SHP2 ablation mitigates osteoarthritic cartilage degeneration by promoting chondrocyte anabolism through SOX9.

Articular cartilage phenotypic homeostasis is crucial for life-long joint function, but the underlying cellular and molecular mechanisms governing chondrocyte stability remain poorly understood. Here, we show that the protein tyrosine phosphatase SHP2 is differentially expressed in articular cartilage (AC) and growth plate cartilage (GPC) and that it negatively regulates cell proliferation and cartilage phenotypic program. Postnatal SHP2 deletion in Prg4+ AC chondrocytes increased articular cellularity and thickness, whereas SHP2 deletion in Acan+ pan-chondrocytes caused excessive GPC chondrocyte proliferation and led to joint malformation post-puberty. These observations were verified in mice and in cultured chondrocytes following treatment with the SHP2 PROTAC inhibitor SHP2D26. Further mechanistic studies indicated that SHP2 negatively regulates SOX9 stability and transcriptional activity by influencing SOX9 phosphorylation and promoting its proteasome degradation. In contrast to published work, SHP2 ablation in chondrocytes did not impact IL-1-evoked inflammation responses, and SHP2's negative regulation of SOX9 could be curtailed by genetic or chemical SHP2 inhibition, suggesting that manipulating SHP2 signaling has translational potential for diseases of cartilage dyshomeostasis.

Porous NiMoO4 Nanosheet Films and a Device with Ultralarge Optical Modulation for Electrochromic Energy-Storage Applications.

Reducing building energy consumption, improving aesthetics, and improving occupant privacy as well as comfort by dynamically adjusting solar radiation are important application areas for electrochromic (EC) smart windows. However, the current transition metal oxides still cannot meet the requirements of neutral coloration and large optical modulation. We report NiMoO4 nanosheet films directly grown on fluorine-doped tin oxide glasses. The as-grown NiMoO4 film not only achieves neutral coloration from transparent to dark brown but also shows an ultralarge optical modulation (86.8% at 480 nm) and excellent cycling stability (99.4% retention of maximum optical modulation after 1500 cycles). Meanwhile, an EC device demonstrating good EC performance was constructed. These results will greatly promote the research and development of binary transition metal oxides for both EC and energy-storage applications, and NiMoO4 films may be an excellent candidate to replace NiO films as ion-storage layers in complementary EC devices with WO3 films as EC layers.

tRNA modification enzyme MiaB connects environmental cues to activation of Pseudomonas aeruginosa type III secretion system.

Pseudomonas aeruginosa, a major inhabitant of numerous environmental reservoirs, is a momentous opportunistic human pathogen associated with severe infections even death in the patients suffering from immune deficiencies or metabolic diseases. Type III secretion system (T3SS) employed by P. aeruginosa to inject effector proteins into host cells is one of the pivotal virulence factors pertaining to acute infections caused by this pathogen. Previous studies showed that P. aeruginosa T3SS is regulated by various environmental cues such as calcium concentration and the host signal spermidine. However, how T3SS is regulated and expressed particularly under the ever-changing environmental conditions remains largely elusive. In this study, we reported that a tRNA modification enzyme PA3980, designated as MiaB, positively regulated T3SS gene expression in P. aeruginosa and was essential for the induced cytotoxicity of human lung epithelial cells. Further genetic assays revealed that MiaB promoted T3SS gene expression by repressing the LadS-Gac/Rsm signaling pathway and through the T3SS master regulator ExsA. Interestingly, ladS, gacA, rsmY and rsmZ in the LadS-Gac/Rsm signaling pathway seemed potential targets under the independent regulation of MiaB. Moreover, expression of MiaB was found to be induced by the cAMP-dependent global regulator Vfr as well as the spermidine transporter-dependent signaling pathway and thereafter functioned to mediate their regulation on the T3SS gene expression. Together, these results revealed a novel regulatory mechanism for MiaB, with which it integrates different environmental cues to modulate T3SS gene expression in this important bacterial pathogen.

