Sideroflexin-1 promotes progression and sensitivity to lapatinib in triple-negative breast cancer by inhibiting TOLLIP-mediated autophagic degradation of CIP2A.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and it lacks specific therapeutic targets and effective treatment protocols. By analyzing a proteomic TNBC dataset, we found significant upregulation of sideroflexin 1 (SFXN1) in tumor tissues. However, the precise function of SFXN1 in TNBC remains unclear. Immunoblotting was performed to determine SFXN1 expression levels. Label-free quantitative proteomics and liquid chromatography-tandem mass spectrometry were used to identify the downstream targets of SFXN1. Mechanistic studies of SFXN1 and cellular inhibitor of PP2A (CIP2A) were performed using immunoblotting, immunofluorescence staining, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Functional experiments were used to investigate the role of SFXN1 in TNBC cells. SFXN1 was significantly overexpressed in TNBC tumor tissues and was associated with unfavorable outcomes in patients with TNBC. Functional experiments demonstrated that SFXN1 promoted TNBC growth and metastasis in vitro and in vivo. Mechanistic studies revealed that SFXN1 promoted TNBC progression by inhibiting the autophagy receptor TOLLIP (toll interacting protein)-mediated autophagic degradation of CIP2A. The pro-tumorigenic effect of SFXN1 overexpression was partially prevented by lapatinib-mediated inhibition of the CIP2A/PP2A/p-AKT pathway. These findings may provide a new targeted therapy for patients with TNBC.

Characterizing the mechanism of action for mRNA therapeutics for the treatment of propionic acidemia, methylmalonic acidemia, and phenylketonuria.

Messenger RNA (mRNA) therapeutics delivered via lipid nanoparticles hold the potential to treat metabolic diseases caused by protein deficiency, including propionic acidemia (PA), methylmalonic acidemia (MMA), and phenylketonuria (PKU). Herein we report results from multiple independent preclinical studies of mRNA-3927 (an investigational treatment for PA), mRNA-3705 (an investigational treatment for MMA), and mRNA-3210 (an investigational treatment for PKU) in murine models of each disease. All 3 mRNA therapeutics exhibited pharmacokinetic/pharmacodynamic (PK/PD) responses in their respective murine model by driving mRNA, protein, and/or protein activity responses, as well as by decreasing levels of the relevant biomarker(s) when compared to control-treated animals. These preclinical data were then used to develop translational PK/PD models, which were scaled allometrically to humans to predict starting doses for first-in-human clinical studies for each disease. The predicted first-in-human doses for mRNA-3927, mRNA-3705, and mRNA-3210 were determined to be 0.3, 0.1, and 0.4 mg/kg, respectively.

Deciphering the pathogenic risks of microplastics as emerging particulate organic matter in aquatic ecosystem.

Microplastics are accumulating rapidly in aquatic ecosystems, providing habitats for pathogens and vectors for antibiotic resistance genes (ARGs), potentially increasing pathogenic risks. However, few studies have considered microplastics as particulate organic matter (POM) to elucidate their pathogenic risks and underlying mechanisms. Here, we performed microcosm experiments with microplastics and natural POM (leaves, algae, soil), thoroughly investigating their distinct effects on the community compositions, functional profiles, opportunistic pathogens, and ARGs in Particle-Associated (PA) and Free-Living (FL) bacterial communities. We found that both microplastics and leaves have comparable impacts on microbial community structures and functions, enriching opportunistic pathogens and ARGs, which may pose potential environmental risks. These effects are likely driven by their influences on water properties, including dissolved organic carbon, nitrate, DO, and pH. However, microplastics uniquely promoted pathogens as keystone species and further amplified their capacity as hosts for ARGs, potentially posing a higher pathogenic risk than natural POM. Our research also emphasized the importance of considering both PA and FL bacteria when assessing microplastic impacts, as they exhibited different responses. Overall, our study elucidates the role and underlying mechanism of microplastics as an emerging POM in intensifying pathogenic risks of aquatic ecosystems in comparison with conventional natural POM.

Pipelle Endometrial Sampling With a Full Bladder Compared With Standard Care: A Randomized Controlled Trial.

