simulated wearing conditions. All experiments were conducted using a controlled test chamber setup with a constant inspiratory flow. Commercially available medical masks with a three-layer material, composition comprising polypropylene (PP in the outer and middle layers) and polyethylene (PE in the inner layer), were used as the test material. Brand-new masks with and without hand rubbing, as well as reused medical masks, were tested. Physical properties (number, size, and shape) and chemical composition (polymers) were identified using various analytical techniques such as fluorescence staining, fluorescence microscopy, and micro-Fourier Transform Infrared Spectroscopy (µFTIR). Scanning Electron Microscopy (SEM) was used to scrutinize the surface structure of reused masks across different layers, elucidating the mechanism behind the MP generation. The findings revealed that brand-new masks subjected to hand rubbing exhibited a higher cumulative count of MPs, averaging approximately 1.5 times more than those without hand rubbing. Fragments remained the predominant shape across all selected size classes among the released MPs from reused masks, primarily through a physical abrasion mechanism, accounting for >90 % of the total MPs. The numbers of PE particles were higher than PP particles, indicating that the inner layer of the mask contributed more inhalable MPs than the middle and outer layers combined. The released MPs from reused masks reached their peak after 8 h of wearing. This implies that regularly replacing masks serves as a preventive measure and mitigates associated health risks of inhalation exposure to MPs.

Submonolayer biolasers for ultrasensitive biomarker detection.

Biomarker detection is key to identifying health risks. However, designing sensitive and single-use biosensors for early diagnosis remains a major challenge. Here, we report submonolayer lasers on optical fibers as ultrasensitive and disposable biosensors. Telecom optical fibers serve as distributed optical microcavities with high Q-factor, great repeatability, and ultralow cost, which enables whispering-gallery laser emission to detect biomarkers. It is found that the sensing performance strongly depends on the number of gain molecules. The submonolayer lasers obtained a six-order-of-magnitude improvement in the lower limit of detection (LOD) when compared to saturated monolayer lasers. We further achieve an ultrasensitive immunoassay for a Parkinson's disease biomarker, alpha-synuclein (α-syn), with a lower LOD of 0.32 pM in serum, which is three orders of magnitude lower than the α-syn concentration in the serum of Parkinson's disease patients. Our demonstration of submonolayer biolaser offers great potentials in high-throughput clinical diagnosis with ultimate sensitivity.

A Deep-Learning Algorithm-Enhanced Electrocardiogram Interpretation for Detecting Pulmonary Embolism.

Background: The early diagnosis of pulmonary embolism (PE) remains a challenge. Electrocardiograms (ECGs) and D-dimer levels are used to screen potential cases. Objective: To develop a deep learning model (DLM) to detect PE using ECGs and investigate the clinical value of false detections in patients without PE. Methods: Among patients who visited the emergency department between 2011 and 2019, PE cases were identified through a review of medical records. Non-PE ECGs were collected from patients without a diagnostic code for PE. There were 113 PE and 51,456 non-PE ECGs in the training and validation sets for developing the DLM, respectively, and 27 PE and 13,105 non-PE cases in an independent testing set for performance validation. A human-machine competition was conducted from the testing set to compare the performance of the DLM with that of physicians. Receiver operating characteristic (ROC) curves, sensitivity, and specificity were used to determine the diagnostic value. Survival analysis was used to assess the prognosis of the patients without PE, stratified by DLM prediction. Results: The DLM was as effective as physicians in diagnosing PE, with 70.8% sensitivity and 69.7% specificity. The area under the ROC curve of DLM was 0.778 in the testing set and up to 0.9 with D-dimer and demographic data. The non-PE patients whose ECG was misclassified as PE by DLM had higher all-cause mortality [hazard ratio (HR) 2.13 (1.51-3.02)] and risk of non-cardiovascular hospitalization [HR 1.55 (1.42-1.68)] than those correctly classified. Conclusions: A DLM-enhanced ECG system may prompt PE recognition and provide prognostic outcomes in patients with false-positive predictions.

Physics-informed neural networks to solve lumped kinetic model for chromatography process.

