Recent advances in photothermal nanomaterials-mediated detection of circulating tumor cells.

Photothermal materials have shown great potential for cancer detection and treatment due to their excellent photothermal effects. Circulating tumor cells (CTCs) are tumor cells that are shed from the primary tumor into the blood and metastasize. In contrast to other tumor markers that are free in the blood, CTCs are a collective term for all types of tumor cells present in the peripheral blood, a source of tumor metastasis, and clear evidence of tumor presence. CTCs detection enables early detection, diagnosis and treatment of tumors, and plays an important role in cancer prevention and treatment. This review summarizes the application of various photothermal materials in CTC detection, including gold, carbon, molybdenum, phosphorus, etc. and describes the significance of CTC detection for early tumor diagnosis and tumor prognosis. Focus is also put on how various photothermal materials play their roles in CTCs detection, including CT, imaging and photoacoustic and therapeutic roles. The physicochemical properties, shapes, and photothermal properties of various photothermal materials are discussed to improve the detection sensitivity and efficiency and to reduce the damage to normal cells. These photothermal materials are capable of converting radiant light energy into thermal energy for highly-sensitive CTCs detection and improving their photothermal properties by various methods, and have achieved good results in various experiments. The use of photothermal materials for CTCs detection is becoming more and more widespread and can be of significant help in early cancer screening and later treatment.

Leachate derived humic-like substances drive the variation in microbial communities in landfill-affected groundwater.

Landfills are commonly used for waste disposal in many countries, and pose a significant threat of groundwater contamination. Dissolved organic matter (DOM) plays a crucial role as a carbon and energy source, supporting the growth and activity of microorganisms. However, the changes in the DOM signature and microbial community composition in landfill-affected groundwater and their bidirectional relationships remain inadequately explored. Herein, we showed that DOM originating from more recent landfills mainly comprises microbially produced substances resembling tryptophan and tyrosine. Conversely, DOM originating from older landfills predominantly comprises fulvic-like and humic-like compounds. Leachate leakage increases microbial diversity and richness and facilitates the transfer of foreign bacteria from landfills to groundwater, thereby increasing the vulnerability of the microbial ecosystem in groundwater. Deterministic processes dominated the assembly of the groundwater microbial community, while stochastic processes accounted for an increased proportion of the microbial community in the old landfills. The dominant phyla observed in groundwater were Proteobacteria, Bacteroidota, and Actinobacteriota, and humic-like substances play a crucial role in driving the variation in microbial communities in landfill-affected groundwater. Predictions using PICRUSt2 suggested significant associations between various metabolic pathways and microbial communities, with the Kyoto Encyclopedia of Genes and Genomes pathway "Metabolism" being the most predominant. The findings contribute to advancing our understanding of the transformation of DOM and its interplay with microbial communities and can serve as a scientific reference for decision-making regarding groundwater pollution monitoring and remediation.

MSMTSeg: Multi-Stained Multi-Tissue Segmentation of Kidney Histology Images via Generative Self-Supervised Meta-Learning Framework.

Accurately diagnosing chronic kidney disease requires pathologists to assess the structure of multiple tissues under different stains, a process that is timeconsuming and labor-intensive. Current AI-based methods for automatic structure assessment, like segmentation, often demand extensive manual annotation and focus on single stain domain. To address these challenges, we introduce MSMTSeg, a generative self-supervised meta-learning framework for multi-stained multi-tissue segmentation in renal biopsy whole slide images (WSIs). MSMTSeg incorporates multiple stain transform models for style translation of inter-stain domains, a self-supervision module for obtaining pre-trained models with the domain-specific feature representation, and a meta-learning strategy that leverages generated virtual data and pre-trained models to learn the domain-invariant feature representation across multiple stains, thereby enhancing segmentation performance. Experimental results demonstrate that MSMTSeg achieves superior and robust performance, with mDSC of 0.836 and mIoU of 0.718 for multiple tissues under different stains, using only one annotated training sample for each stain. Our ablation study confirms the effectiveness of each component, positioning MSMTSeg ahead of classic advanced segmentation networks, recent few-shot segmentation methods, and unsupervised domain adaptation methods. In conclusion, our proposed few-shot cross-domain technology offers a feasible and cost-effective solution for multi-stained renal histology segmentation, providing convenient assistance to pathologists in clinical practice. The source code and conditionally accessible data are available at https://github.com/SnowRain510/MSMTSeg.

Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images.

BACKGROUND: Interstitial lung disease (ILD) represents a group of chronic heterogeneous diseases, and current clinical practice in assessment of ILD severity and progression mainly rely on the radiologist-based visual screening, which greatly restricts the accuracy of disease assessment due to the high inter- and intra-subjective observer variability. OBJECTIVE: To solve these problems, in this work, we propose a deep learning driven framework that can assess and quantify lesion indicators and outcome the prediction of severity of ILD. METHODS: In detail, we first present a convolutional neural network that can segment and quantify five types of lesions including HC, RO, GGO, CONS, and EMPH from HRCT of ILD patients, and then we conduct quantitative analysis to select the features related to ILD based on the segmented lesions and clinical data. Finally, a multivariate prediction model based on nomogram to predict the severity of ILD is established by combining multiple typical lesions. RESULTS: Experimental results showed that three lesions of HC, RO, and GGO could accurately predict ILD staging independently or combined with other HRCT features. Based on the HRCT, the used multivariate model can achieve the highest AUC value of 0.755 for HC, and the lowest AUC value of 0.701 for RO in stage I, and obtain the highest AUC value of 0.803 for HC, and the lowest AUC value of 0.733 for RO in stage II. Additionally, our ILD scoring model could achieve an average accuracy of 0.812 (0.736 - 0.888) in predicting the severity of ILD via cross-validation. CONCLUSIONS: In summary, our proposed method provides effective segmentation of ILD lesions by a comprehensive deep-learning approach and confirms its potential effectiveness in improving diagnostic accuracy for clinicians.

Enhancing the Catalytic Efficiency of D-lactonohydrolase through the Synergy of Tunnel Engineering, Evolutionary Analysis, and Force-Field Calculations.

Computational design advances enzyme evolution and their use in biocatalysis in a faster and more efficient manner. In this study, a synergistic approach integrating tunnel engineering, evolutionary analysis, and force-field calculations has been employed to enhance the catalytic activity of D-lactonohydrolase (D-Lac), which is a pivotal enzyme involved in the resolution of racemic pantolactone during the production of vitamin B5. The best mutant, N96S/A271E/F274Y/F308G (M3), was obtained and its catalytic efficiency (kcat/KM) was nearly 23-fold higher than that of the wild-type. The M3 whole-cell converted 20 % of DL-pantolactone into D-pantoic acid (D-PA, >99 % e.e.) with a conversion rate of 47 % and space-time yield of 107.1 g L-1 h-1, demonstrating its great potential for industrial-scale D-pantothenic acid production. Molecular dynamics (MD) simulations revealed that the reduction in the steric hindrance within the substrate tunnel and conformational reconstruction of the distal loop resulted in a more favourable"catalytic" conformation, making it easier for the substrate and enzyme to enter their pre-reaction state. This study illustrates the potential of the distal residue on the pivotal loop at the entrance of the D-Lac substrate tunnel as a novel modification hotspot capable of reshaping energy patterns and consequently influencing the enzymatic activity.

Exploring organic matter conversion pathway and its effect on nitrogen removal in tidal flow constructed wetlands.

Achieving effective nitrogen removal remains a significant challenge faced by constructed wetlands. Although organic matter is a crucial factor influencing nitrogen removal, little attention has been paid to the impact of organic matter conversion pathways on nitrogen removal in constructed wetlands. Here, we showed that endogenous microorganisms performing carbon internalization could be easily enriched in tidal flow constructed wetlands (TFCWs) under its special rhythmic cycle of anaerobic/aerobic operational mode. Endogenous microorganisms could translate influent carbon sources into intracellular carbons during the anaerobic stage and supply the carbon source for endogenous denitrification after the aerobic stage (rest period). Based on these findings, an innovative combined TFCW and Nitrifying-CW system was developed, and robust total nitrogen (TN) removal (82% on average) was achieved even under carbon source limiting conditions. This performance was a substantial improvement compared to the conventional single bed TFCW with multiple "tides" (corresponding to the multiple contact/rest periods) with TN removal of only 54% on average. Simultaneous nitrification-endogenous denitrification (SNED) was found to be the major nitrogen removal pathway in the proposed system. Compared with classical nitrification-denitrification, simultaneous nitrification-endogenous denitrification brings high nitrogen conversion rates and significantly reduces the demand for oxygen and organic carbon. Furthermore, microbial community analysis indicated that endogenous microorganisms such as Candidatus_Competibacter and Defluviicoccus were successfully enriched, accounting for 50.73% and 3.46% in CW1, and 25.25% and 1.76% in CW2, respectively. Together, these mechanisms allow the proposed system to achieve efficient TN removal.