OBJECTIVE: To compare insertion failure rates for Pipelle endometrial sampling with a full bladder compared with the standard process (not taking into account bladder status) without cervical manipulation. METHODS: A single-masked randomized trial was conducted in a single tertiary care center from July 2021 to January 2022. Two hundred fourteen participants aged 18 years or older who were scheduled for outpatient Pipelle endometrial sampling were randomized: 107 each to having a full bladder (by oral water intake) or standard process (without delayed sampling and bladder status not taken into account). Women with known cervical stenosis, gynecologic malignancy, uterine anomalies, leiomyoma distorting the uterine cavity, acute cervicitis, urge bladder dysfunction, intense anxiety, need for anesthesia or analgesic before the procedure, positive pregnancy test, or previous failed office endometrial sampling were excluded. The primary outcome was the insertion failure rate of endometrial sampling at first attempt. Secondary outcomes were pain during procedure, satisfaction score, analgesia use, procedure duration, and need for cervical manipulation. Factoring in a baseline insertion failure rate of 30.0%, relative risk of 0.45, α of 0.05, 80.0% power, and a dropout rate of 10.0%, we needed 107 participants in each arm. RESULTS: The insertion failure rate was significantly lower in the full bladder group compared with standard process: 25 of 107 (23.4%) compared with 45 of 107 (42.1%) (relative risk 0.56, 95% CI, 0.37-0.84; number needed to treat to benefit 6.0, 95% CI, 3.20-15.70). Pain score (median) during the procedure (interquartile range) was 4 (3-6) compared with 5 (3-8) ( P =.004); patient satisfaction score was 8 (7-9) compared with 7 (4-8) ( P <.001); and mean±SD procedure duration was 3.0±2.4 compared with 4.7±2.9 minutes ( P <.001) for the full bladder and standard process arm, respectively. Other secondary outcomes of cervical laceration, analgesia use, and adequacy of endometrial tissue for histopathologic assessment were not significantly different between groups. CONCLUSION: Pipelle endometrial sampling with a full bladder reduces the initial insertion failure rate, procedure-related pain, and duration of sampling and increases patient satisfaction compared with the standard process. CLINICAL TRIAL REGISTRATION: ISRCTN, ISRCTN33938192.

The DRAP1/DR1 Repressor Complex Increases mTOR Activity to Promote Progression and Confer Everolimus Sensitivity in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. Transcriptional dysregulation is a hallmark of cancer, and several transcriptional regulators have been demonstrated to contribute to cancer progression. Here, we identified upregulation of the transcriptional corepressor DRAP1 in TNBC, which was closely associated with poor recurrence-free survival in TNBC patients. DRAP1 promoted TNBC proliferation, migration, and invasion in vitro and tumor growth and metastasis in vivo. Mechanistically, the DR1/DRAP1 heterodimer complex inhibited expression of the arginine sensor CASTOR1 and thereby increased activation of mTOR, which sensitized TNBC to treatment with the mTOR inhibitor everolimus. DRAP1 and DR1 also formed a positive feedback loop. DRAP1 enhanced the stability of DR1, recruiting the deubiquitinase USP7 to inhibit its proteasomal degradation; in turn, DR1 directly promoted DRAP1 transcription. Collectively, this study uncovered a DRAP1-DR1 bidirectional regulatory pathway that promotes TNBC progression, suggesting that targeting the DRAP1/DR1 complex might be a potential therapeutic strategy to treat TNBC.

Patient preference and acceptability of self-sampling for cervical screening in colposcopy clinic attenders: A cross-sectional semi-structured survey.

