Search

Abstract Background The prediction of coronavirus disease in 2019 (COVID-19) in broader regions has been widely researched, but for specific areas such as urban areas the predictive models were rarely studied. It may be inaccurate to apply predictive models from a broad region directly to a small area. This paper builds a prediction approach for small size COVID-19 time series in a city. Methods Numbers of COVID-19 daily confirmed cases were collected from November 1, 2022 to November 16, 2023 in Xuzhou city of China. Classical deep learning models including recurrent neural network (RNN), long and short-term memory (LSTM), gated recurrent unit (GRU) and temporal convolutional network (TCN) are initially trained, then RNN, LSTM and GRU are integrated with a new attention mechanism and transfer learning to improve the performance. Ten times ablation experiments are conducted to show the robustness of the performance in prediction. The performances among the models are compared by the mean absolute error, root mean square error and coefficient of determination. Results LSTM outperforms than others, and TCN has the worst generalization ability. Thus, LSTM is integrated with the new attention mechanism to construct an LSTMATT model, which improves the performance. LSTMATT is trained on the smoothed time series curve through frequency domain convolution augmentation, then transfer learning is adopted to transfer the learned features back to the original time series resulting in a TLLA model that further improves the performance. RNN and GRU are also integrated with the attention mechanism and transfer learning and their performances are also improved, but TLLA still performs best. Conclusions The TLLA model has the best prediction performance for the time series of COVID-19 daily confirmed cases, and the new attention mechanism and transfer learning contribute to improve the prediction performance in the flatten part and the jagged part, respectively.

Abstract This study aims to broaden the morphological scope of SDH-deficient renal cell carcinoma and to assist clinicians and pathologists in better understanding this entity to prevent misdiagnosis. This study used immunohistochemistry staining and the first-generation sequencing Sanger method for gene detection. It retrospectively analysed the clinical pathology, molecular characteristics, biological behaviour, and treatment information of one case of SDH-deficient renal cell carcinoma. The patient was a 57-year-old female with right back pain for more than 20 days and had no personal or family history of kidney tumours. In addition, the tumour cells had clear boundaries in morphology, and residual normal renal tubules could be seen around them. There were also ossification and adipose tissue around the tumour centre. The tumour cells were arranged in a glandular tubular and cord-like manner. Vacuolar and eosinophilic inclusion bodies could be observed in the cytoplasm. The nucleus was regular, the chromatin distribution was fine, and there were no obvious nucleoli. They were low-grade nuclei. In addition, no atypical mitosis or necrosis could been found. Furthermore, immunohistochemistry staining showed SDHB-negative and keratin 20 -positive tumour. Meanwhile, the first-generation sequencing also pointed out the presence of SDHB gene mutations in the tumour. After 12 months of follow-up, there was no evidence of disease recurrence in the patient. SDH-deficient renal cell carcinoma is a rare tumour associated with SDH gene germline mutations, and suspected cases should undergo SDHB immunohistochemistry staining. Most SDH-deficient renal cell carcinomas have a good prognosis, but undifferentiated cases require long-term follow-up.

BackgroundPrevious research on chlorinated paraffins (CPs) in fine particulate matter (PM2.5) has predominantly focused on short- and medium-chain chlorinated paraffins (SCCPs and MCCPs), and few studies could simultaneously determine short-, medium-, and long-chain chlorinated paraffins (LCCPs). Simultaneous extraction and determination of SCCPs, MCCPs, and LCCPs in PM2.5 could provide technical support for their environmental monitoring and human health risk assessment.ObjectiveTo establish a method based on QUEChERS pretreatment method in conjunction with ultra-performance liquid chromatography-quadrupole/orbitrap high resolution mass spectrometry for simultaneously determining the levels of SCCPs, MCCPs, and LCCPs in PM2.5. MethodsThe extraction solvents, extraction salts, and extraction steps of a QuEChERS method were optimized. The extraction efficiencies of the target substances were compared under 4 extraction solvents [acetonitrile, dichloromethane, and n-hexane solvents in sequence; acetonitrile: dichloromethane: n-hexane = 1: 1: 2 (v/v/v) mixed solvent; 1% acetic acid-acetonitrile: dichloromethane: n-hexane = 1: 1: 1 (v/v/v) mixed solvent; acetonitrile: dichloromethane: n-hexane = 1: 1: 1 (v/v/v) mixed solvent], 2 dehydrated salts (anhydrous MgSO4+NaCl and anhydrous Na2SO4+NaCl), 2 purification salts (C18 and PSA), and 4 vortex time (5, 7.5, 10, and 12.5 min) conditions. Then internal standard was utilized to estimate linear range and detection limit of the refined QuEChERS approach. ResultsThe linearities of SCCPs, MCCPs, and LCCPs were good in the range of 10~1000 ng·mL−1 with the correlation coefficients all greater than 0.96. The method detection limits (MDLs) ranged from 0.01 to 0.29 ng·m−3. The spiked recoveries of SCCPs, MCCPs, and LCCPs at the low, medium, and high concentrations were 77.38%-81.64%, 93.11%-99.78%, and 87.41%-101.39%, respectively, and the relative standard deviations (RSDs) were 2.90%-12.84%. This method was used to determine the CPs levels in 11 PM2.5 samples from Shijiazhuang. The positive rates of ∑SCCPs, ∑MCCPs, and ∑LCCPs were all 100%, the concentration ranges were 0.24-2.18 ng·m−3 (mean 0.84 ng·m−3), 0.17-1.67 ng·m−3 (mean 0.70 ng·m−3), and 0.01-0.16 ng·m−3 (mean 0.04 ng·m−3), respectively, and the percentages to ΣCPs were 52.95%, 44.39%, and 2.66%, respectively. ConclusionThe method established in this study is simple, time-saving, solvent saving, and can simultaneously detect SCCPs, MCCPs, and LCCPs in PM2.5, which is suitable for the determination of PM2.5 samples in large quantities, and can also provide a reference for the detection methods of other halogenated organic compounds in PM2.5.