Ubiquitin-dependent Argonauteprotein MEL1 degradation is essential for rice sporogenesis and phasiRNA target regulation.

MEIOSIS ARRESTED AT LEPTOTENE1 (MEL1), a rice (Oryza sativa) Argonaute (AGO) protein, has been reported to function specifically at premeiotic and meiotic stages of germ cell development and is associated with a novel class of germ cell-specific small noncoding RNAs called phased small RNAs (phasiRNAs). MEL1 accumulation is temporally and spatially regulated and is eliminated after meiosis. However, the metabolism and turnover (i.e. the homeostasis) of MEL1 during germ cell development remains unknown. Here, we show that MEL1 is ubiquitinated and subsequently degraded via the proteasome pathway in vivo during late sporogenesis. Abnormal accumulation of MEL1 after meiosis leads to a semi-sterile phenotype. We identified a monocot-specific E3 ligase, XBOS36, a CULLIN RING-box protein, that is responsible for the degradation of MEL1. Ubiquitination at four K residues at the N terminus of MEL1 by XBOS36 induces its degradation. Importantly, inhibition of MEL1 degradation either by XBOS36 knockdown or by MEL1 overexpression prevents the formation of pollen at the microspore stage. Further mechanistic analysis showed that disrupting MEL1 homeostasis in germ cells leads to off-target cleavage of phasiRNA target genes. Our findings thus provide insight into the communication between a monocot-specific E3 ligase and an AGO protein during plant reproductive development.

Anti-HER2 therapy response assessment for guiding treatment (de-)escalation in early HER2-positive breast cancer using a novel deep learning radiomics model.

OBJECTIVES: Anti-HER2 targeted therapy significantly reduces risk of relapse in HER2 + breast cancer. New measures are needed for a precise risk stratification to guide (de-)escalation of anti-HER2 strategy. METHODS: A total of 726 HER2 + cases who received no/single/dual anti-HER2 targeted therapies were split into three respective cohorts. A deep learning model (DeepTEPP) based on preoperative breast magnetic resonance (MR) was developed. Patients were scored and categorized into low-, moderate-, and high-risk groups. Recurrence-free survival (RFS) was compared in patients with different risk groups according to the anti-HER2 treatment they received, to validate the value of DeepTEPP in predicting treatment efficacy and guiding anti-HER2 strategy. RESULTS: DeepTEPP was capable of risk stratification and guiding anti-HER2 treatment strategy: DeepTEPP-Low patients (60.5%) did not derive significant RFS benefit from trastuzumab (p = 0.144), proposing an anti-HER2 de-escalation. DeepTEPP-Moderate patients (19.8%) significantly benefited from trastuzumab (p = 0.048), but did not obtain additional improvements from pertuzumab (p = 0.125). DeepTEPP-High patients (19.7%) significantly benefited from dual HER2 blockade (p = 0.045), suggesting an anti-HER2 escalation. CONCLUSIONS: DeepTEPP represents a pioneering MR-based deep learning model that enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thereby providing valuable guidance for anti-HER2 (de-)escalation strategies. DeepTEPP provides an important reference for choosing the appropriate individualized treatment in HER2 + breast cancer patients, warranting prospective validation. CLINICAL RELEVANCE STATEMENT: We built an MR-based deep learning model DeepTEPP, which enables the non-invasive prediction of adjuvant anti-HER2 effectiveness, thus guiding anti-HER2 (de-)escalation strategies in early HER2-positive breast cancer patients. KEY POINTS: • DeepTEPP is able to predict anti-HER2 effectiveness and to guide treatment (de-)escalation. • DeepTEPP demonstrated an impressive prognostic efficacy for recurrence-free survival and overall survival. • To our knowledge, this is one of the very few, also the largest study to test the efficacy of a deep learning model extracted from breast MR images on HER2-positive breast cancer survival and anti-HER2 therapy effectiveness prediction.

Molecular Insights into the Effects of F16L and F19L Substitutions on the Conformation and Aggregation Dynamics of Human Calcitonin.

