Cut-off values of haemoglobin and clinical outcomes in incident peritoneal dialysis: the PDTAP study.

BACKGROUND: To explore the cut-off values of haemoglobin (Hb) on adverse clinical outcomes in incident peritoneal dialysis (PD) patients based on a national-level database. METHODS: The observational cohort study was from the Peritoneal Dialysis Telemedicine-assisted Platform (PDTAP) dataset. The primary outcomes were all-cause mortality, major adverse cardiovascular events (MACE) and modified MACE (MACE+). The secondary outcomes were the occurrences of hospitalization, first-episode peritonitis and permanent transfer to haemodialysis (HD). RESULTS: A total of 2591 PD patients were enrolled between June 2016 and April 2019 and followed up until December 2020. Baseline and time-averaged Hb <100 g/l were associated with all-cause mortality, MACE, MACE+ and hospitalizations. After multivariable adjustments, only time-averaged Hb <100 g/l significantly predicted a higher risk for all-cause mortality {hazard ratio [HR] 1.83 [95% confidence interval (CI) 1.19-281], P = .006}, MACE [HR 1.99 (95% CI 1.16-3.40), P = .012] and MACE+ [HR 1.77 (95% CI 1.15-2.73), P = .010] in the total cohort. No associations between Hb and hospitalizations, transfer to HD and first-episode peritonitis were observed. Among patients with Hb ≥100 g/l at baseline, younger age, female, use of iron supplementation, lower values of serum albumin and renal Kt/V independently predicted the incidence of Hb <100 g/l during the follow-up. CONCLUSION: This study provided real-world evidence on the cut-off value of Hb for predicting poorer outcomes through a nation-level prospective PD cohort.

Evaluating gender bias in ML-based clinical risk prediction models: A study on multiple use cases at different hospitals.

BACKGROUND: An inherent difference exists between male and female bodies, the historical under-representation of females in clinical trials widened this gap in existing healthcare data. The fairness of clinical decision-support tools is at risk when developed based on biased data. This paper aims to quantitatively assess the gender bias in risk prediction models. We aim to generalize our findings by performing this investigation on multiple use cases at different hospitals. METHODS: First, we conduct a thorough analysis of the source data to find gender-based disparities. Secondly, we assess the model performance on different gender groups at different hospitals and on different use cases. Performance evaluation is quantified using the area under the receiver-operating characteristic curve (AUROC). Lastly, we investigate the clinical implications of these biases by analyzing the underdiagnosis and overdiagnosis rate, and the decision curve analysis (DCA). We also investigate the influence of model calibration on mitigating gender-related disparities in decision-making processes. RESULTS: Our data analysis reveals notable variations in incidence rates, AUROC, and over-diagnosis rates across different genders, hospitals and clinical use cases. However, it is also observed the underdiagnosis rate is consistently higher in the female population. In general, the female population exhibits lower incidence rates and the models perform worse when applied to this group. Furthermore, the decision curve analysis demonstrates there is no statistically significant difference between the model's clinical utility across gender groups within the interested range of thresholds. CONCLUSION: The presence of gender bias within risk prediction models varies across different clinical use cases and healthcare institutions. Although inherent difference is observed between male and female populations at the data source level, this variance does not affect the parity of clinical utility. In conclusion, the evaluations conducted in this study highlight the significance of continuous monitoring of gender-based disparities in various perspectives for clinical risk prediction models.

A novel dynamic cardiorespiratory coupling quantification method reveals the effect of aging on the autonomic nervous system.