Abstract This study investigates the potential anti-colorectal cancer (CRC) activity of IMT1, a novel specific inhibitor of mitochondrial RNA polymerase (POLRMT). Single-cell RNA sequencing data reveal that POLRMT is overexpressed in CRC cells. Additionally, elevated POLRMT expression was observed in local CRC tissues and cells, while its expression remained relatively low in colon epithelial tissues and cells. IMT1 significantly inhibited colony formation, cell viability, proliferation, cell cycle progression, and migration in both primary and immortalized CRC cells. Furthermore, IMT1 induced apoptosis and cell death in CRC cells. The inhibition of POLRMT by IMT1 disrupted mitochondrial functions in CRC cells, leading to mitochondrial depolarization, oxidative damage, and decreased ATP levels. Using targeted shRNA to silence POLRMT closely mirrored the effects of IMT1, showing robust anti-CRC cell activity. Crucially, the efficacy of IMT1 was diminished in CRC cells with silenced POLRMT. Contrarily, boosting POLRMT expression externally by a lentiviral construct promoted the proliferation and migration of CRC cells. Importantly, treatment with IMT1 or silencing POLRMT in primary colon cancer cells decreased the phosphorylation of Akt1-S6K1, whereas overexpression of POLRMT had the opposite effect. In nude mice, orally administering IMT1 potently restrained primary colon cancer xenograft growth. IMT1 suppressed POLRMT activity, disrupted mitochondrial function, hindered Akt-mTOR activation, and prompted apoptosis within the xenograft tissues. In addition, IMT1 administration suppressed lung metastasis of primary colon cancer cells in nude mice. These combined results highlight the robust anti-CRC activity of IMT1 by specifically targeting POLRMT.

Abstract As one of the main pathogenic factors of cardiovascular and cerebrovascular diseases, the incidence of metabolic diseases such as adiposity and metabolic dysfunction-associated steatotic liver disease (MASLD) is increasing annually. It is urgent and crucial to find more therapeutic targets to treat these diseases. Mainly expressed in brown adipocytes, mitochondrial uncoupling protein 1 (UCP1) is key to the thermogenesis of classical brown adipose tissue (BAT). Furthermore, white adipose tissue (WAT) is likely to express more UCP1 and subsequently acquire the ability to undergo thermogenesis under certain stimuli. Therefore, targeting and activating UCP1 to promote increased BAT thermogenesis and browning of WAT are helpful in treating metabolic diseases, such as adiposity and MASLD. In this case, the stimuli that activate UCP1 are emerging. Therefore, we summarize the thermogenic stimuli that have activated UCP1 in recent decades, among which cold exposure is one of the stimuli first discovered to activate BAT thermogenesis. As a convenient and efficient therapy with few side effects and good metabolic benefits, physical exercise can also activate the expression of UCP1 in adipose tissue. Notably, for the first time, we have summarized and demonstrated the stimuli of traditional Chinese medicines that can activate UCP1, such as acupuncture, Chinese herbal formulas, and Chinese medicinal herbs. Moreover, pharmacological agents, functional foods, food ingredients, and the gut microbiota are also commonly associated with regulating and activating UCP1. The identification and analysis of UCP1 stimuli can greatly facilitate our understanding of adipose tissue thermogenesis, including the browning of WAT. Thus, it is more conducive to further research and therapy for glucose and lipid metabolism disorders.