Diagnosis of diabetic kidney disease in whole slide images via AI-driven quantification of pathological indicators.

Diagnosis of diabetic kidney disease (DKD) mainly relies on screening the morphological variations and internal lesions of glomeruli from pathological kidney biopsy. The prominent pathological alterations of glomeruli for DKD include glomerular hypertrophy and nodular mesangial sclerosis. However, the qualitative judgment of these alterations is inaccurate and inconstant due to the intra- and inter-subject variability of pathologists. It is necessary to design artificial intelligence (AI) methods for accurate quantification of these pathological alterations and outcome prediction of DKD. In this work, we present an AI-driven framework to quantify the volume of glomeruli and degree of nodular mesangial sclerosis, respectively, based on an instance segmentation module and a novel weakly supervised Macro-Micro Aggregation (MMA) module. Subsequently, we construct classic machine learning models to predict the degree of DKD based on three selected pathological indicators via factor analysis. These corresponding modules are trained and tested on a total of 281 whole slide images (WSIs) digitized from two hospitals with different scanners. Our designed AI framework achieved inspiring results with 0.926 mIoU for glomerulus segmentation, and 0.899 F1 score for glomerulus classification in the external testing dataset. Meantime, the visualized results of the MMA module could reflect the location of the lesions. The performance of predicting disease achieved the F1 score of 0.917, which further proved the effectiveness of our AI-driven quantification of pathological indicators. Additionally, the interpretation of the machine learning model with the SHAP method showed similar accordance with the development of DKD in pathology. In conclusion, the proposed auxiliary diagnostic technologies have the feasibility for quantitative analysis of glomerular pathological tissues and alterations in DKD. Pathological quantitative indicators will also make it more convenient to provide doctors with assistance in clinical practice.

Minocycline induces tolerance to dendritic cell production probably by targeting the SOCS1/ TLR4/NF-κB signaling pathway.

OBJECTIVE: Dendritic cells (DCs) are professional antigen-presenting cells that play a key role in maintaining peripheral immune tolerance. The use of tolerogenic DCs (tolDCs), i.e., semi-mature DCs that express co-stimulatory molecules but not pro-inflammatory cytokines, has been proposed. However, the mechanism of tolDCs induced by minocycline is still unclear. Our previous bioinformatics analyses based on multiple databases suggested that the suppressor of cytokine signaling 1/Toll-like receptor 4/NF-κB (SOCS1/TLR4/NF-κB) signal pathway was associated with DCs maturation. Thus, we studied whether minocycline could induce DC tolerance through this pathway. METHODS: A search for potential targets was carried out through public databases, and pathway analysis was performed on these potential targets to obtain pathways relevant to the experiment. Flow cytometry was used to detect the expression of DC surface markers CD11c, CD86, and CD80, and major histocompatibility complex II. The secretion of interleukin (IL)-12p70, tumor necrosis factor alpha (TNF- α), and IL-10 in the DC supernatant was detected by enzyme-linked immunoassay. The ability of three groups (Ctrl-DCs, Mino-DCs, and LPS-DCs) of DCs to stimulate allogeneic CD4+ T cells was analyzed using a mixed lymphocyte reaction assay. Western blotting was used to detect the expression of TLR4, NF-κB-p65, NF-κB-p-p65, IκB-α, and SOCS1 proteins. RESULTS: The hub gene plays a vital role in biological processes; in related pathways, the regulation of other genes is often affected by it. The SOCS1/TLR4/NF-κB signaling pathway was further validated by searching for potential targets through public databases to obtain relevant pathways. The minocycline-induced tolDCs showed characteristics of semi-mature DCs. Moreover, the IL-12p70 and TNF-α levels in the minocycline-stimulated DC group (Mino-DC group) were lower than those in the lipopolysaccharide (LPS)-DC group, and the IL-10 levels were higher in the Mino-DC group than in the LPS-DC and control DC groups. In addition, the Mino-DC group had decreased protein expression levels of TLR4 and NF-κB-p65 and upregulated protein levels of NF-κB-p-p65, IκB-α, and SOCS1 compared with the other groups. CONCLUSION: The results of this study indicate that minocycline could improve the tolerance of DCs probably by blocking the SOCS1/TLR4/NF-κB signaling pathway.