Low vaginal self-sampling has been pioneered as an important development to improve uptake of cervical screening globally. Limited research is available in specific patient groups in the UK exploring views around self-sampling to detect high-risk human papillomavirus (hrHPV) DNA. Therefore, we explored patient views to support development of a novel point-of-care self-sampling cervical cancer screening device, by undertaking a cross-sectional semi-structured questionnaire survey to explore preferences, acceptability, barriers and facilitators around self-sampling. Patients attending a colposcopy clinic, 25-64 years old, were invited to participate after having carried out a low vaginal self-sample using a regular flocked swab. Participants self-completed an anonymous 12-point questionnaire. Quantitative data were analysed in MS Excel and Graphpad Prism, and qualitative data with Nvivo. We recruited 274 patients with a questionnaire response rate of 76%. Acceptability of self-sampling was high (95%, n = 187/197; Cronbachs-α = 0.778). Participants were asked their choice of future screening method: a) low vaginal self-sampling, b) healthcare professional collected vaginal swab, c) cervical brush sample with healthcare professional speculum examination, or d) no preference. Preferences were: a) 37% (n = 74/198), b) 19% (n = 37/198); c) 9% (n = 17/198), and d) 35% (n = 70/198), showing no single option as a strong preference. Key motivators were: Test simplicity (90%, n = 170/190), speed (81%, n = 153/190) and less pain (65%, n = 123/190). Barriers included lack of confidence taking the sample (53%, n = 10/19), resulting in preference for a healthcare professional sample (47%, n = 9/19). Whilst self-sampling showed high acceptability, lack of strong preference for screening method may reflect that respondents attending colposcopy are already engaged with screening and have differing perception of cervical cancer risk. This group appear less likely to 'switch' to self-sampling, and it may be better targeted within primary and community care, focusing on under-screened populations. Any shift in this paradigm in the UK requires comprehensive education and support for patients and providers.

Contrast Volume-to-Estimated Glomerular Filtration Rate Ratio as a Predictor of Short-Term Outcomes Following Transcatheter Aortic Valve Implantation.

Background/Objectives: Contrast-induced acute kidney injury (AKI) is associated with early mortality and adverse events. However, in the setting of transcatheter aortic valve implantation (TAVI), previous literature has failed to establish a correlation between the absolute volume of contrast media administered and mortality. We aimed to investigate the impact of contrast volume administered normalised to estimated glomerular filtration rate (CV/eGFR) on the development of AKI and on 30-day all-cause mortality in TAVI patients. Methods: We retrospectively analysed a cohort of 1150 patients who underwent TAVI at our unit between 2015 and 2018. Results: Follow-up was complete for 1064 patients. There were 23 deaths within the follow-up period and 76 cases of AKI, 9 of which required new renal replacement therapy (RRT). Receiver-operating characteristic (ROC) curve analysis showed fair discrimination for 30-day all-cause mortality at a CV/eGFR ratio of 3.6 (area under the ROC curve (AUC) 0.671). Of patients in whom CV data were available, 86.0% (n = 757) had a CV/eGFR < 3.6 and 14.0% (n = 123) had a CV/eGFR ≥ 3.6. In multivariate logistic regression analysis, CV/eGFR ≥ 3.6 was the strongest predictor of 30-day all-cause mortality (odds ratio 5.06, 95% confidence interval [1.61-15.7], p = 0.004). Other independent predictors were procedural urgency (3.28 [1.04-10.3], p = 0.038) and being under general anaesthesia (4.81 [1.10-17.3], p = 0.023). CV/eGFR ≥ 3.6 was also independently associated with significantly increased odds of AKI (2.28 [1.20-4.17], p = 0.009) alongside significant non-left main stem coronary artery disease (2.56 [1.45-4.66], p = 0.001), and diabetes (1.82 [1.03-3.19], p = 0.037). In supplementary ROC curve analysis, a similar CV/eGFR cut point of 3.6 was found to be an excellent predictor for new RRT (AUC 0.833). Conclusions: In conclusion, a CV/eGFR ≥ 3.6 post-TAVI was found to be a strong predictor of 30-day mortality and AKI. The maximum contrast volume that can be safely administered in each patient without significantly increasing the risk of mortality and AKI can be calculated using this ratio.

The every woman study™ low- and middle-income countries edition protocol: A multi-country observational study to assess opportunities and challenges to improving survival and quality of life for women with ovarian cancer.