Summary: Background: Ovarian cancer has the highest mortality rate among gynaecological malignancies and is initially screened using ultrasound. Owing to the high complexity of ultrasound images of ovarian masses and the anatomical characteristics of the deep pelvic cavity, subjective assessment requires extensive experience and skill. Therefore, detecting the ovaries and ovarian masses and diagnose ovarian cancer are challenging. In the present study, we aimed to develop an automated deep learning framework, the Ovarian Multi-Task Attention Network (OvaMTA), for ovary and ovarian mass detection, segmentation, and classification, as well as further diagnosis of ovarian masses based on ultrasound screening. Methods: Between June 2020 and May 2022, the OvaMTA model was trained, validated and tested on a training and validation cohort including 6938 images and an internal testing cohort including 1584 images which were recruited from 21 hospitals involving women who underwent ultrasound examinations for ovarian masses. Subsequently, we recruited two external test cohorts from another two hospitals. We obtained 1896 images between February 2024 and April 2024 as image-based external test dataset, and further obtained 159 videos for the video-based external test dataset between April 2024 and May 2024. We developed an artificial intelligence (AI) system (termed OvaMTA) to diagnose ovarian masses using ultrasound screening. It includes two models: an entire image-based segmentation model, OvaMTA-Seg, for ovary detection and a diagnosis model, OvaMTA-Diagnosis, for predicting the pathological type of ovarian mass using image patches cropped by OvaMTA-Seg. The performance of the system was evaluated in one internal and two external validation cohorts, and compared with doctors’ assessments in real-world testing. We recruited eight physicians to assess the real-world data. The value of the system in assisting doctors with diagnosis was also evaluated. Findings: In terms of segmentation, OvaMTA-Seg achieved an average Dice score of 0.887 on the internal test set and 0.819 on the image-based external test set. OvaMTA-Seg also performed well in ovarian mass detection from test images, including healthy ovaries and masses (internal test area under the curve [AUC]: 0.970; external test AUC: 0.877). In terms of classification diagnosis prediction, OvaMTA-Diagnosis demonstrated high performance on image-based internal (AUC: 0.941) and external test sets (AUC: 0.941). In video-based external testing, OvaMTA recognised 159 videos with ovarian masses with AUC of 0.911, and is comparable to the performance of senior radiologists (ACC: 86.2 vs. 88.1, p = 0.50; SEN: 81.8 vs. 88.6, p = 0.16; SPE: 89.2 vs. 87.6, p = 0.68). There was a significant improvement in junior and intermediate radiologists who were assisted by AI compared to those who were not assisted by AI (ACC: 80.8 vs. 75.3, p = 0.00015; SEN: 79.5 vs. 74.6, p = 0.029; SPE: 81.7 vs. 75.8, p = 0.0032). General practitioners assisted by AI achieved an average performance of radiologists (ACC: 82.7 vs. 81.8, p = 0.80; SEN: 84.8 vs. 82.6, p = 0.72; SPE: 81.2 vs. 81.2, p > 0.99). Interpretation: The OvaMTA system based on ultrasound imaging is a simple and practical auxiliary tool for screening for ovarian cancer, with a diagnostic performance comparable to that of senior radiologists. This provides a potential tool for screening ovarian cancer. Funding: This work was supported by the National Natural Science Foundation of China (Grant Nos. 12090020, 82071929, and 12090025) and the R&D project of the Pazhou Lab (Huangpu) (Grant No. 2023K0605).

Distribution and removal of chemicals in wastewater treatment plants (WWTPs) have mainly relied on mathematical models. Existing exposure assessment models such as SimpleTreat, STPWIN are based on conventional activated sludge processes. There is an urgent need to develop an A2/O-based WWTPs exposure assessment model. Organophosphates (OPEs) have different physical and chemical properties and potential environmental risks. The fate and biodegradation kinetics of three types of OPEs, including alkyl OPEs, chlorinated OPEs and triphenyl ester OPEs, were studied in a laboratory-scale anaerobic/anoxic/aerobic (A2/O) sewage treatment system and batch reactors. The three types of OPEs had different anaerobic, anoxic, and aerobic removal characteristics. A compensation mechanism was found between anaerobic and anoxic/aerobic removal. When the hydraulic retention time decreased from 82 to 20.5 h, the removal efficiencies in the anaerobic unit decreased, while those in the anoxic unit increased; as a result, the total removal efficiencies remained high (>80 %) for all OPEs except triphenyl phosphate (65.5%–75.1 %). The concept of effective sludge concentration (MLSSeff) was proposed to illustrate the compensation mechanism and calibrate the second-order kinetic equation for predicting pollutant removal in the A2/O system: MLSSeff = ken × MLSS, where ken is a constant related to the influent total organic carbon content (TOC). The influent TOC contents of the anoxic and aerobic units affected the value of MLSSeff and OPEs removal. The results are of great significance for assessing OPEs exposure and predicting exposure to emerging micropollutants in sewage treatment systems.

This study outlines asthma burden trends across age, sex, regions and risk factors in ‘Belt and Road’ (B&R) countries from 1990 to 2019 using the Global Burden of Disease Study 2019 data. Incidence, mortality, prevalence, years lived with disability (YLDs), disability-adjusted life years (DALYs) and risk factors for asthma were measured. India, China and Indonesia bore the heaviest burden in 2019. Despite the significant decline in the average annual percent change for age-standardized mortality and years of life lost from 1990 to 2019, increases were observed in several East Asian, Central Asian, North African and Middle Eastern countries between 2010 and 2019. For both sexes, YLDs decreased in most B&R countries but increased in Montenegro, Saudi Arabia, Armenia, Vietnam and Oman. YLDs in Georgia, the United Arab Emirates and Albania increased in males but decreased in females. YLDs increased for those aged

Abstract Cloud products from geostationary satellites are the main alternative to surface synoptic cloud observations (SYNOP), and have become the baseline products for the development and construction of the Quality Management System (QMS) of integrated meteorological observation in China. This study addresses the needs of the China Meteorological Administration (CMA) for such an operational reform, and it is carried out using real-time observations obtained from the Fengyun-2E (FY-2E) geostationary satellite in 2012 to derive cloud total amount and classification using two different methods. Compared to surface SYNOP observations, the cloud total amount estimated by FY-2E is generally significantly lower (about 30% lower on average). The cloud classification resulting from the two methods used in this study is also significantly different from the classification obtained from surface observations (difference between 22 and 32%). The difference is smaller for the classification method, which uses additional auxiliary temperature profiles.