Human calcitonin (hCT) regulates calcium-phosphorus metabolism, but its amyloid aggregation disrupts physiological activity, increases thyroid carcinoma risk, and hampers its clinical use for bone-related diseases like osteoporosis and Paget's disease. Improving hCT with targeted modifications to mitigate amyloid formation while maintaining its function holds promise as a strategy. Understanding how each residue in hCT's amyloidogenic core affects its structure and aggregation dynamics is crucial for designing effective analogues. Mutants F16L-hCT and F19L-hCT, where Phe residues in the core are replaced with Leu as in nonamyloidogenic salmon calcitonin, showed different aggregation kinetics. However, the molecular effects of these substitutions in hCT are still unclear. Here, we systematically investigated the folding and self-assembly conformational dynamics of hCT, F16L-hCT, and F19L-hCT through multiple long-time scale independent atomistic discrete molecular dynamics (DMD) simulations. Our results indicated that the hCT monomer primarily assumed unstructured conformations with dynamic helices around residues 4-12 and 14-21. During self-assembly, the amyloidogenic core of hCT14-21 converted from dynamic helices to ß-sheets. However, substituting F16L did not induce significant conformational changes, as F16L-hCT exhibited characteristics similar to those of wild-type hCT in both monomeric and oligomeric states. In contrast, F19L-hCT exhibited substantially more helices and fewer ß-sheets than did hCT, irrespective of their monomers or oligomers. The substitution of F19L significantly enhanced the stability of the helical conformation for hCT14-21, thereby suppressing the helix-to-ß-sheet conformational conversion. Overall, our findings elucidate the molecular mechanisms underlying hCT aggregation and the effects of F16L and F19L substitutions on the conformational dynamics of hCT, highlighting the critical role of F19 as an important target in the design of amyloid-resistant hCT analogs for future clinical applications.

Aerobic Microbial Transformation of Fluorinated Liquid Crystal Monomer: New Pathways and Mechanism.

Fluorinated liquid crystal monomers (FLCMs) have been suggested as emerging contaminants, raising global concern due to their frequent occurrence, potential toxic effects, and endurance capacity in the environment. However, the environmental fate of the FLCMs remains unknown. To fill this knowledge gap, we investigated the aerobic microbial transformation mechanisms of an important FLCM, 4-[difluoro(3,4,5-trifluorophenoxy)methyl]-3, 5-difluoro-4'-propylbiphenyl (DTMDPB), using an enrichment culture termed as BG1. Our findings revealed that 67.5 ± 2.1% of the initially added DTMDPB was transformed in 10 days under optimal conditions. A total of 14 microbial transformation products obtained due to a series of reactions (e.g., reductive defluorination, ether bond cleavage, demethylation, oxidative hydroxylation and aromatic ring opening, sulfonation, glucuronidation, O-methylation, and thiolation) were identified. Consortium BG1 harbored essential genes that could transform DTMDPB, such as dehalogenation-related genes [e.g., glutathione S-transferase gene (GST), 2-haloacid dehalogenase gene (2-HAD), nrdB, nuoC, and nuoD]; hydroxylating-related genes hcaC, ubiH, and COQ7; aromatic ring opening-related genes ligB and catE; and methyltransferase genes ubiE and ubiG. Two DTMDPB-degrading strains were isolated, which are affiliated with the genus Sphingopyxis and Agromyces. This study provides a novel insight into the microbial transformation of FLCMs. The findings of this study have important implications for the development of bioremediation strategies aimed at addressing sites contaminated with FLCMs.

The effect of childhood family adversity on adulthood depression among Chinese older migrant workers: gender differences in the mediating role of social-ecological systems.