Traditional cardiopulmonary coupling (CPC) based on the Fourier transform shares an inherent trade-off between temporal and frequency resolutions with fixed window designs. Therefore, a cross-wavelet cardiorespiratory coupling (CRC) method was developed to highlight interwave cardiorespiratory dynamics and applied to evaluate the age effect on the autonomic regulation of cardiorespiratory function. The cross-wavelet CRC visualization successfully reflected dynamic alignments between R-wave interval signal (RR intervals) and respiration. Strong and continuous CRC was shown if there was perfect temporal coordination between consecutive R waves and respiration, while CRC becomes weaker and intermittent without such coordination. Using real data collected on electrocardiogram (ECG) and respiratory signals, the heart rate variability (HRV) and CRC were calculated. Subsequently, comparisons were conducted between young and elderly individuals. Young individuals had significantly higher partial time and frequency HRV indices than elderly individuals, indicating stronger control of parasympathetic regulation. The overall coupling strength of the CRC of young individuals was higher than that of elderly individuals, especially in high-frequency power, which was significantly lower in the elderly group than in the young group, achieving better results than the HRV indices in terms of statistical significance. Further analyses of the time-frequency dynamics of CRC indices revealed that the coupling strength was consistently higher in the high-frequency (HF) band (0.15-0.4 Hz) in young participants compared to elderly individuals. The dynamic CRC between respiration and HRV indices was accessible by integrating the cross-wavelet spectrum and coherence. Young participants had a significantly higher level of CRC in the HF band, indicating that aging reduces vagus nerve modulation.

Irregular Scene Text Detection Based on a Graph Convolutional Network.

Detecting irregular or arbitrary shape text in natural scene images is a challenging task that has recently attracted considerable attention from research communities. However, limited by the CNN receptive field, these methods cannot directly capture relations between distant component regions by local convolutional operators. In this paper, we propose a novel method that can effectively and robustly detect irregular text in natural scene images. First, we employ a fully convolutional network architecture based on VGG16_BN to generate text components via the estimated character center points, which can ensure a high text component detection recall rate and fewer noncharacter text components. Second, text line grouping is treated as a problem of inferring the adjacency relations of text components with a graph convolution network (GCN). Finally, to evaluate our algorithm, we compare it with other existing algorithms by performing experiments on three public datasets: ICDAR2013, CTW-1500 and MSRA-TD500. The results show that the proposed method handles irregular scene text well and that it achieves promising results on these three public datasets.

Telemedicine and Clinical Outcomes in Peritoneal Dialysis: A Propensity-Matched Study.

INTRODUCTION: Telemedicine (TM) has shown to provide potential benefits on clinical outcomes in patients with chronic kidney disease but limited evidences published in the peritoneal dialysis (PD) population. This study aimed to explore the long-term effects of TM on the mortality and technique failure. METHODS: The Peritoneal Dialysis Telemedicine-assisted Platform Cohort Study (PDTAP Study) was conducted prospectively in 27 hospitals in China since 2016. Patient and practice data were collected through the doctor-end of the TM app (Manburs) for all participants. TM including self-monitoring records, on-line education materials, and real-time physician-patient contact was only performed for the patient-end users of the Manburs. The primary outcome was all-cause mortality. The secondary outcomes were cause-specific mortality and all-cause and cause-specific permanent transfer to hemodialysis. RESULTS: A total of 7,539 PD patients were enrolled between June 2016 and April 2019, with follow-up till December 2020. Patients were divided into two cohorts: TM group (39.1%) and non-TM group (60.9%). A propensity score was used to create 2,160 matched pairs in which the baseline covariates were well-balanced. There were significantly lower risks of all-cause mortality (HR 0.59 [0.51, 0.67], p < 0.001), CVD mortality (HR 0.59 [0.49, 0.70], p < 0.001), all-cause transfer to hemodialysis (0.57 [0.48, 0.67], p < 0.001), transfer to hemodialysis from PD-related infection (0.67 [0.51, 0.88], p = 0.003), severe fluid overload (0.40 [0.30, 0.55], p < 0.001), inadequate solute clearance (0.49 [0.26, 0.92], p = 0.026), and catheter-related noninfectious complications (0.41 [0.17, 0.97], p = 0.041) in the TM group compared with the non-TM group. CONCLUSION: This study indicated real-world associations between TM usage and reduction in patient survival and technique survival through a multicenter prospective cohort.