Abstract Acute lung injury (ALI) is characterized by an unregulated inflammatory reaction, often leading to severe morbidity and ultimately death. Excessive inflammation caused by M1 macrophage polarization and pyroptosis has been revealed to have a critical role in ALI. Recent study suggests that glycolytic reprogramming is important in the regulation of macrophage polarization and pyroptosis. However, the particular processes underlying ALI have yet to be identified. In this study, we established a Lipopolysaccharide(LPS)-induced ALI model and demonstrated that blocking glycolysis by using 2-Deoxy-D-glucose(2-DG) significantly downregulated the expression of M1 macrophage markers and pyroptosis-related genes, which was consistent with the in vitro results. Furthermore, our research has revealed that Phosphoglycerate Kinase 1(PGK1), an essential enzyme in the glycolysis pathway, interacts with NOD-, LRR- and pyrin domain-containing protein 3(NLRP3). We discovered that LPS stimulation improves the combination of PGK1 and NLRP3 both in vivo and in vitro. Interestingly, the absence of PGK1 reduces the phosphorylation level of NLRP3. Based on in vitro studies with mice bone marrow-derived macrophages (BMDMs), we further confirmed that siPGK1 plays a protective role by inhibiting macrophage pyroptosis and M1 macrophage polarization. The PGK1 inhibitor NG52 suppresses the occurrence of excessive inflammation in ALI. In general, it is plausible to consider a therapeutic strategy that focuses on modulating the relationship between PGK1 and NLRP3 as a means to mitigate the activation of inflammatory macrophages in ALI.

Abstract With the high incidence of urogenital tumors worldwide, urinary system tumors are among the top 10 most common tumors in men, with prostate cancer ranking first and bladder cancer fourth. Patients with resistant urogenital tumors often have poor prognosis. In recent years, researchers have discovered numerous specific cancer antigens, which has led to the development of several new anti-cancer drugs. Using protein analysis techniques, researchers developed immune checkpoint inhibitors (ICIs) and antibody-conjugated drugs (ADCs) for the treatment of advanced urogenital tumors. However, tumor resistance often leads to the failure of monotherapy. Therefore, clinical trials of the combination of ICIs and ADCs have been carried out in numerous centers around the world. This article reviewed phase 2 and 3 clinical studies of ICIs, ADCs, and their combination in the treatment of urogenital tumors to highlight safe and effective methods for selecting individualized therapeutic strategies for patients. ICIs activate the immune system, whereas ADCs link monoclonal antibodies to toxins, which can achieve a synergistic effect when the two drugs are combined. This synergistic effect provides multiple advantages for the treatment of urogenital tumors.

Walnut cake has the potential for use in preparing wood adhesives because of its richness in protein and carbohydrate. In this work, walnut cakes were treated with sodium periodate or potassium permanganate and then were directly used as wood adhesives. Their bonding properties, curing performances, thermal properties, and chemical structures were compared. The results showed that: (1) The oxidation by KMnO4was non-selective. The reaction was very intense, accompanied by the great variability of oxidation degree and degradation degree, enormous viscosity of oxidation products, high coating difficulty, and low content of active aldehyde groups. (2) The oxidation by NaIO4 was selective; the reaction was mild and easy to control. More active aldehydes could be produced and the treatment was beneficial for constructing a spatial net structure of the adhesive. (3) Compared with oxidation of KMnO4, the walnut cake adhesive prepared by NaIO4 oxidation exhibited a more compact structure, a higher crosslinking degree, low curing temperature, and high thermal stability after curing; its bonding performances met the requirements for Class II plywood specified in GB/T 17657(2013).

Abstract BackgroundUnderstanding the impact of asthma on public health is crucial for evidence-based prevention and treatment strategies. ObjectiveThis study aimed to identify the causes of asthma-related mortality in Pudong, Shanghai, China, offering insights for managing similar regions or countries in transition. MethodsMortality statistics were obtained from the Vital Statistics System of Pudong for 2005‐2021. Temporal patterns for the burden of asthma were examined. The crude mortality rate (CMR), age-standardized mortality rate by Segi’s world standard population (ASMRW), and years of life lost (YLL) for both all-cause and asthma-specific deaths were computed. Mortality rates associating with aging and other variables were categorized using the decomposition technique. The autoregressive integrated moving average model was used to forecast the asthma-related death mortality rate by 2035. ResultsA total of 1568 asthma-related deaths occurred during the follow-up period, with the CMR and ASMRW being 3.25/1055555PPP ConclusionsAsthma remains a significant public health challenge in transitioning countries, requiring increased attention and resource allocation.