BACKGROUND: Older migrant workers (OMWs) in China face unique challenges rooted in their early life experiences, which increase their vulnerability to psychological and behavioral problems in adulthood. By utilizing the cumulative disadvantage model and the social-ecological systems theory, this study explored the effect of childhood family adversity on adulthood depression in the mediating roles of OMWs' social-ecological microsystem and mesosystem and further examined gender differences in these associations. METHODS: Data were collected from the China Health and Retirement Longitudinal Study (CHARLS), involving a sample of 4,309 OMWs aged 50 and above. The measures included the Center for Epidemiological Research Depression Scale, childhood family adversity, socioeconomic status, marital quality, and physical and cognitive health. RESULTS: Childhood family adversity was positively associated with adulthood depression among OMWs. Social microsystem (physical and cognitive health) and mesosystem (marital quality and socioeconomic status) factors significantly mediated this relationship. Multi-group analysis revealed that the mediating effects of marital quality and socioeconomic status were stronger for female OMWs, while the mediating effects of physical and cognitive health were stronger for male OMWs. CONCLUSIONS: The findings suggest that childhood family adversity has a lasting impact on the mental health of OMWs, and that social-ecological systems factors play an important role in this relationship. The study also highlights the need for gender-specific interventions to address the mental health needs of OMWs.

Advancing Alzheimer's research: Radiomics visualization of the default mode network in cerebral perfusion imaging.

OBJECTIVE: Alzheimer's disease, an irreversible neurological condition, demands timely diagnosis for effective clinical intervention. This study employs radiomics analysis to assess image features in default mode network cerebral perfusion imaging among individuals with cognitive impairment. METHODS: A radiomics analysis of cerebral perfusion imaging was conducted on 117 patients with cognitive impairment. They were divided into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest were employed to select and model image features, followed by logistic regression analysis of LASSO and Random Forest results. Diagnostic performance was assessed by calculating the area under the curve (AUC). RESULTS: In the training set, LASSO achieved AUC of 0.978, Random Forest had an AUC of 0.933. In the validation set, LASSO had AUC of 0.859, Random Forest had AUC of 0.986. By conducting Logistic Regression analysis in combination with LASSO and Random Forest, we identified a total of five radiomics features, with four related to morphology and one to textural features, originating from the medial prefrontal cortex and middle temporal gyrus. In the training set, Logistic Regression achieved AUC of 0.911, while in the validation set, it attained AUC of 0.925. CONCLUSION: The medial prefrontal cortex and middle temporal gyrus are the two brain regions within the default mode network that hold the highest significance for Alzheimer's disease diagnosis. Radiomics analysis contributes to the clinical assessment of Alzheimer's disease by delving into image data to extract deeper layers of information.

A portable intelligent hydrogel platform for multicolor visual detection of HAase.

Hyaluronidase (HAase) is an important endoglycosidase involved in numerous physiological and pathological processes, such as apoptosis, senescence, and cancer progression. Simple, convenient, and sensitive detection of HAase is important for clinical diagnosis. Herein, an easy-to-operate multicolor visual sensing strategy was developed for HAase determination. The proposed sensor was composed of an enzyme-responsive hydrogel and a nanochromogenic system (gold nanobipyramids (AuNBPs)). The enzyme-responsive hydrogel, formed by polyethyleneimine-hyaluronic acid (PEI-HA), was specifically hydrolyzed with HAase, leading to the release of platinum nanoparticles (PtNPs). Subsequently, PtNPs catalyzed the mixed system of 3,3',5,5'-tetramethylbenzidine (TMB) and H2O2 to produce TMB2+ under acidic conditions. Then, TMB2+ effectively etched the AuNBPs and resulted in morphological changes in the AuNBPs, accompanied by a blueshift in the localized surface plasmon resonance peak and vibrant colors. Therefore, HAase can be semiquantitatively determined by directly observing the color change of AuNBPs with the naked eye. On the basis of this, the method has a linear detection range of HAase concentrations between 0.6 and 40 U/mL, with a detection limit of 0.3 U/mL. In addition, our designed multicolor biosensor successfully detected the concentration of HAase in human serum samples. The results showed no obvious difference between this method and enzyme-linked immunosorbent assay, indicating the good accuracy and usability of the suggested method.

Integrated metabolomic and transcriptomic analysis reveals variation in the metabolites of Dendrobium officinale, Dendrobium huoshanense, Dendrobium nobile.

