Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine Learning Algorithms: Case-Control Study.

BACKGROUND: Acute kidney injury (AKI) is a common adverse outcome following nephrectomy. The progression from AKI to acute kidney disease (AKD) and subsequently to chronic kidney disease (CKD) remains a concern; yet, the predictive mechanisms for these transitions are not fully understood. Interpretable machine learning (ML) models offer insights into how clinical features influence long-term renal function outcomes after nephrectomy, providing a more precise framework for identifying patients at risk and supporting improved clinical decision-making processes. OBJECTIVE: This study aimed to (1) evaluate postnephrectomy rates of AKI, AKD, and CKD, analyzing long-term renal outcomes along different trajectories; (2) interpret AKD and CKD models using Shapley Additive Explanations values and Local Interpretable Model-Agnostic Explanations algorithm; and (3) develop a web-based tool for estimating AKD or CKD risk after nephrectomy. METHODS: We conducted a retrospective cohort study involving patients who underwent nephrectomy between July 2012 and June 2019. Patient data were randomly split into training, validation, and test sets, maintaining a ratio of 76.5:8.5:15. Eight ML algorithms were used to construct predictive models for postoperative AKD and CKD. The performance of the best-performing models was assessed using various metrics. We used various Shapley Additive Explanations plots and Local Interpretable Model-Agnostic Explanations bar plots to interpret the model and generated directed acyclic graphs to explore the potential causal relationships between features. Additionally, we developed a web-based prediction tool using the top 10 features for AKD prediction and the top 5 features for CKD prediction. RESULTS: The study cohort comprised 1559 patients. Incidence rates for AKI, AKD, and CKD were 21.7% (n=330), 15.3% (n=238), and 10.6% (n=165), respectively. Among the evaluated ML models, the Light Gradient-Boosting Machine (LightGBM) model demonstrated superior performance, with an area under the receiver operating characteristic curve of 0.97 for AKD prediction and 0.96 for CKD prediction. Performance metrics and plots highlighted the model's competence in discrimination, calibration, and clinical applicability. Operative duration, hemoglobin, blood loss, urine protein, and hematocrit were identified as the top 5 features associated with predicted AKD. Baseline estimated glomerular filtration rate, pathology, trajectories of renal function, age, and total bilirubin were the top 5 features associated with predicted CKD. Additionally, we developed a web application using the LightGBM model to estimate AKD and CKD risks. CONCLUSIONS: An interpretable ML model effectively elucidated its decision-making process in identifying patients at risk of AKD and CKD following nephrectomy by enumerating critical features. The web-based calculator, found on the LightGBM model, can assist in formulating more personalized and evidence-based clinical strategies.

Efficacy and Safety of Crisugabalin (HSK16149) in Adults with Postherpetic Neuralgia: A Phase 3 Randomized Clinical Trial.

Importance: China carries a heavy burden of postherpetic neuralgia, with an unmet need for novel drugs with greater efficacy and less prominent neurotoxic effects than existing calcium channel ligands. Objective: To investigate the efficacy and safety of crisugabalin, an oral calcium channel α2δ-1 subunit ligand, for postherpetic neuralgia. Design, Setting, and Participants: This randomized clinical trial, carried out between November 9, 2021, and January 5, 2023, at 48 tertiary care centers across China had 2 parts. Part 1 was a phase 3, multicenter, randomized, double-blind, placebo-controlled, parallel-group study consisting of a 2-week screening period, a 7-day run-in period, and a 12-week double-blind treatment period. Part 2 was a 14-week open-label extension study. Investigators, statisticians, trial clinicians, and patients were blinded to trial group assignments. Participants included adults with postherpetic neuralgia with an average daily pain score (ADPS) of at least 4 on the 11-point Numeric Pain Rating Scale over the preceding week, with the exclusion of patients with pain not controlled by prior therapy with pregabalin (≥300 mg/d) or gabapentin (≥1200 mg/d). Interventions: Patients were randomized 1:1:1 to receive crisugabalin, 20 mg twice daily (ie, 40 mg/d), and crisugabalin, 40 mg twice daily (ie, 80 mg/d), or placebo for 12 weeks. Eligible patients received crisugabalin, 40 mg, twice daily during extension. Main Outcome and Measure: The primary efficacy end point was the change from baseline in ADPS at week 12. Results: The study enrolled 366 patients (121 patients receiving crisugabalin, 40 mg/d; 121 patients receiving crisugabalin, 80 mg/d; 124 patients receiving placebo; median [IQR] age, 63.0 [56.0-69.0] years; 193 men [52.7%]). At week 12, the least squares mean (SD) change from baseline in ADPS was -2.2 (0.2) for crisugabalin, 40 mg/d, and -2.6 (0.2) for crisugabalin, 80 mg/d, vs -1.1 (0.2) for placebo, with a least squares mean difference of -1.1 (95% CI, -1.6 to -0.7; P < .001) and -1.5 (-95% CI, -2.0 to -1.0; P < .001) vs placebo, respectively. No new safety concerns emerged. Conclusions and Relevance: Crisugabalin, 40 mg/d, or crisugabalin, 80 mg/d, was well tolerated and demonstrated a statistically significant improvement in ADPS over placebo. Trial Registration: ClinicalTrials.gov Identifier: NCT05140863.