Anti-High-Power Microwave RFID Tag Based on Highly Thermal Conductive Graphene Films.

In this paper, a radio frequency identification (RFID) tag is designed and fabricated based on highly electrical and thermal conductive graphene films. The tag operates in the ultrahigh-frequency (UHF) band, which is suitable for high-power microwave environments of at least 800 W. We designed the protection structure to avoid charge accumulation at the antenna's critical positions. In the initial state, the read range of the anti-high-power microwave graphene film tag (AMGFT) is 10.43 m at 915 MHz. During the microwave heating experiment, the aluminum tag causes a visible electric spark phenomenon, which ablates the aluminum tag and its attachment, resulting in tag failure and serious safety issues. In contrast, the AMGFT is intact, with its entire read range curve growing and returning to its initial position as its temperature steadily decreases back to room temperature. In addition, the proposed dual-frequency tag further confirms the anti-high-power microwave performance of graphene film tags and provides a multi-scenario interactive application.

Accurate Kidney Pathological Image Classification Method Based on Deep Learning and Multi-Modal Fusion Method with Application to Membranous Nephropathy.

Membranous nephropathy is one of the most prevalent conditions responsible for nephrotic syndrome in adults. It is clinically nonspecific and mainly diagnosed by kidney biopsy pathology, with three prevalent techniques: light microscopy, electron microscopy, and immunofluorescence microscopy. Manual observation of glomeruli one by one under the microscope is very time-consuming, and there are certain differences in the observation results between physicians. This study makes use of whole-slide images scanned by a light microscope as well as immunofluorescence images to classify patients with membranous nephropathy. The framework mainly includes a glomerular segmentation module, a confidence coefficient extraction module, and a multi-modal fusion module. This framework first identifies and segments the glomerulus from whole-slide images and immunofluorescence images, and then a glomerular classifier is trained to extract the features of each glomerulus. The results are then combined to produce the final diagnosis. The results of the experiments show that the F1-score of image classification results obtained by combining two kinds of features, which can reach 97.32%, is higher than those obtained by using only light-microscopy-observed images or immunofluorescent images, which reach 92.76% and 93.20%, respectively. Experiments demonstrate that considering both WSIs and immunofluorescence images is effective in improving the diagnosis of membranous nephropathy.

Two Hawks with One Arrow: A Review on Bifunctional Scaffolds for Photothermal Therapy and Bone Regeneration.

Despite the significant improvement in the survival rate of cancer patients, the total cure of bone cancer is still a knotty clinical challenge. Traditional surgical resectionof bone tumors is less than satisfactory, which inevitably results in bone defects and the inevitable residual tumor cells. For the purpose of realizing minimal invasiveness and local curative effects, photothermal therapy (PTT) under the irradiation of near-infrared light has made extensive progress in ablating tumors, and various photothermal therapeutic agents (PTAs) for the treatment of bone tumors have thus been reported in the past few years, has and have tended to focus on osteogenic bio-scaffolds modified with PTAs in order to break through the limitation that PTT lacks, osteogenic capacity. These so-called bifunctional scaffolds simultaneously ablate bone tumors and generate new tissues at the bone defects. This review summarizes the recent application progress of various bifunctional scaffolds and puts forward some practical constraints and future perspectives on bifunctional scaffolds for tumor therapy and bone regeneration: two hawks with one arrow.

Quasi-2D Mn3Si2Te6 Nanosheet for Ultrafast Photonics.

The magnetic nanomaterial Mn3Si2Te6 is a promising option for spin-dependent electronic and magneto-optoelectronic devices. However, its application in nonlinear optics remains fanciful. Here, we demonstrate a pulsed Er-doped fiber laser (EDFL) based on a novel quasi-2D Mn3Si2Te6 saturable absorber (SA) with low pump power at 1.5 µm. The high-quality Mn3Si2Te6 crystals were synthesized by the self-flux method, and the ultrathin Mn3Si2Te6 nanoflakes were prepared by a simple mechanical exfoliation procedure. To the best of our knowledge, this is the first time laser pulses have been generated using quasi-2D Mn3Si2Te6. A stable pulsed laser at 1562 nm with a low threshold pump power of 60 mW was produced by integrating the Mn3Si2Te6 SA into an EDFL cavity. The maximum power of the output pulse is 783 µW. The repetition rate can vary from 24.16 to 44.44 kHz, with corresponding pulse durations of 5.64 to 3.41 µs. Our results indicate that the quasi-2D Mn3Si2Te6 is a promising material for application in ultrafast photonics.