Graph Clustering With Graph Capsule Network.

Graph clustering, which aims to partition a set of graphs into groups with similar structures, is a fundamental task in data analysis. With the great advances made by deep learning, deep graph clustering methods have achieved success. However, these methods have two limitations: (1) they learn graph embeddings by a neural language model that fails to effectively express graph properties, and (2) they treat embedding learning and clustering as two isolated processes, so the learned embeddings are unsuitable for the subsequent clustering. To overcome these limitations, we propose a novel capsule-based graph clustering (CGC) algorithm to cluster graphs. First, we construct a graph clustering capsule network (GCCN) that introduces capsules to capture graph properties. Second, we design an iterative optimization strategy to alternately update the GCCN parameters and clustering assignment parameters. This strategy leads GCCN to learn cluster-oriented graph embeddings. Experimental results show that our algorithm achieves performance superior to that of existing graph clustering algorithms in terms of three standard evaluation metrics: ACC, NMI, and ARI. Moreover, we use visualization results to analyze the effectiveness of the capsules and demonstrate that GCCN can learn cluster-oriented embeddings.

Novel Plant Growth-Promoting Bacteria Isolated from Bauxite Residue: The Application for Revegetation.

Microbial inoculation with appropriate inorganic-organic amendments is a promising strategy for ecological rehabilitation at bauxite residue disposal areas. Nevertheless, research on screening suitable plant growth-promoting bacteria with tolerance to highly sodic-alkalinity is very limited in the literature. In this study, novel plant growth-promoting bacteria isolated from bauxite residue were used to investigate their potential for revegetation. Under high saline-alkalinity stress, inoculation of Z18 and Z28 increased the activity of antioxidative enzymes, whilst improving chlorophyll and carotenoid contents in ryegrass. Inoculation of the selected strains greatly reduced damage to organelles in ryegrass as observed by transmission electron microscopy. Based on 90-day soil incubation, inoculated strains improved physicochemical properties of bauxite residue and improved plant growth. These findings suggest that Z18 and Z28 may be selected as potential strains for vegetation establishment, aiding microbial remediation at bauxite disposal areas.

Improvements on physical conditions of bauxite residue following application of organic materials.

Soil formation and ecological rehabilitation is the most promising strategy to eliminate environmental risks of bauxite residue disposal areas. Its poor physical structure is nevertheless a major limitation to plant growth. Organic materials were demonstrated as effective ameliorants to improve the physical conditions of bauxite residue. In this study, three different organic materials including straw (5% W/W), humic acid (5% W/W), and humic acid-acrylamide polymer (0.2% and 0.4%, W/W) were selected to evaluate their effects on physical conditions of bauxite residue pretreated by phosphogypsum following a 120-day incubation experiment. The proportion of 2-1 mm macro-aggregates, mean weight diameter (MWD) and geometric mean diameter (GWD) increased following organic materials addition, which indicated that organic materials could enhance aggregate stability. Compared with straw, and humic acid, humic acid-acrylamide polymer application had improved effects on the formation of water-stable aggregates in the residues. Furthermore, organic materials increased the total porosity, total pore volume and average pore diameter, and reduced the micropore content according to nitrogen gas adsorption (NA) and mercury intrusion porosimetry (MIP) analysis, whilst enhancing water retention of the residues based on water characteristic curves. Compared with traditional organic wastes, humic acid-acrylamide polymer could be regarded as a candidate according to the comprehensive consideration of the additive amount and the effects on physical conditions of bauxite residue. These findings could provide a novel application to both Ca-contained acid solid waste and high-molecular polymers on ecological rehabilitation at disposal areas.

The Preciseness in Diagnosing Thyroid Malignant Nodules Using Shear-Wave Elastography.