Due to the complexity of environmental exposure factors and the low levels of exposure in the general population, identifying the key environmental factors associated with diabetes and understanding their potential mechanisms present significant challenges. This study aimed to identify key polycyclic aromatic hydrocarbons (PAHs) contributing to increased fasting blood glucose (FBG) concentrations and to explore their potential metabolic mechanisms. We recruited a highly PAH-exposed diesel engine exhaust testing population and healthy controls. Our findings found a positive association between FBG concentrations and PAH metabolites, identifying 1-OHNa, 2-OHPh, and 9-OHPh as major contributors to the rise in FBG concentrations induced by PAH mixtures. Specifically, each 10 % increase in 1-OHNa, 2-OHPh, and 9-OHPh concentrations led to increases in FBG concentrations of 0.201 %, 0.261 %, and 0.268 %, respectively. Targeted metabolomics analysis revealed significant alterations in metabolic pathways among those exposed to high levels of PAHs, including sirtuin signaling, asparagine metabolism, and proline metabolism pathway. Toxic function analysis highlighted differential metabolites involved in various dysglycemia-related conditions, such as cardiac arrhythmia and renal damage. Mediation analysis revealed that 2-aminooctanoic acid mediated the FBG elevation induced by 2-OHPh, while 2-hydroxyphenylacetic acid and hypoxanthine acted as partial suppressors. Notably, 2-aminooctanoic acid was identified as a crucial intermediary metabolic biomarker, mediating significant portions of the associations between the multiple different structures of OH-PAHs and elevated FBG concentrations, accounting for 16.73 %, 10.84 %, 10.00 %, and 11.90 % of these effects for 1-OHPyr, 2-OHFlu, the sum concentrations of 2- and 9-OHPh, and the sum concentrations of total OH-PAHs, respectively. Overall, our study explored the potential metabolic mechanisms underlying the elevated FBG induced by PAHs and identified 2-aminooctanoic acid as a pivotal metabolic biomarker, presenting a potential target for intervention.

Recurrent miscarriage (RM) is currently difficult to prevent and treat due to a lack of comprehensive understanding of its molecular mechanisms. The aim of this study was to identify genes potentially involved in the pathogenesis of RM and to observe their expression in the decidual tissues of RM patients. A total of 1823 differentially expressed genes (DEGs) and 148 differentially expressed proteins (DEPs) in decidual tissues between RM and control groups were identified. Subsequently, DCN, DPT, LUM, MFAP4, and ISG15 were identified from the DEGs/DEPs as RM-related hub genes through systematic bioinformatics analysis. Bioinformatics analysis of the single-cell dataset GSE214607 revealed that the expression of these five hub genes in the decidual stromal cells of RM patients appeared to be upregulated, while the RT-qPCR assay showed that their decidual expression levels were significantly increased in RM patients. Uterine Isg15expression was significantly increased, whereas the uterine expression of Dcn, Dpt, Lum, and Mfap4 was decreased in LPS-induced early pregnancy loss mice. MiR-16-5p, -21-3p, -27a-3p, and -941 were identified as potentially involved in the regulation of these five hub genes, and their decidual expression levels were significantly decreased in RM patients. The abnormally increased ISG15 expression in the decidual tissues of RM patients and uterine tissues of LPS-induced mice was validated by WB analysis. ISG15 expression was significantly reduced during the in vitro decidualization of human endometrial stromal cells (hESCs). Collectively, DCN, DPT, LUM, MFAP4, and ISG15 were identified as RM-related hub genes, and their expression in the decidual tissues of RM patients was significantly increased. The decidualization of hESCs was accompanied by reduced ISG15 expression, suggesting that increased decidual ISG15 expression might lead to early pregnancy loss by disrupting the decidualization process.

The TRIAD approach is widely used as a scientific methodology available to deal with site-specific contamination scenarios. In this study, a soil environmental risk assessments (SERA) of ten industrial sites in China was conducted by ranking risks across multiple sites based on the TRIAD method, and attempted to provide an alternative method for risk ranking, the VIKOR method, when reference screening values for assessing individual indicators are unavailable. The VIKOR method was developed to address ranking and selection problems in multi-criteria decision-making, particularly in complex decision environments. The present assessments analyzed 33 chemical analysis indicators, 14 ecotoxicological indicators, and 5 ecological indicators related to soil microbial diversity, subsequently compared and discuss the differences in risk ranking results obtained using the two methods. The results of the TRIAD approach showed that six sites (including F, G, E, B, J and I) were ranked high in the risk order, which were in the moderate-risk range (Integrated risk value, 0.5–0.75). While the results of the VIKOR method showed that four sites (including F, G, H, and E) were ranked high in the risk order, followed by another four sites (including D, A, B, and C). Although there were differences in the assessment results, both methods consistently identified three sites (including F, G, and E) with relatively higher risks. The results indicated that when the TRIAD method is not applicable in special cases, the VIKOR comprehensive evaluation can be used to infer the relative ecological risk of the assessed site.

Background: Distinguishing nontuberculous mycobacteria pulmonary disease (NTM-PD) from pulmonary tuberculosis (PTB) is a challenge especially in patients with positive sputum smear of acid-fast bacilli (AFB). This study aimed to compare and identify the clinical characteristics between the two diseases among patients with positive sputum AFB. Methods: From February 2017 through March 2021, patients with positive sputum AFB were reviewed in two hospitals of China. Among them, clinical data of NTM-PD and PTB patients was collected and compared. Results: 76 cases of NTM-PD and 92 cases of PTB were included in our study. When compared with PTB, NTM-PD patients were older (59.2 ± 11.4 vs 44.2 ± 19.5 years, P