Secreted aminoacyl-tRNA synthetase-interacting multifunctional protein-1 (AIMP1) is a promising predictor for the severity of acute AQP4-IgG positive neuromyelitis optica spectrum disorder.

BACKGROUND AND OBJECTIVES: Aminoacyl-tRNA synthetase complex interacting with multifunctional protein-1 (AIMP1) has been reported to carry pro-inflammatory properties and anti-angiogenesis effects. However, the exact role of AIMP1 in patients with NMOSD is not yet clear. Our objective was to investigate the relationship between plasma AIMP1 levels and disease severity in patients with AQP4-IgG+ NMOSD from North China based on the Expanded Disability Status Scale (EDSS) score. METHODS: Plasma AIMP1 levels were measured using ELISA kits in 94 patients with AQP4-IgG+NMOSD (48 in the acute phase before high-dose intravenous methylprednisolone (IVMP) therapy, 21 in the acute phase after IVMP therapy, 25 in the clinical remission-phase)as well as 33 healthy controls (HCs). The disability function of NMOSD patients was evaluated using the EDSS score. Furthermore, the clinical characteristics of the patients were also evaluated, and laboratory tests were performed on blood samples. RESULTS: The plasma AIMP1 levels in AQP4-IgG+NMOSD patients with acute phase before IVMP therapy were significantly higher as compared to those in patients after the IVMP therapy (p < 0.001) as well as those in the clinical remission phase (p = 0.021) or HCs (p < 0.001). Plasma AIMP1 levels were positively correlated with EDSS scores (r = 0.485, p < 0.001) and negatively correlated with serum complement 3 concentrations (r =-0.452, p = 0.001). AIMP1 exhibited the potential to distinguish NMOSD from HCs (AUROC 0.820, p < 0.0001) and could differentiate mild and moderate-severe NMOSD (AUROC 0.790, p = 0.0006). Furthermore, plasma AIMP1 levels of ≥49.55pg/mL were found to be an independent predictor of the risk for moderate-severe NMOSD (with OR 0.03, 95%CI 0.001-0.654, p = 0.026). CONCLUSION: AIMP1 may be involved in the pathogenesis of AQP4-IgG+NMOSD disease and predict the disease activity, severity, or effect of treatment in patients with NMOSD. Further studies should be performed to reveal the precise mechanisms of AQP4-IgG+NMOSD.

Insights into organic matter evolution during food waste stabilization induced by 14-hour high-temperature fermentation.

Large amounts of secondary pollutants are released during traditional composting, and rapid fermentation is desirable for the stabilization of food wastes. Food wastes were mixed with rice husk, placed in a bioreactor, and stirred continuously to achieve high-temperature fermentation for 14 h. The transformations of the mixtures were analyzed using elemental and spectral analysis combined with kinetic equations and two-dimensional correlation spectroscopy. The carbohydrates, proteins, and aliphatic compounds of food waste were degraded after 4 h of fermentation. Transformations of dissolved organic and sulfur- and nitrogen-containing substances followed first-order kinetic equations with reaction rate constants of 0.142 h-1, 0.098 h-1, and 0.016 h-1, respectively. Organic matter conversion was in the following order: aliphatic → protein → carbohydrate and followed the order, acrylamide C → O-alkyl C → anomeric C at the molecular level. The fermentation process was characterized by the increase in protein- and fulvic-like compounds. Fulvic acid substances gradually accumulated during the late fermentation period. Thus, dissolved organic matter components were gradually transformed into humic substances with increasing fermentation time. The sequence of transformation during the fermentation process was, tyrosine-like → tryptophan-like → fulvic-like substances. Humification mainly occurred in the mature stage of composting; therefore, it was verified that the food waste was stabilized by a 14-h fermentation.

Sediment diffusion is feasible to simultaneously reduce nitrate discharge from recirculating aquaculture system and ammonium release from sediments in receiving intensive aquaculture pond.