INTRODUCTION: Dendrobium is a perennial herb of the genus Dendrobium in the orchid family. Generally, Dendrobium officinale (TP) and Dendrobium huoshanense (HS) are both considered to have the function of yin-nourishing, while Dendrobium nobile (JC) has better efficacy of heat-clearing. However, because of the wide variety of Dendrobium species, the classification and clinical application of Dendrobium are often confused clearly distinguished in different medicinal uses. OBJECTIVE: In order to compare the differentially accumulated metabolites (DAMs) and differentially expressed genes (DEGs) of the three Dendrobium. METHODS: We selected TP, HS, and JC cultivated on stone for metabolomic and transcriptomic analyses between 2 and 3 years. RESULTS: The results showed that a total of 489 metabolites were obtained, including 72 were DAMs. The 72 DAMs were mainly enriched in metabolic pathways and biosynthesis of secondary metabolites. Transcriptome analysis results showed that 1,038 annotated DEGs were identified among the three Dendrobium species. The comprehensive analysis showed that the three Dendrobium differed in the distribution of the content of four major active components: flavonoids, amino acids, alkaloids, and sugars and alcohols, among which the DAMs and DEGs were mainly enriched in metabolic pathways and secondary metabolite biosynthesis. CONCLUSION: In this study, metabolomics and transcriptomics were utilized to compare the differences among the three species of Dendrobium, to provide theoretical references for future research and selection of different species of Dendrobium based on different medicinal uses, and to lay the foundation for further research on the biosynthesis of flavonoids in Dendrobium.

Cone-beam CT landmark detection for measuring basal bone width: a retrospective validation study.

BACKGROUND: Accurate assessment of basal bone width is essential for distinguishing individuals with normal occlusion from patients with maxillary transverse deficiency who may require maxillary expansion. Herein, we evaluated the effectiveness of a deep learning (DL) model in measuring landmarks of basal bone width and assessed the consistency of automated measurements compared to manual measurements. METHODS: Based on the U-Net algorithm, a coarse-to-fine DL model was developed and trained using 80 cone-beam computed tomography (CBCT) images. The model's prediction capabilities were validated on 10 CBCT scans and tested on an additional 34. To evaluate the performance of the DL model, its measurements were compared with those taken manually by one junior orthodontist using the concordance correlation coefficient (CCC). RESULTS: It took approximately 1.5 s for the DL model to perform the measurement task in only CBCT images. This framework showed a mean radial error of 1.22 ± 1.93 mm and achieved successful detection rates of 71.34%, 81.37%, 86.77%, and 91.18% in the 2.0-, 2.5-, 3.0-, and 4.0-mm ranges, respectively. The CCCs (95% confidence interval) of the maxillary basal bone width and mandibular basal bone width distance between the DL model and manual measurement for the 34 cases were 0.96 (0.94-0.97) and 0.98 (0.97-0.99), respectively. CONCLUSION: The novel DL framework developed in this study improved the diagnostic accuracy of the individual assessment of maxillary width. These results emphasize the potential applicability of this framework as a computer-aided diagnostic tool in orthodontic practice.

Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: a pilot study.