Numerical method is widely used for solving the mechanistic models of chromatography process, but it is time-consuming and hard to response in real-time. Physics-informed neural network (PINN) as an emerging technology combines the structure of neural network with physics laws, and is getting noticed for solving physics problems with a balanced accuracy and calculation speed. In this research, a proof-of-concept study was carried out to apply PINN to chromatography process simulation. The PINN model structure was designed for the lumped kinetic model (LKM) with all LKM parameters. The PINN structure, training data and model complexity were optimized, and an optimal mode was obtained by adopting an in-series structure with a nonuniform training data set focusing on the breakthrough transition region. A PINN for LKM (LKM-PINN) consisting of four neural networks, 12 layers and 606 neurons was then used for the simulation of breakthrough curves of chromatography processes. The LKM parameters were estimated with two breakthrough curves and used to infer the breakthrough curves at different residence times, loading concentrations and column sizes. The results were comparable to that obtained with numerical methods. With the same raw data and constraints, the average fitting error for LKM-PINN model was 0.075, which was 0.081 for numerical method. With the same initial guess, the LKM-PINN model took 160 s to complete the fitting, while the numerical method took 7 to 72 min, depending on the fitting settings. The fitting speed of LKM-PINN model was further improved to 30 s with random initial guess. Thus, the LKM-PINN model developed in this study is capable to be applied to real-time simulation for digital twin.

Hereditary transthyretin cardiac amyloidosis proven by endomyocardial biopsy: a single-centre retrospective study and literature review.

BACKGROUND: This study aimed to report the genotypes and phenotypes of hereditary transthyretin cardiac amyloidosis (hATTR-CA) in a Western Chinese cohort and review the genetic profiles of this disorder in the Chinese population. METHODS: Transthyretin (TTR) gene sequencing of probands diagnosed with TTR cardiac amyloidosis and their relatives was performed at West China Hospital of Sichuan University from January 2018 to December 2021. All patients underwent endomyocardial biopsy for light and electron microscopy examinations. Clinical and essential examination materials were retrospectively collected and analysed. RESULTS: TTR gene alteration was demonstrated in five probands and their two relatives. Three TTR variants were identified, namely, Ser23Asn, Glu54Leu and Thr60Ala. This study is the first to report Glu54Leu as pathogenic mutations in Chinese hATTR-CA patients. The Ser23Asn mutation was the most common mutation in this cohort. Five probands, including two males and three females, were all ethnic Han-Chinese. The median age at diagnosis and delay in diagnosis (interval from onset to diagnosis) was 56 years (range, 54-69 years) and 8 years (range, from 1 to 30 years), respectively. Three cases showed a defined family history of amyloidosis. Endomyocardial biopsies and TTR immunohistochemistry showed positive results in all patients. Two probands died 17.0 months and 21.0 months after diagnosis. CONCLUSIONS: We identified one novel TTR variants causing hATTR-CA in the West Han Chinese population. To avoid misdiagnosis or delayed diagnosis of hATTR-CA, TTR genotypic screening and endomyocardial biopsy should be performed as soon as possible in cases with heightened clinical suspicion.

Personal exposure to aldehydes and potential health risks among schoolchildren in the city.

Schoolchildren are sensitive to airborne aldehyde exposures. The knowledge regarding inhalation exposure to aldehydes and the factors influencing exposure in schoolchildren is limited. This study aimed to assess the variability and potential health risks of exposure to aldehydes (including formaldehyde) in schoolchildren. The important factors affecting personal exposure to aldehydes were also explored. Forty schoolchildren were recruited from the urban and suburban areas of Taiwan for aldehyde samplings and questionnaire surveys. Personal and indoor home samples of aldehydes were collected simultaneously during warm and cold seasons. We also identified the potential variables associated with aldehyde exposure based on the participant's responses to the questionnaires using mixed-effects models. The dominant three abundant aldehydes identified in personal exposure samples were formaldehyde (geometric mean, GM = 12.2 µg/m3), acetaldehyde (GM = 5.53 µg/m3), and hexaldehyde (GM = 8.79 µg/m3), accounting for approximately 80% of the total selected aldehydes. Higher personal exposure to aldehydes was observed during the warm season. Moreover, the within-subject variance was predominant, accounting for 66.6 to > 99.9% of the total variance in exposure. Schoolchildren had a high probability of overexposure to formaldehyde and acrolein, which resulted in an incremental lifetime cancer risk of 1.59 × 10-4 (95th percentile = 4.64 × 10-4). Season, location, household refurbishment, and indoor ventilation variables were significantly associated with personal exposure to aldehydes. The results can improve our understanding of aldehyde exposure among schoolchildren to propose mitigation strategies. These findings may be applied to further epidemiological studies.

