A comprehensive genetic map of cytokine responses in Lyme borreliosis.

The incidence of Lyme borreliosis has risen, accompanied by persistent symptoms. The innate immune system and related cytokines are crucial in the host response and symptom development. We characterized cytokine production capacity before and after antibiotic treatment in 1,060 Lyme borreliosis patients. We observed a negative correlation between antibody production and IL-10 responses, as well as increased IL-1Ra responses in patients with disseminated disease. Genome-wide mapping the cytokine production allowed us to identify 34 cytokine quantitative trait loci (cQTLs), with 31 novel ones. We pinpointed the causal variant at the TLR1-6-10 locus and validated the regulation of IL-1Ra responses at transcritpome level using an independent cohort. We found that cQTLs contribute to Lyme borreliosis susceptibility and are relevant to other immune-mediated diseases. Our findings improve the understanding of cytokine responses in Lyme borreliosis and provide a genetic map of immune function as an expanded resource.

Early detection and prognosis evaluation for hepatocellular carcinoma by circulating tumour DNA methylation: A multicentre cohort study.

BACKGROUND: Early diagnosis of hepatocellular carcinoma (HCC) can significantly improve patient survival. We aimed to develop a blood-based assay to aid in the diagnosis, detection and prognostic evaluation of HCC. METHODS: A three-phase multicentre study was conducted to screen, optimise and validate HCC-specific differentially methylated regions (DMRs) using next-generation sequencing and quantitative methylation-specific PCR (qMSP). RESULTS: Genome-wide methylation profiling was conducted to identify DMRs distinguishing HCC tumours from peritumoural tissues and healthy plasmas. The twenty most effective DMRs were verified and incorporated into a multilocus qMSP assay (HepaAiQ). The HepaAiQ model was trained to separate 293 HCC patients (Barcelona Clinic Liver Cancer (BCLC) stage 0/A, 224) from 266 controls including chronic hepatitis B (CHB) or liver cirrhosis (LC) (CHB/LC, 96), benign hepatic lesions (BHL, 23), and healthy controls (HC, 147). The model achieved an area under the curve (AUC) of 0.944 with a sensitivity of 86.0% in HCC and a specificity of 92.1% in controls. Blind validation of the HepaAiQ model in a cohort of 523 participants resulted in an AUC of 0.940 with a sensitivity of 84.4% in 205 HCC cases (BCLC stage 0/A, 167) and a specificity of 90.3% in 318 controls (CHB/LC, 100; BHL, 102; HC, 116). When evaluated in an independent test set, the HepaAiQ model exhibited a sensitivity of 70.8% in 65 HCC patients at BCLC stage 0/A and a specificity of 89.5% in 124 patients with CHB/LC. Moreover, HepaAiQ model was assessed in paired pre- and postoperative plasma samples from 103 HCC patients and correlated with 2-year patient outcomes. Patients with high postoperative HepaAiQ score showed a higher recurrence risk (Hazard ratio, 3.33, p < .001). CONCLUSIONS: HepaAiQ, a noninvasive qMSP assay, was developed to accurately measure HCC-specific DMRs and shows great potential for the diagnosis, detection and prognosis of HCC, benefiting at-risk populations.

Multimorbidity and emergency hospitalisations during hot weather.

BACKGROUND: People with chronic diseases are a commonly listed heat-vulnerable group in heat-health action plans. While prior research identifies multiple health conditions that may increase vulnerability to ambient heat, there is minimal evidence regarding the implications of multimorbidity (two or more chronic diseases). METHODS: From the statewide hospital registry of Queensland, Australia, we identified people aged ≥15 years who had emergency hospitalisation(s) between March 2004 and April 2016 and previously had 0, 1, 2, or ≥3 of five chronic diseases: cardiovascular disease, diabetes, mental disorders, asthma/COPD, and chronic kidney disease. We conducted time-stratified case-crossover analyses to estimate the odds ratio of hospitalisations associated with ambient heat exposure in people with different numbers, types, and combinations of chronic diseases. Ambient heat exposure was defined as a 5 °C increase in daily mean temperature above the median. FINDINGS: There were 2,263,427 emergency hospitalisations recorded (48.7% in males and 51.3% in females). When the mean temperature increased, hospitalisation odds increased with chronic disease number, particularly in older persons (≥65 years), males, and non-indigenous people. For instance, in older persons with 0, 1, 2, or ≥3 chronic diseases, the odds ratios associated with ambient heat exposure were 1.00 (95% confidence interval: 0.96, 1.04), 1.06 (1.02, 1.09), 1.08 (1.02, 1.14), and 1.13 (1.07, 1.19), respectively. Among the chronic diseases, chronic kidney disease, and asthma/COPD, either existing alone, together, or in combination with other diseases, were associated with the highest odds of hospitalisations under ambient heat exposure. INTERPRETATION: While individuals with multimorbidity are considered in heat-health action plans, this study suggests the need to consider specifically examining them as a distinct and vulnerable subgroup. FUNDING: Wellcome.