Nitrogen accumulation has become one of the greatest unresolved challenges restricting the development of aquaculture worldwide. In recirculating aquaculture system (RAS), lack of organic matter (OM) and sensitive organisms makes it difficult to apply efficient denitrifying technology, thus leading to a high nitrate­nitrogen (NO3--N) accumulation. In contrast, excess OM accumulation in intensive aquaculture pond sediments is associated with dissolved oxygen depletion and ammonium­nitrogen (NH4+-N) accumulation in the sediments. Based on the opposing effects of OM on the nitrogen accumulation in RAS and intensive aquaculture ponds, this study assessed the feasibility of simultaneously reducing NO3--N discharge from RAS and controlling NH4+-N accumulation in intensive aquaculture ponds by in situ diffusing RAS tailwater containing NO3--N into intensive aquaculture pond sediments. The results showed that NO3--N diffusion strategy improved the native sediment denitrification capacity, thus increasing NO3--N removal efficiency from RAS tailwater and significantly decreasing the NH4+-N concentration in interstitial water and the total organic carbon content in intensive aquaculture pond sediments. High-throughput sequencing and quantitative real-time polymerase chain reaction (qPCR) results revealed that NO3--N addition significantly increased both nitrifying bacteria and denitrifying bacteria abundance. These results implied that NO3--N diffusion strategy could effectively stimulate microbial decomposition of OM, thus relieving the hypoxia limitation of sediment nitrification. Overall, this study offers a feasible method for simultaneous reduction of NO3--N from RAS tailwater and NH4+-N in intensive aquaculture ponds with low cost and high efficiency.

IDA-MIL: Classification of Glomerular with Spike-like Projections via Multiple Instance Learning with Instance-level Data Augmentation.

BACKGROUND AND OBJECTIVE: Tiny spike-like projections on the basement membrane of glomeruli are the main pathological feature of membranous nephropathy at stage II (MN II), which is the most significant stage for the diagnosis and treatment of renal disease. Pathological technology is the gold standard in the diagnosis of spike-like and other MNs, and automatic classification of spike-like projection is a crucial step in assisting pathologists in their diagnosis. However, owing to hard-to-label spile-like projections and the scarcity of patient data, classification of glomeruli with spike-like projections based on supervised learning methods is a challenging task. METHOD: To overcome the aforementioned problems, the idea of integrating weakly-supervised learning and data augmentation methods is utilized in designing the classification framework. Specifically, a multiple instance learning with instance-level data augmentation (IDA-MIL) method for the classification of glomeruli with spike-like projections is established in this paper. The proposed classification framework first trains the MIL model on a dataset with image-level labels, and the well-trained MIL model is used to extract instances that include spike-like projections in the whole glomerular image. Then, rather than using an image-level generative adversarial network (ImgGAN), an instance-level generative adversarial network (InsGAN) based on the StyleGAN2-ADA model is trained on the spike-like instances obtained by the MIL model and synthesizes new spike-like projection instances. Finally, the synthesized spike-like instances are extended to the training dataset to further improve the classification performance of MIL. RESULTS: The designed IDA-MIL model is verified and evaluated from two aspects based on the in-house dataset. On the one hand, the performance comparisons between InsGAN and ImgGAN on five metrics, which involve FID, KID, Precision, Recall and IS, show that InsGAN obtains a better score and can synthesize effective spike-like projections. However, the proposed IDA-MIL model achieves the best classification performance with an accuracy of 0.9405. Then, to make nephrologists believe the inference result of the proposed model, we use heatmap technology to visualize the basis of the model inferences and show the top 4 probability spike-like instances per glomerulus. Furthermore, we analyze the correlation between the disease and the proportion of spike-like instances in bags from historical cases. CONCLUSION: Compared with the ImgGAN, the InsGAN can synthesize natural and varied spike-like projections, which results in the classification performance of the MIL model achieving great improvement by adding synthesized instance samples into the training dataset. The heatmap of spike-like inferences and the proportion of spike-like instances can help nephrologists to make a preliminary reliable diagnosis in clinical practice. This work provides a valuable reference for medical image classification with limited data and small-scale lesions based on deep learning.

Microfluidic-based exosome isolation and highly sensitive aptamer exosome membrane protein detection for lung cancer diagnosis.