Our study aimed to identify more accurate results about the diagnostic role of shear-wave elastography (SWE) for thyroid malignant nodules through a meta-analysis. Potential articles were searched in PubMed, Embase, and the Cochrane Library databases. Overall sensitivity and specificity with 95% confidence intervals (CIs) was used to represent the diagnostic accuracy of SWE. Summary receiver operating characteristic (ROC) curve was constructed to illustrate the results. In addition, χ² and I² tests were performed to assess heterogeneity. A value of p≤0.05 indicated significant heterogeneity. All the analysis was conducted in Meta-DiSc version 1.4 software. Twenty studies were included in the analysis. There were a total of 2,907 patients and 3,397 thyroid nodules included in the meta-analysis. Overall sensitivity and specificity were 0.68 (95% CI: 0.66-0.70) and 0.85 (95% CI: 0.84-0.87), respectively. The results showed the area under curve (AUC) was 0.9041, suggesting high accuracy of SWE for differentiating benign and malignant thyroid nodules. SWE showed high accuracy in identifying thyroid malignant nodules, suggesting it could serve as a diagnostic biomarker in thyroid nodules.

Phosphorylated mTOR and YAP serve as prognostic markers and therapeutic targets in gliomas.

Glioma is the most prevalent type of tumor in the brain and is comprised of grades I-IV, according to the WHO classification system. Grade IV glioma is also known as glioblastoma multiforme (GBM), the most malignant type of glioma. Glioma is characterized by a complex molecular background, and gene profiling studies have disclosed critical genetic events in human gliomas, which make targeted therapies the most promising therapeutic strategy. However, crosstalk between the targeted signaling pathways may hinder the efficacy of targeted therapies in gliomas. Therefore, it is necessary to identify effective markers to stratify patients for specific therapeutic procedures. Although several mechanisms have been proposed based on the crosstalk between PI3K/AKT/mTORC1 and Hippo/YAP pathways, the clinical significance of the two pathways has not yet been assessed in a combinatorial manner. In this study, we evaluated the two pathways in human glioma specimens and observed the positive correlation between protein levels of p-mTORS2448 and YAP in gliomas. The findings indicated that high expression of p-mTORS2448 and YAP correlated with poor overall survival of glioma patients. As p-mTORS2448 is a specific marker of mTORC1 activation, our results reveal a potential interaction between mTORC1 and YAP, which might functionally participate in the development and progression of gliomas. In support of this hypothesis, a combination of inhibitors targeting mTORC1 and YAP showed a better inhibitory effect on growth of glioma cell lines. Altogether, our work, for the first time, reveals that p-mTORS2448 and YAP can be used as markers of PI3K/AKT/mTORC1 and Hippo/YAP pathway activity to predict prognosis and are target candidates for personalized medicine.

PTP1B promotes aggressiveness of breast cancer cells by regulating PTEN but not EMT.

Metastasis is a complicated, multistep process and remains the major cause of cancer-related mortality. Exploring the molecular mechanisms underlying tumor metastasis is crucial for development of new strategies for cancer prevention and treatment. In this study, we found that protein tyrosine phosphatase 1B (PTP1B) promoted breast cancer metastasis by regulating phosphatase and tensin homolog (PTEN) but not epithelial-mesenchymal transition (EMT). By detecting PTP1B expression of the specimens from 128 breast cancer cases, we found that the level of PTP1B was higher in breast cancer tissues than the corresponding adjacent normal tissues. Notably, PTP1B was positively associated with lymph node metastasis (LNM) and estrogen receptor (ER) status. In vitro, disturbing PTP1B expression obviously attenuated cell migration and invasion. On the contrary, PTP1B overexpression significantly increased migration and invasion of breast cancer cells. Mechanistically, PTP1B knockdown upregulated PTEN, accompanied with an abatement of AKT phosphorylation and the expression of matrix metalloproteinase 2 (MMP2) and MMP7. Conversely, forced expression of PTP1B reduced PTEN and increased AKT phosphorylation as well as the expression of MMP2 and MMP7. Notably, neither EMT nor stemness of breast cancer cells was regulated by PTP1B. We also found that PTP1B acted as an independent prognostic factor and predicted poor prognosis in ER-positive breast cancer patients. Taken together, our findings provide advantageous evidence for the development of PTP1B as a potential therapeutic target for breast cancer, especially for ER-positive breast cancer patients.