BACKGROUND: Ovarian cancer is a challenging disease to diagnose and treat effectively with five-year survival rates below 50%. Previous patient experience research in high-income countries highlighted common challenges and opportunities to improve survival and quality of life for women affected by ovarian cancer. However, no comparable data exist for low-and middle-income countries, where 70% of women with the disease live. This study aims to address this evidence gap. METHODS: This is an observational multi-country study set in low- and middle-income countries. We aim to recruit over 2000 women diagnosed with ovarian cancer across multiple hospitals in 24 countries in Asia, Africa and South America. Country sample sizes have been calculated (n = 70-96 participants /country), taking account of varying national five-year disease prevalence rates. Women within five years of their diagnosis, who are in contact with participating hospitals, are invited to take part in the study. A questionnaire has been adapted from a tool previously used in high-income countries. It comprises 57 multiple choice and two open-ended questions designed to collect information on demographics, women's knowledge of ovarian cancer, route to diagnosis, access to treatments, surgery and genetic testing, support needs, the impact of the disease on women and their families, and their priorities for action. The questionnaire has been designed in English, translated into local languages and tested according to local ethics requirements. Questionnaires will be administered by a trained member of the clinical team. CONCLUSION: This study will inform further research, advocacy, and action in low- and middle-income countries based on tailored approaches to the national, regional and global challenges and opportunities. In addition, participating countries can choose to repeat the study to track progress and the protocol can be adapted for other countries and other diseases.

Unraveling varying spatiotemporal patterns of dengue and associated exposure-response relationships with environmental variables in Southeast Asian countries before and during COVID-19.

The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g. plant shutdowns lead to lower greenhouse gas emissions), influences the dengue vector. This provides a unique opportunity to investigate the impact of COVID-19 on dengue transmission and generate insights to guide more targeted prevention measures. We aim to compare dengue transmission patterns and the exposure-response relationship of environmental variables and dengue incidence in the pre- and during-COVID-19 to identify variations and assess the impact of COVID-19 on dengue transmission. We initially visualized the overall trend of dengue transmission from 2012-2022, then conducted two quantitative analyses to compare dengue transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess dengue seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and dengue incidence. We observed that all subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic and seasonal patterns of dengue remained consistent pre- and during-COVID-19. Monthly dengue incidence in three countries varied significantly. Singapore witnessed a notable surge during-COVID-19, particularly from May to August, with cases multiplying several times compared to pre-COVID-19, while seasonality of Malaysia weakened. Exposure-response relationships of dengue and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-dengue relationship rose from about 3 to 17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 5 to 55. Our study is the first to compare dengue transmission patterns and their relationship with environmental variables before and during COVID-19, showing that COVID-19 has affected dengue transmission at both the national and regional level, and has altered the exposure-response relationship between dengue and the environment.

Inhibition of IGF2BP1 attenuates the progression of endometriosis through PTBP1.

INTRODUCTION: Endometriosis (EMs), manifested by pain and infertility, is a chronic inflammatory disease. The precise pathophysiology of this disease remains uncertain. Insulin-like growth factor-2 mRNA-binding protein 1 (IGF2BP1) and polypyrimidine tract-binding protein 1 (PTBP1) have both been found to regulate proliferation, apoptosis, and invasion. This study aimed to investigate the effects of IGF2BP1/PTBP1 in treating EMs. MATERIALS AND METHODS: qRT-PCR and western blotting were employed to quantify IGF2BP1 and PTBP1 expression in six patients with EMs (mean age 33.83 years). The correlation analysis, STRING database prediction, and RNA immunoprecipitation were utilized to identify the relationship between IGF2BP1 and PTBP1. Ectopic endometrial volume, weight, HE staining, and IGF2BP1 silencing were utilized to estimate the effects of IGF2BP1 in EMs model rats. qRT-PCR, CCK-8, 5-ethynyl-2'-deoxyuridine (EDU) labeling, Transwell assay, and flow cytometry were utilized to assess the effects of IGF2BP1/PTBP1 on the proliferation, migration, invasion, and apoptosis of ectopic endometrial stromal cells (eESCs). Furthermore, western blotting was employed to evaluate expressions of PCNA, VEGF, and E-cadherin in EMs rats and eESCs. RESULTS: The mRNA and protein levels of IGF2BP1 and PTBP1 in the ectopic and eutopic endometrium of EMs patients were significantly increased. RNA immunoprecipitation revealed a close interaction of IGF2BP1 with PTBP1. Additionally, the endometrial volume, weight, and histopathologic scores in rats were significantly reduced after IGF2BP1 silencing. IGF2BP1 silencing also decreased the expression of PCNA and VEGF, and increased E-cadherin expression in endometrial tissues of EMs rats. Moreover, IGF2BP1 silencing inhibited proliferation, migration, and invasion and promoted apoptosis through PTBP1 in eESCs. CONCLUSIONS: IGF2BP1 exhibits potential beneficial properties in the management of EMs by interacting with PTBP1, thereby highlighting IGF2BP1 as a promising therapeutic target for EMs.