BackgroundsDuring the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the disease has placed an enormous burden on the world’s healthcare and economy. The early risk assessment system based on a variety of machine learning (ML) algorithms may be able to provide more accurate advice on the classification of COVID-19 patients, offering predictive, preventive, and personalized medicine (PPPM) solutions in the future.MethodsIn this retrospective study, we divided a portion of the data into training and validation cohorts in a 7:3 ratio and established a model based on a combination of two ML algorithms first. Then, we used another portion of the data as an independent testing cohort to determine the most accurate and stable model and compared it with other scoring systems. Finally, patients were categorized according to risk scores and then the correlation between their clinical data and risk scores was studied.ResultsThe elderly accounted for the majority of hospitalized patients with COVID-19. The C-index of the model constructed by combining the stepcox[both] and survivalSVM algorithms was 0.840 in the training cohort and 0.815 in the validation cohort, which was calculated to have the highest C-index in the testing cohort compared to the other 119 ML model combinations. Compared with current scoring systems, including the CURB-65 and several reported prognosis models previously, our model had the highest AUC value of 0.778, representing an even higher predictive performance. In addition, the model’s AUC values for specific time intervals, including days 7,14 and 28, demonstrate excellent predictive performance. Most importantly, we stratified patients according to the model’s risk score and demonstrated a difference in survival status between the high-risk, median-risk, and low-risk groups, which means a new and stable risk assessment system was built. Finally, we found that COVID-19 patients with a history of cerebral infarction had a significantly higher risk of death.ConclusionThis novel risk assessment system is highly accurate in predicting the prognosis of patients with COVID-19, especially elderly patients with COVID-19, and can be well applied within the PPPM framework. Our ML model facilitates stratified patient management, meanwhile promoting the optimal use of healthcare resources.

Abstract Decidual macrophages (dMϕs) play critical roles in regulation of immune-microhomeostasis at maternal-fetal interface during pregnancy, but the underlying molecular mechanisms are still unclear. In this study, it was found that litter size and fetal weight were significantly reduced, whereas the rate of embryo resorption was increased in miR-3074-5p knock-in (3074-KI) pregnant mice, compared to that of wild-type (WT) pregnant mice. Plasma levels of pro-inflammatory cytokines in 3074-KI pregnant mice were also significantly elevated compared to WT pregnant mice at GD7.5. The quantity of M1-Mϕs in uterine tissues of 3074-KI pregnant mice was significantly increased compared to WT pregnant mice at GD13.5. Estrogen receptor-α (ERα) was validated to be a target of miR-3074-5p. Either miR-3074-5p overexpression or ERα knockdown promoted transcriptional activity of NF-κB/p65, induced M1-polarization and pyroptosis of THP1-derived Mϕs, accompanied with increased intracellular levels of cleaved Caspase-1, cleaved IL-1β, NLRP3, cleaved GSDMD and ASC aggregation. Furthermore, ERα could not only bind to NLRP3 or ASC directly, but also inhibit the interaction between NLRP3 and ASC. The endometrial miR-3074-5p expression level at the middle secretory stage of repeated implantation failure (RIF) patients was significantly decreased compared to that of control fertile women. These data indicated that miR-3074-5p could promote M1 polarization and pyroptosis of Mϕs via activation of NLRP3 inflammasome by targeting ERα, and the dysregulation of miR-3074-5p expression in dMϕs might damage the embryo implantation and placentation by interfering with inflammatory microenvironment at the maternal-fetal interface during early pregnancy.

Virtual reality technology promises to transform immersive experiences across various applications, particularly within office environments. Despite its potential, the challenge of achieving efficient text entry in virtual reality persists. This study addresses this obstacle by introducing a novel machine learning-based solution, namely, the two-stream long short-term memory typing method, to enhance text entry performance in virtual reality. The two-stream long short-term memory method utilizes the back-of-the-hand image, employing a long short-term memory network and a Kalman filter to enhance hand position tracking accuracy and minimize jitter. Through statistical analysis of the data collected in the experiment and questionnaire results, we confirmed the effectiveness of the proposed method. In addition, we conducted an extra experiment to explore the differences in users’ typing behavior between regular typing and virtual reality-based typing. This additional experiment provides valuable insights into how users adapt their typing behavior in different environments. These findings represent a significant step in advancing text entry within virtual reality, setting the stage for immersive work experiences in office environments and beyond.

As network systems become larger and more complex, there is an increasing focus on how to verify the security of systems that are at risk of being attacked. Automated penetration testing is one of the effective ways to achieve this. Uncertainty caused by adversarial relationships and the “fog of war” is an unavoidable problem in penetration testing research. However, related methods have largely focused on the uncertainty of state transitions in the penetration testing process, and have generally ignored the uncertainty caused by partially observable conditions. To address this new uncertainty introduced by partially observable conditions, we model the penetration testing process as a partially observable Markov decision process (POMDP) and propose an intelligent penetration testing decision method compatible with it. We experimentally validate the impact of partially observable conditions on penetration testing. The experimental results show that our method can effectively mitigate the negative impact of partially observable conditions on penetration testing decision. It also exhibits good scalability as the size of the target network increases.