M6[Cd2(CO3)2(B12O18)(OH)6] (M = K, Rb): Borate-Carbonates with Two CdCO3 Embedded in a Cyclic Oxoboron Anion.

Two metal borate-carbonates, M6[Cd2(CO3)2(B12O18)(OH)6] [M = K (1), Rb (2)], were obtained under surfactant-thermal conditions. In 1 and 2, each cyclic [(B12O18)(OH)6]6- anion captures two CdCO3 in two sides of the rings and finally forms the unusual (CdCO3)2@[(B12O18)(OH)6] cluster. Both 1 and 2 show moderate birefringence. Density functional theory calculations indicate that carbonate groups have a major contribution to electron-related optical transition.

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals.

OBJECTIVE: Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge. APPROACH: Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network (ResGCN) block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance. MAIN RESULTS: The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls (NC), in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve (AUC) of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks. SIGNIFICANCE: The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.

A Novel Mis-Seg-Focus Loss Function Based on a Two-Stage nnU-Net Framework for Accurate Brain Tissue Segmentation.

Brain tissue segmentation plays a critical role in the diagnosis, treatment, and study of brain diseases. Accurately identifying these boundaries is essential for improving segmentation accuracy. However, distinguishing boundaries between different brain tissues can be challenging, as they often overlap. Existing deep learning methods primarily calculate the overall segmentation results without adequately addressing local regions, leading to error propagation and mis-segmentation along boundaries. In this study, we propose a novel mis-segmentation-focused loss function based on a two-stage nnU-Net framework. Our approach aims to enhance the model's ability to handle ambiguous boundaries and overlapping anatomical structures, thereby achieving more accurate brain tissue segmentation results. Specifically, the first stage targets the identification of mis-segmentation regions using a global loss function, while the second stage involves defining a mis-segmentation loss function to adaptively adjust the model, thus improving its capability to handle ambiguous boundaries and overlapping anatomical structures. Experimental evaluations on two datasets demonstrate that our proposed method outperforms existing approaches both quantitatively and qualitatively.

Functional role of TrIL-1ß in Takifugu rubripes defense against Cryptocaryon irritans infection.

The Japanese puffer, Takifugu rubripes, is a commercially important fish species in China that is under serious threat from white spot disease (cyptocaryoniasis), which leads to heavy economic losses. We previously found that interleukin-1ß (IL-1ß), an important cytokine with a potential role in resistance against pathogens, was one of the most significantly differentially up-regulated proteins in the gills and spleen of T. rubripes infected by the protozoan parasite Cryptocaryon irritans. In this study, we assessed the potential function of T. rubripes IL-1ß (TrIL-1ß) in fish infected with C. irritans. Phylogenetic analysis indicated that the TrIL-1ß protein sequence was most closely related to that of Atlantic salmon (Salmo salar) (67.2 %). The incubation experiments revealed that TrIL-1ß may reduce trophont activity by destroying membranes. Immunofluorescence experiments showed that recombinant TrIL-1ß promoted the expression of endogenous IL-1ß, which penetrated and disrupted the cell membranes of trophonts. Transmission electron microscopy showed that the IL-1ß group had less tissue damage compared with control groups of fish. IL-1ß-small interfering RNA and IL-1ß overexpression experiments were performed in head kidney primary cells, and challenge experiments were performed in vitro. Quantitative RT-PCR results showed that TrIL-1ß regulated and activated MyD88/NF-κB and MyD88/MAPK/p38 signaling pathways during C. irritans infection. TrIL-1ß also promoted the differential expression of IgM, showing that it was involved in humoral immunity of T. rubripes. The cumulative mortality experiment show that TrIL-1ß could protect fish against C. irritans infection. These results enrich current knowledge about the molecular structure of TrIL-1ß. They also suggested that recombinant TrIL-1ß could be used as an adjuvant in a subunit vaccine against C. irritans infection, which is of profound importance for the prevention and control of parasitic diseases in T. rubripes.