[Effect of Biogas Slurry Return to Field on Heavy Metal Accumulation in Soil-crop System: A Meta-analysis].

To investigate the effects of biogas slurry return-to-field methods, the duration of biogas slurry return to field and the amount of heavy metals brought in from biogas slurry on the accumulation of heavy metals in soil-crop systems, and the importance of factors influencing heavy metal accumulation, 41 papers and 1972 pairs of data were integrated and analyzed. The results showed that the application of biogas slurry alone significantly increased the accumulation of As, Cd, Cr, Cu, and Zn in soil and As and Cr in crops by 20.5%, 15.2%, 25.6%, 18.7%, and 26.3% and 14.6% and 39.5%, respectively, and it had no significant effect on the accumulation of other heavy metals in crops. The combined application of biogas slurry and chemical fertilizers significantly increased the accumulation of soil Cr and Zn by 8.05% and 4.70% and decreased the accumulation of As by crops. Correlation analysis showed that the accumulation rates of soil As, Cd, and Cr were highly significantly and positively correlated (P<0.01) with the duration of biogas slurry return to field and soil organic matter (SOM) content, with correlation coefficients of 0.30, 0.15, and 0.13 and 0.22, 0.27, and 0.22, respectively; they were highly significantly and negatively correlated (P<0.01) with soil pH, with correlation coefficients of 0.16, 0.13, and 0.11, respectively. The heavy metals brought in by biogas slurry return to field promoted the accumulation of As, Cd, and Cr in soil and As, Cd, Cr, and Zn in crops, whereas the accumulation of Cd, Cu, and Zn in soil promoted the accumulation of Cd, Cu, and Zn in crops, with correlation coefficients of 0.45, 0.58, and 0.42, respectively. The main factors of heavy metal accumulation in the soil-crop systems were the duration of biogas slurry return to field, SOM, and soil pH.

Obesity as a clinical predictor for severe manifestation of dengue: a systematic review and meta-analysis.

BACKGROUND: Severe dengue often leads to poor clinical outcomes and high mortality; as a result, it is of vital importance to find prognostic factors associated with the severe form of dengue. Obesity is known to deteriorate many infectious diseases due to impaired immune responses. Several studies have suggested that obese patients with dengue infection tend to have more severe manifestations with poorer prognosis. However, a firm conclusion could not be drawn due to the varied results of these studies. Here, we aimed to conduct a systematic review and meta-analysis to investigate the association between obesity and dengue severity. METHODS: A literature search for relevant studies was conducted in PubMed, Embase, Ovid Medline and Cochrane from inception to September 9, 2022. The two main keywords were "dengue" and "obesity". Mantel-Haenszel method and random effects model was used to analyze the pooled odds ratio with 95% confidence intervals. RESULTS: A total of 15 article involving a total of 6,508 patients were included in the meta-analysis. Included patients in most studies were hospitalized pediatric patients. Only one study included adulthood data. Three cohort studies, four case-control studies, and one cross-sectional studies found a significant association between obesity and dengue severity. In contrast, three cohort studies, three case-control studies, and one cross-sectional study reported no significant relationship between obesity and dengue severity. Our analysis results showed that patient with obesity is 50% (OR = 1.50; 95%CI: 1.15-1.97) more likely to develop severe manifestation of dengue. CONCLUSION: This meta-analysis revealed that overweight could be a clinical predictor for severe disease for pediatric patients with dengue infection.