Calculating Volume of Pig Point Cloud Based on Improved Poisson Reconstruction.

Pig point cloud data can be used to digitally reconstruct surface features, calculate pig body volume and estimate pig body weight. Volume, as a pig novel phenotype feature, has the following functions: (a) It can be used to estimate livestock weight based on its high correlation with body weight. (b) The volume proportion of various body parts (such as head, legs, etc.) can be obtained through point cloud segmentation, and the new phenotype information can be utilized for breeding pigs with smaller head volumes and stouter legs. However, as the pig point cloud has an irregular shape and may be partially missing, it is difficult to form a closed loop surface for volume calculation. Considering the better water tightness of Poisson reconstruction, this article adopts an improved Poisson reconstruction algorithm to reconstruct pig body point clouds, making the reconstruction results smoother, more continuous, and more complete. In the present study, standard shape point clouds, a known-volume Stanford rabbit standard model, a measured volume piglet model, and 479 sets of pig point cloud data with known body weight were adopted to confirm the accuracy and reliability of the improved Poisson reconstruction and volume calculation algorithm. Among them, the relative error was 4% in the piglet model volume result. The average absolute error was 2.664 kg in the weight estimation obtained from pig volume by collecting pig point clouds, and the average relative error was 2.478%. Concurrently, it was determined that the correlation coefficient between pig body volume and pig body weight was 0.95.

Fatigue Crack Growth Rates and Crack Tip Opening Loads in CT Specimens Made of SDSS and Manufactured Using WAAM.

Additive manufacturing offers greater flexibility in the design and fabrication of structural components with complex shapes. However, the use of additively manufactured parts for load-bearing structural applications, specifically involving cyclic loading, requires a thorough investigation of material fatigue properties. These properties can be affected by many factors, including residual stresses and crack tip shielding mechanisms, which can be very different from those of conventionally manufactured materials. This research focuses on super duplex stainless steels (SDSSs) fabricated with wire arc additive manufacturing (WAAM) and investigates their fatigue crack growth rates and the net effect of crack tip shielding mechanisms. Using the compliance-based method, we measured crack tip opening loads in compact tension (CT) specimens with cracks propagating longitudinally and transversely to the WAAM deposition direction. It was found that fatigue crack growth rates were very similar in both directions when correlated by the effective stress intensity factor range. However, the differences in crack tip opening loads explain a quite significant influence of the deposition direction on the fatigue life.

SF3A2 promotes progression and cisplatin resistance in triple-negative breast cancer via alternative splicing of MKRN1.

Triple-negative breast cancer (TNBC) is the deadliest subtype of breast cancer owing to the lack of effective therapeutic targets. Splicing factor 3a subunit 2 (SF3A2), a poorly defined splicing factor, was notably elevated in TNBC tissues and promoted TNBC progression, as confirmed by cell proliferation, colony formation, transwell migration, and invasion assays. Mechanistic investigations revealed that E3 ubiquitin-protein ligase UBR5 promoted the ubiquitination-dependent degradation of SF3A2, which in turn regulated UBR5, thus forming a feedback loop to balance these two oncoproteins. Moreover, SF3A2 accelerated TNBC progression by, at least in part, specifically regulating the alternative splicing of makorin ring finger protein 1 (MKRN1) and promoting the expression of the dominant and oncogenic isoform, MKRN1-T1. Furthermore, SF3A2 participated in the regulation of both extrinsic and intrinsic apoptosis, leading to cisplatin resistance in TNBC cells. Collectively, these findings reveal a previously unknown role of SF3A2 in TNBC progression and cisplatin resistance, highlighting SF3A2 as a potential therapeutic target for patients with TNBC.