Biochar reduces antibiotic transport by altering soil hydrology and enhancing antibiotic sorption.

The performance of biochar (BC) in reducing the transport of antibiotics under field conditions has not been sufficiently explored. In repacked sloping boxes of a calcareous soil, the effects of different BC treatments on the discharge of three relatively weakly sorbing antibiotics (sulfadiazine, sulfamethazine, and florfenicol) via runoff and drainage were monitored for three natural rain events. Surface application of 1 % BC (1 %BC-SA) led to the most effective reduction in runoff discharge of the two sulfonamide antibiotics, which can be partly ascribed to the enhanced water infiltration. The construction of 5 % BC amended permeable reactive wall (5 %BC-PRW) at the lower end of soil box was more effective than the 1 %BC-SA treatment in reducing the leaching of the most weakly sorbing antibiotic (florfenicol), which can be mainly ascribed to the much higher plant available and drainable water contents in the 5 %BC-PRW soil than in the unamended soil. The results of this study highlight the importance of BC's ability to regulate flow pattern by modifying soil hydraulic properties, which can make a significant contribution to the achieved reduction in the transport of antibiotics offsite or to groundwater.

Regulation of Charge Transfer Pathway in Ag-ZnIn2S4 Nanowires for Visible Photodynamic Therapy on Candida Albicans Infections.

Photodynamic therapy (PDT) is receiving extensive attention as an antimicrobial strategy that does not cause drug resistance by reactive oxygen species (ROS). Herein, hierarchical Ag-ZnIn2S4 (Ag-ZIS) core-shell nanowires are synthesized by in situ Metal-Organic Framework derived method for efficient PDT of Candida albicans (C. albicans). The core-shell structure enables spatial synergy strategy to regulate the charge transfer pathway under visible light excitation, in which the Ag nanowires are like the highway for the photogenerated electrons. The enhanced charge carrier separation efficiency greatly increased the chances for the generation of ROS. As expected, the optimized Ag-ZIS nanowires exhibit excellent performance for inactivation of C. albicans under visible light irradiation (λ ≥ 420 nm, 15 min), and the effective sterilization concentration is as high as 107CFU mL-1. Moreover, in vivo infection experiments suggested that the PDT effect of Ag-ZIS nanowires on the mouse wound healing is better than that of the clinical Ketoconazole drug. The PDT antifungal mechanism of Ag-ZIS nanowires is also investigated, and superoxide anion is found to be the predominant active species to causes C. albicans damage. This work provides a new perspective for designing novel interface structures to regulate charge transfer to achieve efficient PDT antifungal therapy.

Enhancing Efficiency and Stability in Carbon-Based Perovskite Solar Cells by Double Passivation with Ultralow-Cost Coal-Derived Graphene and Its Derivatives.

Coal-derived carbon nanomaterials possess numerous superior features compared to other classic carbon, such as readily accessible surfaces, tunable pore structure, and facile and precise surface functionalization. Therefore, the controllable preparation of coal-derived carbon nanomaterials is anticipated to be of great significance for the performance improvement and commercialization process of carbon-based perovskite solar cells (C-PSCs). In this study, we successfully synthesized highly stable and commercially valuable graphene oxide (GO) and reduced graphene oxide (rGO) utilizing coal. Compared to traditional methods and commercial graphene, the chemical oxidation and pyrolysis process used in this study is mild and simple, offering the advantages of controlled composition and the absence of other impurities. GO or rGO was incorporated into the top of the SnO2 electron transport layer (ETL) of C-PSCs. Under optimized conditions and ultraviolet-ozone (UVO) irradiation, the ultimate power conversion efficiency (PCE) increased from the unmodified 12.4 to 14.04% (based on rGO) and 15.18% (based on GO), representing improvements of 22 and 31%, respectively. The improved photovoltaic performance is mainly owing to enhanced charge transport capabilities, denser interfacial contacts, improved carrier separation properties, increased conductivity, and abundance of hydrophilic functional groups in GO, which can form more stable hydrogen bonds with SnO2. After being stored at room temperature and ambient humidity for 30 days, the modified, unpacked devices retained 87% of the highest power conversion efficiency (PCE). This study introduces a practical and manageable method to enhance the performance of C-PSCs by using functional carbon nanomaterials derived from coal.