Non-invasive methods of detecting cancer by circulating exosomes are challenged by inefficient purification and identification. This study hereby proposed an automated centrifugal microfluidic disc system combined with functionalized membranes (Exo-CMDS) to isolate and enrich exosomes, which will then be processed by a novel aptamer fluorescence system (Exo-AFS) in order to detect the exosome surface proteins in an effective manner. Exo-CMDS features in highly qualified yields with optimal exosomal concentration of 5.1 × 109 particles/mL from trace amount of blood samples (<300 µL) in only 8 min, which truly accomplishes the exosome isolation and purification in one-step methods. Meanwhile, the limit of detection (LOD) of PD-L1 in Exo-AFS reaches as low as 1.58 × 105 particles/mL. In the trial of clinical samples, the diagnostic accuracy of lung cancer achieves 91% (95% CI: 79%-96%) in contrast to the exosome ELISA (area under the curve: 0.9378 versus 0.8733; 30 patients). Exo-CMDS and Exo-AFS display the precedence in the aspects of inexpensiveness, celerity, purity, sensitivity and specificity when compared with the traditional techniques. Such assays potentially grant a practicable way of detecting inchoate cancers and guiding immunotherapy in clinic.

Reference dose prediction by using CDK molecular descriptors: A non-experimental method.

Reference dose (RfD) is an estimate of a daily dose that individual can be exposed chronically without obvious deleterious effects during a lifetime. In the area of toxicology, researchers always use the traditional approach by employing NOAEL/LOAEL or the benchmark dose (BMD) and other dose-response approaches to estimate RfD. These methods have, despite their typicalness, certain limitations. In this study, we present a novel method of the estimation of reference dose without experiments. The information of the organic chemicals is available from the Integrated Risk Information System (IRIS) of USEPA. Molecular descriptors for each molecular structure were calculated by an integrated platform, and the chemicals were classified into four categories based on molecular similarity: 128 contained benzene rings, 47 were heteroaromatics, 104 contained halogen substituents and 44 were halogenated aliphatic hydrocarbons. The predictive model of RfD was constructed by the multiple linear stepwise regression (MLR) method. Approximately 95% and 82% of the data points differ by less than 10-fold and 5-fold between the predicted values and the true values respectively. The non-experimental method improves the estimation efficiency and has a certain reference value to predict.

Insight into structural composition of dissolved organic matter in saline-alkali soil by fluorescence spectroscopy coupled with self-organizing map and structural equation modeling.

Soil salinization has been occurring all over the world, which severely affected crop production and threatened the life of mankind. It is necessary to take serious steps to improve soil fertility for the sustainability and productive capacity of agriculture. Soil samples of different depths were collected from native vegetation communities (Comm. Phragmites communis (CPC) and Comm. Populus alba (CPA)) and irrigated crops (corn fields (CFD) and seed melon fields (SMF)) in Hetao irrigation area of China. Three dimensional excitation-emission matrix (EEM) fluorescence technology combined with self-organizing map were used to analyze the dissolved organic matter (DOM) composition and structural characteristics in saline-alkali soils and its spatial distribution under different vegetation covers. Critical factors were recognized by classification and regression tree (CART) for distinguishing soil samples, and latent factors were revealed with structural equation modeling (SEM) for improving the humification degree of DOM from saline soils in Hetao irrigation area. Five components were obtained in the DOM substances, i.e., tyrosine-like (C1), tryptophan-like (C2), UV fulvic-like (C3), visible fulvic-like (C4) and humic-like (C5). The protein-like peaks were all obvious, and the fulvic-like peaks (600-735 a.u.) were conspicuous in the CPC soil than in others, except CFD1 and SMF1. C1 was the critical factor to distinguish native vegetation from irrigated crops, and C1 and C2 were the critical factors to distinguish CFD from SMF. Contrary to the HA/FA (0.20) and A/C (0.25), the path coefficient (-0.15) of sources with T/H was negative, indicating that the incremental contents of fluorenscense substances were in the sequences of protein-like > visible fulvic-like > UV fulvic-like > humic-like, affecting by the allochthonous. C1 (1.00) and C4 (1.00) were the primary components for improving the humification degree of DOM, which were principally originated from plant debris. EEM combined with self-organizing map, CART and SEM is an efficient way to distinguish different salinized soils and reveal the latent factors for improving the soil fertility.