Pharmacokinetics and apparent Michaelis constant for metabolite conversion of sorafenib in healthy and hepatocellular carcinoma-bearing rats.

Aim: Investigation of the pharmacokinetics of sorafenib (SRF) in rats with hepatocellular carcinoma (HCC). Methods: A reproducible ultra-HPLC-MS method for simultaneous determination of serum SRF, N-hydroxymethyl sorafenib and N-demethylation sorafenib. Results: Both the maximum serum concentrations (2.5-times) and the area under the serum concentration-time curve from 0 h to infinity (4.5-times) of SRF were observed to be significantly higher, with a greater than 3.0-fold decrease in the clearance rate in the HCC-bearing rats compared with these values in healthy animals. Further study revealed approximately 3.8- and 3.2-times increases in the apparent Michaelis constant for N-hydroxymethyl sorafenib and N-demethylation sorafenib conversions in the HCC-bearing rats. Conclusion: The low efficiency for the SRF conversions was a key contributor to the increased serum concentrations of SRF.

[Box: see text].

SemiSynBio: A new era for neuromorphic computing.

Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence (AI) systems, due to its advantages of adaptive learning and parallel computing. Meanwhile, biocomputing has seen ongoing development with the rise of synthetic biology, becoming the driving force for new generation semiconductor synthetic biology (SemiSynBio) technologies. DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks (ANNs), providing the possibility of executing neuromorphic computing at the molecular level. Herein, we briefly outline the principles of neuromorphic computing, describe the advances in DNA computing with a focus on synthetic neuromorphic computing, and summarize the major challenges and prospects for synthetic neuromorphic computing. We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing, which would be of widespread use in biocomputing, DNA storage, information security, and national defense.

Amino acids analysis reveals serum methionine contributes to diagnosis of the Kawasaki disease in mice and children.

BACKGROUND: Kawasaki disease (KD) patients often lack early and definitive diagnosis due to insufficient clinical criteria, whereas biomarkers might accelerate the diagnostic process and treatment. METHODS: The KD mouse models were established and thirteen amino acids were determined. A total of 551 serum samples were collected including KD patients (n = 134), HCs (n = 223) and KD patients after intravascular immunoglobulin therapy (IVIG, n = 194). A paired analysis of pre- and post-IVIG was employed in 10 KD patients. RESULTS: The pathological alterations of the aorta, myocardial interstitium and coronary artery vessel were observed in KD mice; the serum levels of methionine in KD mice (n = 40) were markedly altered and negatively correlated with the C-reactive protein levels. Consistent with the mouse model, serum methionine were significantly decreased in KD children, with the relative variation ratio of KD with HCs above 30% and AUROC value of 0.845. Serum methionine were correlated with Z-Score and significantly restored to the normal ranges after KD patient IVIG treatment. Another case-control study with 10 KD patients with IVIG sensitivity and 20 healthy controls validated serum methionine as a biomarker for KD patients with AUROC of 0.86. Elevation of serum DNMT1 activities, but no differences of DNMT3a and DNMT3b, were observed in KD patients when comparing with those in the HCs. CONCLUSIONS: Our study validated that serum methionine was a potential biomarker for KD, the alteration of which is associated with the activation of DNMT1 in KD patients.

Conserved MHC class I-presented dengue virus epitopes identified by immunoproteomics analysis are targets for cross-serotype reactive T-cell response.