BACKGROUND: Deep learning, as an artificial intelligence method has been proved to be powerful in analyzing images. The purpose of this study is to construct a deep learning-based model (ToothNet) for the simultaneous detection of dental caries and fissure sealants in intraoral photos. METHODS: A total of 1020 intraoral photos were collected from 762 volunteers. Teeth, caries and sealants were annotated by two endodontists using the LabelMe tool. ToothNet was developed by modifying the YOLOX framework for simultaneous detection of caries and fissure sealants. The area under curve (AUC) in the receiver operating characteristic curve (ROC) and free-response ROC (FROC) curves were used to evaluate model performance in the following aspects: (i) classification accuracy of detecting dental caries and fissure sealants from a photograph (image-level); and (ii) localization accuracy of the locations of predicted dental caries and fissure sealants (tooth-level). The performance of ToothNet and dentist with 1year of experience (1-year dentist) were compared at tooth-level and image-level using Wilcoxon test and DeLong test. RESULTS: At the image level, ToothNet achieved an AUC of 0.925 (95% CI, 0.880-0.958) for caries detection and 0.902 (95% CI, 0.853-0.940) for sealant detection. At the tooth level, with a confidence threshold of 0.5, the sensitivity, precision, and F1-score for caries detection were 0.807, 0.814, and 0.810, respectively. For fissure sealant detection, the values were 0.714, 0.750, and 0.731. Compared with ToothNet, the 1-year dentist had a lower F1 value (0.599, p < 0.0001) and AUC (0.749, p < 0.0001) in caries detection, and a lower F1 value (0.727, p = 0.023) and similar AUC (0.829, p = 0.154) in sealant detection. CONCLUSIONS: The proposed deep learning model achieved multi-task simultaneous detection in intraoral photos and showed good performance in the detection of dental caries and fissure sealants. Compared with 1-year dentist, the model has advantages in caries detection and is equivalent in fissure sealants detection.

Neoadjuvant docetaxel, epirubicin, and cyclophosphamide with or without metformin in breast cancer patients with metabolic abnormality: results from the randomized Phase II NeoMET trial.

PURPOSE: Breast cancer patients with metabolic syndrome (MetS) and its components show worse treatment responses to chemotherapy. Metformin is a widely used antidiabetic drug which also shows potential anticancer effect. This study aims to evaluate the efficacy, safety, and metabolic parameters change of metformin combined with docetaxel, epirubicin, and cyclophosphamide (TEC) in neoadjuvant treatment (NAT) for breast cancer patients with metabolic abnormality. METHODS: Eligible breast cancer patients were randomized to receive six cycles of TEC (docetaxel 75 mg/m2, epirubicin 75 mg/m2, and cyclophosphamide 500 mg/m2, d1, q3w) or TEC with metformin (TECM, TEC with oral metformin 850 mg once daily for the first cycle, then 850 mg twice daily for the following cycles). The primary end point was total pathological complete response (tpCR, ypTis/0N0) rate. RESULTS: Ninety-two patients were enrolled and randomized from October 2013 to December 2019: 88 patients were available for response and safety assessment. The tpCR rates were 12.5% (5/40) and 14.6% (7/48) in the TEC and TECM groups, respectively (P = 0.777). There was no difference in Ki67 decrease after NAT between two groups (P = 0.456). Toxicity profile were similar between two groups. No grade 3 or higher diarrhea were recorded. Total cholesterol (TC) and high-density lipoprotein cholesterol worsened after NAT in the TEC arm but remained stable in the TECM arm. The absolute increase of TC and low-density lipoprotein cholesterol (LDL-C) was significantly lower in the TECM group compared with the TEC group. After a median follow-up of 40.8 (4.7-70.8) months, no survival difference was observed between TEC and TECM groups (all P > 0.05). CONCLUSION: Adding metformin to TEC didn't increase pCR rate and disease outcome in breast cancer patients with metabolic abnormality. However, additional metformin treatment with chemotherapy would prevent TC and LDL-C increase after NAT. Trial Registration ClinicalTrials.gov Identifier: NCT01929811.

Construction of Functionalized 2H-Pyran-2-ones via N-Heterocyclic Carbene-Catalyzed [3 + 3] Annulation of Alkynyl Esters with Enolizable Ketones.

A new synthesis of functionalized 2H-pyran-2-ones has been developed through N-heterocyclic carbene-catalyzed formal [3 + 3] annulation of alkynyl esters with enolizable ketones. The key to the success of this protocol relies on the use of an NHC instead of a tertiary amine as the catalyst. This protocol also features a broad substrate scope and mild metal-free conditions, offering simple and rapid access to the target molecules in a highly regioselective manner.

Biotransformation of Organophosphate Esters by Rice and Rhizosphere Microbiome: Multiple Metabolic Pathways, Mechanism, and Toxicity Assessment.