The effect of serum triglyceride levels and different lipid-lowering methods on the prognosis of hypertriglyceridemic acute pancreatitis: a single-center 12-year retrospective study by propensity score matching.

AIM: To investigate the impact of triglyceride on hypertriglyceridemic acute pancreatitis (HTG-AP) and different lipid-lowering methods on triglyceride-lowering efficiency and HTG-AP. METHODS: The patients with HTG-AP from January 2012 to December 2023 in Civil Aviation General Hospital were analyzed, retrospectively. Patients were divided and compared according to whether their triglycerides were below 5.56 mmol/L at 48 and 72 h of admission. The patients were divided into control group, insulin group, and low molecular weight heparin (LMWH)+bezafibrate group based on the different methods of lipid-lowering. Propensity score matching (PSM) was employed to balance the baseline characteristics. RESULTS: There was no correlation between the severity of HTG-AP and the triglyceride at admission. The incidence of severity, local complications, and persistent organ failure (POF) were significantly decreased in patients with 48-h and 72-h triglyceride attainment. Following PSM, the incidence of infectious pancreatic necrosis (IPN) (3.3% vs. 13.3%) was significantly reduced in insulin group compared with control group (p < .05). Compared with control group, LMWH + bezafibrate group had higher lipid reduction efficiency, and the incidence of IPN (0.9% vs. 10.1%) and POF (8.3% vs. 19.3%) was significantly decreased (p < .05). There was no significant difference in the efficiency of lipid-lowering, complications, and POF between LMWH + bezafibrate group and insulin group (p > .05). CONCLUSION: The severity of HTG-AP is not associated with the triglyceride levels at admission. However, rapid reduction of triglyceride levels can lower the incidence of local complications and respiratory failure. Compared with conservative treatment, insulin and LMWH + bezafibrate can both reduce the incidence of IPN in patients with HTG-AP.

Spatial and temporal pattern of structure-function coupling of human brain connectome with development.

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7 to 21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC-FC coupling. Our findings revealed that SC-FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC-FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC-FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC-FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC-FC coupling in typical development.

Modifying temperature-related cardiovascular mortality through green-blue space exposure.

Green-blue spaces (GBS) are pivotal in mitigating thermal discomfort. However, their management lacks guidelines rooted in epidemiological evidence for specific planning and design. Here we show how various GBS types modify the link between non-optimal temperatures and cardiovascular mortality across different thermal extremes. We merged fine-scale population density and GBS data to create novel GBS exposure index. A case time series approach was employed to analyse temperature-cardiovascular mortality association and the effect modifications of type-specific GBSs across 1085 subdistricts in south-eastern China. Our findings indicate that both green and blue spaces may significantly reduce high-temperature-related cardiovascular mortality risks (e.g., for low (5%) vs. high (95%) level of overall green spaces at 99th vs. minimum mortality temperature (MMT), Ratio of relative risk (RRR) = 1.14 (95% CI: 1.07, 1.21); for overall blue spaces, RRR = 1.20 (95% CI: 1.12, 1.29)), while specific blue space types offer protection against cold temperatures (e.g., for the rivers at 1st vs MMT, RRR = 1.17 (95% CI: 1.07, 1.28)). Notably, forests, parks, nature reserves, street greenery, and lakes are linked with lower heat-related cardiovascular mortality, whereas rivers and coasts mitigate cold-related cardiovascular mortality. Blue spaces provide greater benefits than green spaces. The severity of temperature extremes further amplifies GBS's protective effects. This study enhances our understanding of how type-specific GBS influences health risks associated with non-optimal temperatures, offering valuable insights for integrating GBS into climate adaptation strategies for maximal health benefits.

Rising trend and regional disparities of the global burden of disease attributable to ambient low temperature, 1990-2019: An analysis of data from the Global Burden of Disease 2019 study.