Dengue fever and dengue hemorrhagic fever are significant global public health problems, and understanding the overall immune response to infection will contribute to appropriate management of the disease and its potentially severe complications. Live attenuated and subunit vaccine candidates, which are under clinical evaluation, induce primarily an antibody response to the virus and minimal cross-reactive T-cell responses. Currently, there are no available tools to assess protective T-cell responses during infection or after vaccination. In this study, we utilize an immunoproteomics process to uncover novel HLA-A2-specific epitopes derived from dengue virus (DV)-infected cells. These epitopes are conserved, and we report that epitope-specific cytotoxic lymphocytes (CTLs) are cross-reactive against all 4 DV serotypes. These epitopes have potential as new informational and diagnostic tools to characterize T-cell immunity in DV infection and may serve as part of a universal vaccine candidate complementary to current vaccines in trial.

Orthogonal threading-through-ß-sheet design of lung cancer EGFR extracellular domain-derived peptidic mimotopes binding to anti-EGFR antibody.

Human epidermal growth factor receptor (EGFR) has been established as a therapeutic target of lung cancer and other diverse tumors. The antibody drug Cetuximab has been developed to target the third subdomain III (TSDIII) of EGFR extracellular domain (ECD) by competitively inhibiting epidermal growth factor binding. In this study, we performed systematic investigation on the crystal complex structure of EGFR ECD domain with Cetuximab to create a residue importance profile for the TSDIII subdomain, based on which a number of U-shaped, double-stranded linear peptides were derived and cyclized to orthogonally thread through most hotspot residues and many responsible residues within the TSDIII ß-sheet plane; they represent mimotopes of the key antibody-recognition site of TSDIII subdomain. Computational analyses revealed that these linear peptides cannot spontaneously fold to the desired conformation in free state due to their intrinsic flexibility. Cell-free assays confirmed that the stapling can considerably improve the binding affinity of linear peptides to Cetuximab by up to 18-fold. The cOrt1 [3-18] cyclic peptide was measured to have the highest affinity in all designed linear and cyclic peptides.

Root architectures differentiate the composition of organic carbon in bauxite residue during natural vegetation.

Understanding plant root architectures induced changes in organic carbon accumulation and conversion is critical to predicting carbon cycling and screening appropriate plant species for ecological restoration on bauxite residue disposal areas. According to the ecological investigation of a weathered bauxite residue disposal area, three plants with different root architectures including Artemisia lavandulaefolia (A. lavandulaefolia), moss, and Zanthoxylum simulans (Z. simulans) were selected to investigate the rhizosphere effects on the composition and structure of organic carbon in bauxite residue. The physic-chemical properties, the contents and structure of different organic carbon fractions, and microbial communities of bauxite residue from rhizosphere and non-rhizosphere were analyzed. Plant growth decreased the saline-alkalinity, increased the contents of total organic carbon, particulate organic carbon and dissolved organic carbon, whilst enhancing the enzymatic activities of bauxite residue. Meanwhile, the rhizosphere effects had significant effects on the accumulation and stabilization of organic carbon in bauxite residue. A. lavandulaefolia had the strongest rhizosphere effects on the composition and structure of total organic carbon and dissolved organic carbon, whilst moss was more effective on the accumulation of particulate organic carbon in bauxite residue. Plant growth and root architecture changed the abundance of specific functional microorganisms and the complexity of microbial co-occurrence networks, thus elevating organic carbon levels in bauxite residue. During natural vegetation encroachment, rhizosphere exciting effects of the salt-tolerated plants could change the composition and structure of organic carbon fractions due to the comprehensive effectiveness of the improvement of physic-chemical properties and microbial communities. The findings improve our understanding of the responses of sequestration and stabilization of organic carbon pools to ecological restoration on bauxite residue disposal areas.

THE ALARMIN EFFECT OF HMGB1/RIP3 ON TRANSFUSION-RELATED ACUTE LUNG INJURY VIA TLR4/NF-ΚB OR MAPK PATHWAY.