A deep learning-based approach for fully automated segmentation and quantitative analysis of muscle fibers in pig skeletal muscle.

Muscle fiber properties exert a significant influence on pork quality, with cross-sectional area (CSA) being a crucial parameter closely associated with various meat quality indicators, such as shear force. Effectively identifying and segmenting muscle fibers in a robust manner constitutes a vital initial step in determining CSA. This step is highly intricate and time-consuming, necessitating an accurate and automated analytical approach. One limitation of existing methods is their tendency to perform well on high signal-to-noise ratio images of intact, healthy muscle fibers but their lack of validation on more complex image datasets featuring significant morphological changes, such as the presence of ice crystals. In this study, we undertake the fully automatic segmentation of muscle fiber microscopic images stained with myosin adenosine triphosphate (mATPase) activity using a deep learning architecture known as SOLOv2. Our objective is to efficiently derive accurate measurements of muscle fiber size and distribution. Tests conducted on actual images demonstrate that our method adeptly handles the intricate task of muscle fiber segmentation, yielding quantitative results amenable to statistical analysis and displaying reliability comparable to manual analysis.

A radical 1,4-aryl migration enables nickel-catalysed remote cross-electrophile coupling of ß-bromo amino acid esters with vinyl triflates.

A radical 1,4-aryl migration enabling a cross-electrophile coupling reaction toward remote transalkylation of N-benzyl alanine has been developed. In this strategy, with the occurrence of a radical-mediated Turce-Smiles rearrangement, key α-aminoalkyl radicals are generated. The as-formed α-aminoalkyl radical serves as a robust coupling partner for cross-electrophilic coupling with vinyl triflates, affording a series of olefin-tethered amino acid motifs.

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites.

BACKGROUND: Diabetic kidney disease (DKD) and diabetic retinopathy (DR) are major diabetic microvascular complications, contributing significantly to morbidity, disability, and mortality worldwide. The kidney and the eye, having similar microvascular structures and physiological and pathogenic features, may experience similar metabolic changes in diabetes. OBJECTIVE: This study aimed to use machine learning (ML) methods integrated with metabolic data to identify biomarkers associated with DKD and DR in a multiethnic Asian population with diabetes, as well as to improve the performance of DKD and DR detection models beyond traditional risk factors. METHODS: We used ML algorithms (logistic regression [LR] with Least Absolute Shrinkage and Selection Operator and gradient-boosting decision tree) to analyze 2772 adults with diabetes from the Singapore Epidemiology of Eye Diseases study, a population-based cross-sectional study conducted in Singapore (2004-2011). From 220 circulating metabolites and 19 risk factors, we selected the most important variables associated with DKD (defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2) and DR (defined as an Early Treatment Diabetic Retinopathy Study severity level ≥20). DKD and DR detection models were developed based on the variable selection results and externally validated on a sample of 5843 participants with diabetes from the UK biobank (2007-2010). Machine-learned model performance (area under the receiver operating characteristic curve [AUC] with 95% CI, sensitivity, and specificity) was compared to that of traditional LR adjusted for age, sex, diabetes duration, hemoglobin A1c, systolic blood pressure, and BMI. RESULTS: Singapore Epidemiology of Eye Diseases participants had a median age of 61.7 (IQR 53.5-69.4) years, with 49.1% (1361/2772) being women, 20.2% (555/2753) having DKD, and 25.4% (685/2693) having DR. UK biobank participants had a median age of 61.0 (IQR 55.0-65.0) years, with 35.8% (2090/5843) being women, 6.7% (374/5570) having DKD, and 6.1% (355/5843) having DR. The ML algorithms identified diabetes duration, insulin usage, age, and tyrosine as the most important factors of both DKD and DR. DKD was additionally associated with cardiovascular disease history, antihypertensive medication use, and 3 metabolites (lactate, citrate, and cholesterol esters to total lipids ratio in intermediate-density lipoprotein), while DR was additionally associated with hemoglobin A1c, blood glucose, pulse pressure, and alanine. Machine-learned models for DKD and DR detection outperformed traditional LR models in both internal (AUC 0.838 vs 0.743 for DKD and 0.790 vs 0.764 for DR) and external validation (AUC 0.791 vs 0.691 for DKD and 0.778 vs 0.760 for DR). CONCLUSIONS: This study highlighted diabetes duration, insulin usage, age, and circulating tyrosine as important factors in detecting DKD and DR. The integration of ML with biomedical big data enables biomarker discovery and improves disease detection beyond traditional risk factors.