The biotransformation behavior and toxicity of organophosphate esters (OPEs) in rice and rhizosphere microbiomes were comprehensively studied by hydroponic experiments. OPEs with lower hydrophobicity were liable to be translocated acropetally, and rhizosphere microbiome could reduce the uptake and translocation of OPEs in rice tissues. New metabolites were successfully identified in rice and rhizosphere microbiome, including hydrolysis, hydroxylated, methylated, and glutathione-, glucuronide-, and sulfate-conjugated products. Rhizobacteria and plants could cooperate to form a complex ecological interaction web for OPE elimination. Furthermore, active members of the rhizosphere microbiome during OPE degradation were revealed and the metagenomic analysis indicated that most of these active populations contained OPE-degrading genes. The results of metabolomics analyses for phytotoxicity assessment implied that several key function metabolic pathways of the rice plant were found perturbed by metabolites, such as diphenyl phosphate and monophenyl phosphate. In addition, the involved metabolism mechanisms, such as the carbohydrate metabolism, amino acid metabolism and synthesis, and nucleotide metabolism in Escherichia coli, were significantly altered after exposure to the products mixture of OPEs generated by rhizosphere microbiome. This work for the first time gives a comprehensive understanding of the entire metabolism of OPEs in plants and associated microbiome, and provides support for the ongoing risk assessment of emerging contaminants and, most critically, their transformation products.

Identifying acute kidney injury subphenotypes using an outcome-driven deep-learning approach.

OBJECTIVE: Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is characterized by an abrupt decrease in kidney function within a few hours or days, leading to kidney failure or damage. Although AKI is associated with poor outcomes, current guidelines overlook the heterogeneity among patients with this condition. Identification of AKI subphenotypes could enable targeted interventions and a deeper understanding of the injury's pathophysiology. While previous approaches based on unsupervised representation learning have been used to identify AKI subphenotypes, these methods cannot assess time series or disease severity. METHODS: In this study, we developed a data- and outcome-driven deep-learning (DL) approach to identify and analyze AKI subphenotypes with prognostic and therapeutic implications. Specifically, we developed a supervised long short-term memory (LSTM) autoencoder (AE) with the aim of extracting representation from time-series EHR data that were intricately correlated with mortality. Then, subphenotypes were identified via application of K-means. RESULTS: In two publicly available datasets, three distinct clusters were identified, characterized by mortality rates of 11.3%, 17.3%, and 96.2% in one dataset and 4.6%, 12.1%, and 54.6% in the other. Further analysis demonstrated that AKI subphenotypes identified by our proposed approach were statistically significant on several clinical characteristics and outcomes. CONCLUSION: In this study, our proposed approach could successfully cluster the AKI population in ICU settings into 3 distinct subphenotypes. Thus, such approach could potentially improve outcomes of AKI patients in the ICU, with better risk assessment and potentially better personalized treatment.

Risk Prediction Model for Crohn's Disease Based on Hematological Indicators.