ABSTRACT: Nonantibody-mediated transfusion-related acute lung injury (TRALI) may account for up to 25% of TRALI cases. This indicates the need for further research to understand the pathophysiological mechanisms involved beyond antibody mediation fully. During this research, a TRALI rat model was developed using the trauma-blood loss-massive transfusion method. The severity of pulmonary edema was checked via measurement of lung histopathological changes and the amount of Evans blue dye fluid and bronchoalveolar lavage fluid protein leakage. In addition, potential mechanisms of pathophysiological pathways and inflammation cascades were investigated in TRALI rats in vivo . The findings indicated that TRALI increased inflammatory cytokines and triggered elevated levels of high-mobility group box 1 (HMGB1)/receptor-interacting protein kinase 3 (RIP3), apoptosis protein, and mRNAs in the TM (TRALI model) group as opposed to the normal control. Furthermore, TRALI activated the toll-like receptor 4/nuclear factor kappa B and mitogen-activated protein kinase signaling pathways, which partially regulated the inflammatory response in the TRALI rats. A significant increase was observed in the inflammatory mediators HMGB1 and RIP3 during the early stages of TRALI, suggesting that these mediators could be used as diagnostic markers for TRALI. In addition, HMGB1 and RIP3 promoted the inflammatory response by stimulating the toll-like receptor 44/nuclear factor kappa B and mitogen-activated protein kinase signaling pathways in the lung tissue of rats. Identifying efficient agents from inflammatory mediators such as alarmin can be an innovative scheme for diagnosing and preventing TRALI. These findings give HMGB1 and RIP3 a strong theoretical and experimental foundation for clinical use.

Long-term weathering difference in soil-like indicators of bauxite residue mediates the multifunctionality driven by microbial communities.

Long-term weathering enhances the stability of ecosystem services and alters the microbiome, however, its influences on the relationship between microbial diversity and multifunctionality are still poorly understood. Hereby, 156 samples (0-20 cm) from five artificially divided functional zones including central bauxite residue zone (BR), the zone near residential area (RA), the zone near dry farming area (DR), the zone near natural forest area (NF), and the zone near grassland and forest area (GF) were collected in a typical disposal area to determine the heterogeneity and development of biotic and abiotic properties of bauxite residue. Residues in BR and RA exhibited higher values of pH, EC, heavy metals, and exchangeable sodium percentage compared to those in NF and GF. Our results showed a positive correlation between multifunctionality and soil-like quality during long-term weathering. Microbial diversity and microbial network complexity responded positively to multifunctionality within the microbial community, which was parallel with ecosystem functioning. Long-term weathering promoted oligotrophs-dominated bacterial assemblages (mostly Acidobacteria and Chloroflexi) and suppressed copiotrophs (including Proteobacteria and Bacteroidota), while the response of fungal communities was lower. Rare taxa from bacterial oligotrophs were particularly important at the current stage for maintaining ecosystem services and ensuring microbial network complexity. Our results underscore the significance of microbial ecophysiological strategies in response to changes in multifunctionality during long-term weathering, and highlight the necessity of conserving and augmenting the abundance of rare taxa to ensure the stable provision of ecosystem functions in bauxite residue disposal areas.

AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images.

Malaria is a significant public health concern, with ∼95% of cases occurring in Africa, but accurate and timely diagnosis is problematic in remote and low-income areas. Here, we developed an artificial intelligence-based object detection system for malaria diagnosis (AIDMAN). In this system, the YOLOv5 model is used to detect cells in a thin blood smear. An attentional aligner model (AAM) is then applied for cellular classification that consists of multi-scale features, a local context aligner, and multi-scale attention. Finally, a convolutional neural network classifier is applied for diagnosis using blood-smear images, reducing interference caused by false positive cells. The results demonstrate that AIDMAN handles interference well, with a diagnostic accuracy of 98.62% for cells and 97% for blood-smear images. The prospective clinical validation accuracy of 98.44% is comparable to that of microscopists. AIDMAN shows clinically acceptable detection of malaria parasites and could aid malaria diagnosis, especially in areas lacking experienced parasitologists and equipment.