Background: Previous studies on the effect of global warming on the global burden of disease have mainly focussed on the impact of high temperatures, thereby providing limited evidence of the effect of lower temperatures. Methods: We adopted a three-stage analysis approach using data from the Global Burden of Disease 2019 study. First, we explored the global burden of disease attributable to low temperatures, examining variations by gender, age, cause, region, and country. Second, we analysed temporal trends in low-temperature-related disease burdens from 1990 to 2019 by meta-regression. Finally, we fitted a mixed-effects meta-regression model to explore the effect modification of country-level characteristics. Results: In 2019, low temperatures were responsible for 2.92% of global deaths and 1.03% of disability-adjusted life years (DALYs), corresponding to a death rate of 21.36 (95% uncertainty interval (UI) = 18.26, 24.73) and a DALY rate of 335 (95% UI = 280, 399) per 100 000 population. Most of the deaths (85.12%) and DALYs (94.38%) attributable to low temperatures were associated with ischaemic heart disease, stroke, and chronic obstructive pulmonary disease. In the last three decades, we observed an upward trend for the annual number of attributable deaths (P < 0.001) and a downward trend for the rates of death (P < 0.001) and DALYs (P < 0.001). The disease burden associated with low temperatures varied considerably among regions and countries, with higher burdens observed in regions with middle or high-middle socio-demographic indices, as well as countries with higher gross domestic product per capita and a larger proportion of ageing population. Conclusions: Our findings emphasise the significance of raising public awareness and prioritising policies to protect global population health from the adverse effects of low temperatures, even in the face of global warming. Particular efforts should be targeted towards individuals with underlying diseases (e.g. cardiovascular diseases) and vulnerable countries or regions (e.g. Central Asia and central Europe).

High-Temperature Tolerance Protein Engineering through Deep Evolution.

Protein engineering aimed at increasing temperature tolerance through iterative mutagenesis and high-throughput screening is often labor-intensive. Here, we developed a deep evolution (DeepEvo) strategy to engineer protein high-temperature tolerance by generating and selecting functional sequences using deep learning models. Drawing inspiration from the concept of evolution, we constructed a high-temperature tolerance selector based on a protein language model, acting as selective pressure in the high-dimensional latent spaces of protein sequences to enrich those with high-temperature tolerance. Simultaneously, we developed a variant generator using a generative adversarial network to produce protein sequence variants containing the desired function. Afterward, the iterative process involving the generator and selector was executed to accumulate high-temperature tolerance traits. We experimentally tested this approach on the model protein glyceraldehyde 3-phosphate dehydrogenase, obtaining 8 variants with high-temperature tolerance from just 30 generated sequences, achieving a success rate of over 26%, demonstrating the high efficiency of DeepEvo in engineering protein high-temperature tolerance.

Efficient second harmonic generation in a high-Q Fabry-Perot microresonator on x-cut thin film lithium niobate.

Microresonators facilitate enhanced light-matter interactions within a limited space, showing great promise for nonlinear optics. Here, we demonstrate a high-quality (Q) factor Fabry-Perot microresonator (FPR) for second harmonic generation (SHG) on an x-cut thin film lithium niobate (TFLN) platform. The FPR exhibits Q factors of Qpump = 1.09 × 105 and QSH = 1.15 × 104 at the 1560 nm pump wavelength and 780 nm second harmonic wavelength, respectively. Under low pump power, a normalized SHG efficiency of 158.5 ± 18.5%/W is attained. We experimentally verify that increased temperatures mitigate photorefractive effects that degrade SHG performance. This work highlights the immense capabilities of one-dimensional planar optical waveguide resonators for efficient on-chip nonlinear wavelength conversion.

Robust and high-efficiency dynamical method of enantio-specific state transfer.

We propose a simple dynamical method to realize fast enantio-specific state transfer (ESST) of chiral molecules. Driven by three external electromagenetic fields, the chiral molecules are modeled as cyclic three-level systems, where the overall phase differs by π for the left- and right-handed chiral molecules. We unveil that the ESST is allowed when the amplitudes of three Rabi frequencies in the cyclic three-level systems are equal. Our method is robust and highly efficient in the sense that the external fields can have arbitrary waveforms. This thus provides the opportunity of simplifying the experimental implementations of ESST through pulse design.

[Spatial-temporal Variation in Water Quality of Rain-source Rivers in Shenzhen from 2015 to 2021 and Its Response to Rainfall].