BACKGROUND: The similarity between Crohn's disease (CD) and non-CD, especially with ulcerative colitis (UC) or intestinal tuberculosis (ITB), makes the diagnostic error rate not low. Therefore, there is an urgent need for an efficient, fast, and simple predictive model that can be applied in clinical practice. The purpose of this study is to establish the risk prediction model for CD based on five routine laboratory tests by logistic-regression algorithm, to construct the early warning model for CD and the corresponding visual nomograph, and to provide an accurate and convenient reference for the risk determination and differential diagnosis of CD, in order to assist clinicians to better manage CD and reduce patient suffering. METHODS: Using a retrospective analysis, a total of 310 cases were collected from 2020 to 2022 at The Sixth Affiliated Hospital, Sun Yat-sen University, who were diagnosed by comprehensive clinical diagnosis, including 100 patients with CD, 50 patients with ulcerative colitis (UC), 110 patients with non-inflammatory bowel disease (non-IBD) diseases (65 cases of intestinal tuberculosis, radioactive enterocolitis 39, and colonic diverticulitis 6), and 50 healthy individuals (NC) in the non-CD group. Risk prediction models were established by measuring ESR, Hb, WBC, ALb, and CH levels in hematology. The models were evaluated and visualized using logistic-regression algorithm. RESULTS: 1) ESR, WBC, and WBC/CH ratios in the CD group were higher than those in the non-CD group, while ALb, Hb, CH, WBC/ESR ratio, and Hb/WBC ratio were lower than those in the non-CD group, and the differences were statistically significant (all p < 0.05). 2) CD occurrence had a strong correlation with the WBC/CH ratio, with the correlation coefficient exceeding 0.4; CD occurrence was correlated with other indicators. 3) A risk prediction model containing age, gender, ESR, ALb, Hb, CH, WBC, WBC/CH, WBC/ESR, and Hb/WBC characteristics was constructed using a logistic-regression algorithm. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of the model were 83.0%, 76.2%, 59.0%, 90.5%, and 0.86, respectively. The model based on the corresponding index also had high diagnostic accuracy (AUC = 0.88) for differentiating CD from ITB. Visual nomograph based on the logistic-regression algorithm was also constructed for clinical application reference. CONCLUSIONS: In this study, a CD risk prediction model was established and visualized by five conventional hema-tological indices: ESR, Hb, WBC, ALb, and CH, in addition to a high diagnostic accuracy for the differential diagnosis of CD and ITB.

Ancient genomes reveal tropical bovid species in the Tibetan Plateau contributed to the prevalence of hunting game until the late Neolithic.

Local wild bovids have been determined to be important prey on the northeastern Tibetan Plateau (NETP), where hunting game was a major subsistence strategy until the late Neolithic, when farming lifestyles dominated in the neighboring Loess Plateau. However, the species affiliation and population ecology of these prehistoric wild bovids in the prehistoric NETP remain unknown. Ancient DNA (aDNA) analysis is highly informative in decoding this puzzle. Here, we applied aDNA analysis to fragmented bovid and rhinoceros specimens dating ∼5,200 y B.P. from the Neolithic site of Shannashuzha located in the marginal area of the NETP. Utilizing both whole genomes and mitochondrial DNA, our results demonstrate that the range of the present-day tropical gaur (Bos gaurus) extended as far north as the margins of the NETP during the late Neolithic from ∼29°N to ∼34°N. Furthermore, comparative analysis with zooarchaeological and paleoclimatic evidence indicated that a high summer temperature in the late Neolithic might have facilitated the northward expansion of tropical animals (at least gaur and Sumatran-like rhinoceros) to the NETP. This enriched the diversity of wildlife, thus providing abundant hunting resources for humans and facilitating the exploration of the Tibetan Plateau as one of the last habitats for hunting game in East Asia.

Superior photo-Fenton degradation of acetamiprid by α- Fe2O3-pillared bentonite/L-cysteine complex: Synergy of L-cysteine and visible light.

Acetamiprid is a potential threat to human health, aquatic life, soil microorganisms and beneficial insects as a recalcitrant pollutant in wastewater treatment plant effluents. In this work, the synthesized α-Fe2O3-pillared bentonite (FPB) was used to degrade acetamiprid in the photo-Fenton process with the assistance of L-cysteine (L-cys) existing in natural aquatic environment. The kinetic constant k of acetamiprid degradation by FPB/L-cys in the photo-Fenton process was far more than that in the Fenton process of FPB/L-cys lacking light and the photo-Fenton process of FPB without L-cys. The positive linear correlation between k and ≡Fe(II) content indicated the synergy of L-cys and visible light accelerated the cycle of Fe(III) to Fe(II) in FPB/L-cys during the degradation of acetamiprid by elevating the visible light response of FPB, and promoting the interfacial electron transfer from the active sites of FPB to hydrogen peroxide and photo-generated electron transfer from conduction band of α-Fe2O3 to the active sites of FPB. The boosting •OH and 1O2 were predominantly responsible for acetamiprid degradation. Acetamiprid could be efficiently degraded into less toxic small molecules in the photo-Fenton process via C-N bond breaking, hydroxylation, demethylation, ketonization, dechlorination, and ring cleavage.