Summary: Male infertility is a recognized side effect of chemoradiotherapy. Extant spermatogonial stem cells (SSCs) may act as originators for any subsequent recovery. However, which type of SSCs, the mechanism by which they survive and resist toxicity, and how they act to restart spermatogenesis remain largely unknown. Here, we identify a small population of Set domain-containing protein 4 (Setd4)-expressing SSCs that occur in a relatively dormant state in the mouse seminiferous tubule. Extant beyond high-dose chemoradiotherapy, these cells then activate to recover spermatogenesis. Recovery fails when Setd4+ SSCs are deleted. Confirmed to be of fetal origin, these Setd4+ SSCs are shown to facilitate early testicular development and also contribute to steady-state spermatogenesis in adulthood. Upon activation, chromatin remodeling increases their genome-wide accessibility, enabling Notch1 and Aurora activation with corresponding silencing of p21 and p53. Here, Setd4+ SSCs are presented as the originators of both testicular development and spermatogenesis recovery in chemoradiotherapy-induced infertility.

Natural products (NPs) play an irreplaceable role in the intervention of various diseases and have been considered a critical source of drug development. Many new pharmacodynamic compounds with potential clinical applications have recently been derived from NPs. These compounds range from small molecules to polysaccharides, polypeptides, proteins, self-assembled nanoparticles, and extracellular vesicles. This review summarizes various active substances found in NPs. The investigation of active substances in NPs can potentiate new drug development and promote the in-depth comprehension of the mechanism of action of NPs that can be beneficial in the prevention and treatment of human diseases.

BackgroundThe clinical presentation of Community-acquired pneumonia (CAP) in hospitalized patients exhibits heterogeneity. Inflammation and immune responses play significant roles in CAP development. However, research on immunophenotypes in CAP patients is limited, with few machine learning (ML) models analyzing immune indicators.MethodsA retrospective cohort study was conducted at Xinhua Hospital, affiliated with Shanghai Jiaotong University. Patients meeting predefined criteria were included and unsupervised clustering was used to identify phenotypes. Patients with distinct phenotypes were also compared in different outcomes. By machine learning methods, we comprehensively assess the disease severity of CAP patients.ResultsA total of 1156 CAP patients were included in this research. In the training cohort (n=809), we identified three immune phenotypes among patients: Phenotype A (42.0%), Phenotype B (40.2%), and Phenotype C (17.8%), with Phenotype C corresponding to more severe disease. Similar results can be observed in the validation cohort. The optimal prognostic model, SuperPC, achieved the highest average C-index of 0.859. For predicting CAP severity, the random forest model was highly accurate, with C-index of 0.998 and 0.794 in training and validation cohorts, respectively.ConclusionCAP patients can be categorized into three distinct immune phenotypes, each with prognostic relevance. Machine learning exhibits potential in predicting mortality and disease severity in CAP patients by leveraging clinical immunological data. Further external validation studies are crucial to confirm applicability.

Abstract Background Inflammation is associated with the pathophysiology of diabetic retinopathy (DR). Within the framework of complete dietary patterns, the Dietary Inflammatory Index (DII) was formulated to evaluate the inflammatory properties inherent in a diet. The main purpose of the current study was to assess the relationship between DII and DR using National Health and Nutrition Examination Survey (NHANES). Methods The original sample size included 1,148 diabetes patients out of 2005–2008 NHANES surveys. Twenty-four-hour dietary consumptions were used to calculate the DII scores. Demographic characteristics and retina examinations were collected for the comparison between DR and non-DR groups in diabetes patients. The relationship between DII and DR was analyzed by a logistic regression model. Results 227 subjects (110 non-DR and 117 DR) were selected in the analyses by using undersampling method to balance the sample size. Compared with non-DR group, DR group had higher DII values (1.14 ± 0.29 vs. 1.49 ± 0.21, p = 0.32), higher levels of HbA1c (6.8 ± 1.1% vs. 7.7 ± 2.6%, p

Norms have been effectively utilized to facilitate smooth interactions among agents. Norms are usually the global data that agents cannot directly access in complex environments; instead, norms can only be indirectly accessed by agents via maintaining their own beliefs about norms. Establishing norms using decentralized interaction-based methods has attracted much attention. However, the current methods overlook Industrial Internet of Things (IIoT) environments. In IIoT, there is a prevalent feature called “conflict-blocking”, where agents’ conflicting action strategies can block an interaction from being completed or even cause danger. To facilitate norm emergence in IIoT, we propose a framework to support agent decisions in conflict-blocking interactions. The framework aids in achieving system scalability by integrating the fusion of agent beliefs about norms. We prove that the proposed framework guarantees norm emergence. We also theoretically and experimentally analyze the time required for norm emergence under the influence of various factors, such as the number of agents. A vehicle movement simulator is also developed to vividly illustrate the process of norm emergence.

Abstract One-step adsorption separation of C2H4 from ternary C2 hydrocarbon mixtures remains an important and challenging goal for petrochemical industry. Current physisorbents either suffer from unsatisfied separation performance, poor stability, or are difficult to scale up. Herein, we report a strategy of constructing multiple supramolecular binding sites in a robust and scalable MOF (Al-PyDC) for highly efficient one-step C2H4 purification from ternary mixtures. Owing to suitable pore confinement with multiple supramolecular binding sites, Al-PyDC exhibits one of the highest C2H2 and C2H6 uptakes and selectivities over C2H4 at ambient conditions. The gas binding sites have been visualized by single-crystal X-ray diffraction studies, unveiling that the low-polarity pore surfaces with abundant electronegative N/O sites provide stronger multiple supramolecular interactions with C2H2 and C2H6 over C2H4. Breakthrough experiments showed that polymer-grade C2H4 can be separated from ternary mixtures with a maximum productivity of 1.61 mmol g−1. This material can be prepared from two simple reagents using a green synthesis method with water as the sole solvent, and its synthesis can be easily scaled to multikilogram batches. Al-PyDC achieves an effective combination of benchmark separation performance, high stability/recyclability, green synthesis and easy scalability to address major challenges for industrial one-step C2H4 purification.