Rain-source urban rivers have the characteristics of small water capacity， lack of dynamic water supply， and being easily polluted. This study analyzed the spatial and temporal distribution characteristics of river water quality and the response of characteristic pollutants to rainfall based on daily rainfall data and 21 water quality indicators of nine major river basins in Shenzhen （excluding Shenzhen-Shantou） from 2015 to 2021 by using the single-factor assessment method， comprehensive pollution index method， hierarchical cluster analysis， and Pearson correlation. The results showed that： ① in 2015， the water quality of most sections in the whole region was inferior Class V water. After October 2018， the overall water quality of rivers was greatly improved， which was consistent with the background of Shenzhen's special water control activities in 2018. By 2021， the water quality of approximately 62% of sections reached Class Ⅰ-Ⅲ water standards. ② The water pollution in the densely populated western part of Shenzhen was more serious than that in the eastern part， and the water pollution in the lower reaches of the estuaries and tributaries was more serious than that in the upper reaches. ③ The water quality of the Pingshan River， Guanlan River， Longgang River， and Maozhou River was significantly affected by rainfall. ④ The main characteristic pollution indexes of the Shenzhen River were DO， permanganate index， COD， BOD5， NH4+-N， TP， petroleum， and anionic surfactant. For the Pingshan River and Longgang River， rainfall increased the concentrations of TP and NH4+-N. For the Maozhou River， rainfall increased the concentrations of TP and COD. For the Shenzhen River， rainfall increased the concentrations of COD， TP， and NH4+-N. The above results reveal the spatio-temporal variation in rain-source river water quality in Shenzhen and its response to non-point source pollution caused by rainfall events and provide a scientific reference for building a higher quality water environment in Shenzhen.

[Adsorption Properties of Magnetic Phosphorous Camellia Oleifera Shells Biochar to Sulfamethoxazole in Water].

Magnetic phosphorous biochar （MPBC） was prepared from Camellia oleifera shells using phosphoric acid activation and iron co-deposition. The materials were characterized and analyzed through scanning electron microscopy （SEM）， X-ray diffractometry （XRD）， specific surface area and pore size analysis （BET）， Fourier infrared spectroscopy （FT-IR）， and X-ray photoelectron spectroscopy （XPS）. MPBC had a high surface area （1 139.28 m2·g-1） and abundant surface functional groups， and it could achieve fast solid-liquid separation under the action of an external magnetic field. The adsorption behavior and influencing factors of sulfamethoxazole （SMX） in water were investigated. The adsorbent showed excellent adsorption properties for SMX under acidic and neutral conditions， and alkaline conditions and the presence of CO32- had obvious inhibition on adsorption. The adsorption process conformed to the quasi-second-order kinetics and Langmuir model. The adsorption rate was fast， and the maximum adsorption capacity reached 356.49 mg·g-1. The adsorption process was a spontaneous exothermic reaction， and low temperature was beneficial to the adsorption. The adsorption mechanism was mainly the chemisorption of pyrophosphate surface functional groups （C-O-P bond） between the SMX molecule and MPBC and also included hydrogen bonding， π-π electron donor-acceptor （π-πEDA） interaction， and a pore filling effect. The development of MPBC adsorbent provides an effective way for resource utilization of waste Camellia oleifera shells and treatment of sulfamethoxazole wastewater.

Machine-learning Algorithm-based Risk Prediction and Screening-detected Prostate Cancer in A Benign Prostate Hyperplasia Cohort.

BACKGROUND/AIM: Prostate cancer (PCa) is lethal. Our aim in this retrospective cohort study was to use machine learning-based methodology to predict PCa risk in patients with benign prostate hyperplasia (BPH), identify potential risk factors, and optimize predictive performance. PATIENTS AND METHODS: The dataset was extracted from a clinical information database of patients at a single institute from January 2000 to December 2020. Patients newly diagnosed with BPH and prescribed alpha blockers/5-alpha-reductase inhibitors were enrolled. Patients were excluded if they had a previous diagnosis of any cancer or were diagnosed with PCa within 1 month of enrolment. The study endpoint was PCa diagnosis. The study utilized the extreme gradient boosting (XGB), support vector machine (SVM) and K-nearest neighbors (KNN) machine-learning algorithms for analysis. RESULTS: The dataset used in this study included 5,122 medical records of patients with and without PCa, with 19 patient characteristics. The SVM and XGB models performed better than the KNN model in terms of accuracy and area under curve. Local interpretable model-agnostic explanation and Shapley additive explanations analysis showed that body mass index (BMI) and late prostate-specific antigen (PSA) were important features for the SVM model, while PSA velocity, late PSA, and BMI were important features for the XGB model. Use of 5-alpha-reductase inhibitor was associated with a higher incidence of PCa, with similar survival outcomes compared to non-users. CONCLUSION: Machine learning can enhance personalized PCa risk assessments for patients with BPH but more research is necessary to refine these models and address data biases. Clinicians should use them as supplementary tools alongside traditional screening methods.