MOFs@POMs-derived bimetallic oxide Fe2(MoO4)3 nanoparticles for sensitive colorimetric detection of salicylic acid in aspirin.

A colorimetric sensing method for salicylic acid (SA) was developed by designing and fabricating bimetallic oxide nanozymes. Firstly, by calcinating MIL-100(Fe)@PMo12 (MOFs@POMs) at different temperature, Fe2(MoO4)3-Ts (T = 400℃, 500℃, 600℃, 700℃) nanoparticles (NPs) were successfully prepared. Secondly, by evaluating the peroxidase-like activities, Fe2(MoO4)3-600 NPs shows the best peroxidase-like activity attributed to the Fenton-like effect and the synergistic coupling interaction between Mo and Fe. Finally, based on the specific complexation between SA and Fe3+, a sensitive colorimetric sensor for SA was established, which exhibits superior selectivity and interference with a detection limit of 0.11 µM and a linear range of 10 to 100 µM, the lowest LOD for SA to date, to the best of our knowledge.

Genome-wide CRISPR screen reveals the synthetic lethality between BCL2L1 inhibition and radiotherapy.

Radiation therapy (RT) is one of the most commonly used anticancer therapies. However, the landscape of cellular response to irradiation, especially to a single high-dose irradiation, remains largely unknown. In this study, we performed a whole-genome CRISPR loss-of-function screen and revealed temporal inherent and acquired responses to RT. Specifically, we found that loss of the IL1R1 pathway led to cellular resistance to RT. This is in part because of the involvement of radiation-induced IL1R1-dependent transcriptional regulation, which relies on the NF-κB pathway. Moreover, the mitochondrial anti-apoptotic pathway, particularly the BCL2L1 gene, is crucially important for cell survival after radiation. BCL2L1 inhibition combined with RT dramatically impeded tumor growth in several breast cancer cell lines and syngeneic models. Taken together, our results suggest that the combination of an apoptosis inhibitor such as a BCL2L1 inhibitor with RT may represent a promising anticancer strategy for solid cancers including breast cancer.

Generating synthetic population for simulating the spatiotemporal dynamics of epidemics.

Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. The fidelity of agent-based models in replicating real-world epidemic scenarios hinges on the accurate portrayal of both population-wide and individual-level interactions. In situations where comprehensive population data are lacking, synthetic populations serve as a vital input to agent-based models, approximating real-world demographic structures. While some current population synthesizers consider the structural relationships among agents from the same household, there remains room for refinement in this domain, which could potentially introduce biases in subsequent disease transmission simulations. In response, this study unveils a novel methodology for generating synthetic populations tailored for infectious disease transmission simulations. By integrating insights from microsample-derived household structures, we employ a heuristic combinatorial optimizer to recalibrate these structures, subsequently yielding synthetic populations that faithfully represent agent structural relationships. Implementing this technique, we successfully generated a spatially-explicit synthetic population encompassing over 17 million agents for Shenzhen, China. The findings affirm the method's efficacy in delineating the inherent statistical structural relationship patterns, aligning well with demographic benchmarks at both city and subzone tiers. Moreover, when assessed against a stochastic agent-based Susceptible-Exposed-Infectious-Recovered model, our results pinpointed that variations in population synthesizers can notably alter epidemic projections, influencing both the peak incidence rate and its onset.