Abstract We conducted a comprehensive investigation into the protective roles of boron (B) against trichloroacetic acid (TCA)-induced hepatotoxicity by assessing TCA exposure in vivo and exploring the potential mechanisms by which B protects against TCA-induced hepatotoxicity in vitro. For the in vivo study, we evaluated TCA-induced hepatotoxicity in adult male B6C3F1 mice exposed to 25, 50, 125, and 500 mg/kg/day of TCA, respectively, for 21 days. We found that the mice’s liver weight was significantly increased, and that there were changes in hepatic histopathology, particularly in mice treated with the highest dosage (500 mg/kg/day). TCA also increased the hepatic oxidoreductase activity of medium-chain and long-chain acyl-coenzyme A (CoA), which are biomarkers of peroxisome proliferation, in a dose-dependent manner. Subsequently, we established a TCA-induced HepG2 cell model of oxidative damage to estimate the cytotoxicity and determine the positive effects of B administration in vitro. We found that B administration significantly reduced oxidative stress by attenuating the production of TCA-induced reactive oxygen species and malondialdehyde. B also significantly downregulated the concentrations of certain cytokines, including interleukin (IL)-6, IL-8, and transforming growth factor-beta, which are predominantly associated with the p38 mitogen-activated protein kinase (MAPK) signaling pathway. In addition, B significantly upregulated phospho-p38 levels and downregulated Bax and p21 levels in the cytoplasm and downregulated p38 and p21 levels in the nucleus. Taken together, our findings suggest that the protective role of B against TCA-induced hepatotoxicity primarily involves alleviation of oxidative damage and cell apoptosis caused by TCA and might be mediated via the p38 MAPK pathway.

IntroductionThe research on cancer patients returning to work in China is still in its infancy, and there is no research and discussion on the adaptability to return-to-work for cancer patients. It is critical to develop the Adaptability to Return-to-Work Scale (ARTWS) for cancer patients and evaluate its psychometric properties.MethodsThe items of the initial scale were compiled based on the theoretical model and literature review results. Through two rounds of Delphi expert consultation (N = 15) and a pilot survey (N = 40), the initial scale was further checked and revised. Conduct a large sample survey (N = 376) and the construct validity and reliability of the ARTWS were assessed by confirmatory factor analysis (CFA) and exploratory factor analysis (EFA).ResultsThe final ARTWS consisted of 24 items. “Focusing on rehabilitation,” “Rebuilding Self-efficiency,” and “Adjusting plans” as common factors in determining adaptability to return to work for cancer patients, and the cumulative variance contribution rate for these three factors was 66.6%. The S-CVI of the total scale was 0.979. The Cronbach’s α coefficient was 0.937 and the 2-week test–retest reliability was 0.814.DiscussionARTWS has good correlation validity and can be used as a tool to measure the adaptability of cancer patients’ return to work. The presentation of the manuscript in Research Square (https://doi.org/10.21203/rs.3.rs-2323264/v1).

BackgroundObesity and metabolic syndrome pose significant health challenges in the United States (US), with connections to disruptions in sex hormone regulation. The increasing prevalence of obesity and metabolic syndrome might be associated with exposure to phthalates (PAEs). Further exploration of the impact of PAEs on obesity is crucial, particularly from a sex hormone perspective.MethodsA total of 7780 adult participants in the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2016 were included in the study. Principal component analysis (PCA) coupled with multinomial logistic regression was employed to elucidate the association between urinary PAEs metabolite concentrations and the likelihood of obesity. Weighted quartiles sum (WQS) regression was utilized to consolidate the impact of mixed PAEs exposure on sex hormone levels (total testosterone (TT), estradiol and sex hormone-binding globulin (SHBG)). We also delved into machine learning models to accurately discern obesity status and identify the key variables contributing most to these models.ResultsPrincipal Component 1 (PC1), characterized by mono(2-ethyl-5-carboxypentyl) phthalate (MECPP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), and mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) as major contributors, exhibited a negative association with obesity. Conversely, PC2, with monocarboxyononyl phthalate (MCNP), monocarboxyoctyl phthalate (MCOP), and mono(3-carboxypropyl) phthalate (MCPP) as major contributors, showed a positive association with obesity. Mixed exposure to PAEs was associated with decreased TT levels and increased estradiol and SHBG. During the exploration of the interrelations among obesity, sex hormones, and PAEs, models based on Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms demonstrated the best classification efficacy. In both models, sex hormones exhibited the highest variable importance, and certain phthalate metabolites made significant contributions to the model’s performance.ConclusionsIndividuals with obesity exhibit lower levels of TT and SHBG, accompanied by elevated estradiol levels. Exposure to PAEs disrupts sex hormone levels, contributing to an increased risk of obesity in US adults. In the exploration of the interrelationships among these three factors, the RF and XGBoost algorithm models demonstrated superior performance, with sex hormones displaying higher variable importance.