Design and optimization of pore structure in three-dimensional micro-nano hierarchical SnOx supercapacitor electrodes for enhanced ion diffusion.

This paper introduced a novel continuous electrochemical synthesis strategy to address the challenges of slow ion/electron transport rates and low electrode reaction efficiency in Sn-based electrode materials. This approach leveraged the induction and confinement of bubble templates to assist atoms deposition, generating micron-sized tin skeletons. Subsequently, these skeletons were transformed into a secondary nanoporous structure through dissolution-deposition etching effects. From liquid-phase ions to metal skeletons to porous oxides, the sequential material transformations realized the innovative design of three-dimensional (3D) hierarchical structures. This strategy ingeniously exploited the diffusion advantages of the electrolyte in the micro-nano hierarchical structure to achieve the diffusion enhancement of ions, thus solving the "dead surface" problem in the energy storage process. This study revealed the thermodynamic and kinetic feasibility of the constructed 3D micro-nano hierarchical structure through electrochemical evaluations and theoretical calculations, and elucidated the constitutive relationship in which the electrochemical performance of the electrode materials was enhanced with decreasing pore size. In addition, design optimization of pore structures and modelling exploration of pore size limit values were conducted based on density functional theory (DFT) simulations. These simulations demonstrated the advantages of hierarchical structures with controllable pore sizes in facilitating electrolyte ion diffusion, predicting an optimal pore size of 55 µm for 3D hierarchical porous SnOx electrodes. The integration of this innovative structural design with simulation insights offered significant implications for enhancing the sluggish electrode reaction kinetics of metal oxide electrode materials, advancing the controllable fabrication of high-performance energy storage devices.

Electroacupuncture reduces corpus callosum injury in rats with permanent cerebral ischemia by inhibiting the activation of high-mobility group box 1 protein and the receptor for advanced glycation end products.

Previous studies have shown that cerebral ischemia can cause white matter injury in the brain. This study aimed to investigate the potential mechanism of electroacupuncture (EA) at the Baihui (GV20) and Zusanli (ST36) acupoints in protecting white matter. Sprague-Dawley rats were used to establish permanent middle cerebral artery occlusion (pMCAO) rat models. Comprehensive motor functions were assessed using the mesh experiment. Morphological changes in the myelin sheath were assessed with Luxol fast blue staining. Morphological changes in oligodendrocytes and myelinated axons were evaluated using Nissl staining. The expressions of high-mobility group box 1 protein (HMGB1) and the receptor for advanced glycation end products (RAGE) in the corpus callosum were detected by immunohistochemical staining and Western blot analysis. pMCAO caused severe injury to the corpus callosum, evidenced by significant loss of white matter fibers and myelinated axons, and induced overexpression of HMGB1 and RAGE in the corpus callosum. EA treatment significantly improved comprehensive motor function alleviated white matter damage, and downregulated the expression of HMGB1 and RAGE. Its effects were comparable to those of FPS-ZM1, a RAGE receptor inhibitor. In conclusion, EA effectively improves comprehensive motor function in rats with cerebral infarction and alleviates corpus callosum injury. This effect may be related to the inhibition of HMGB1 and RAGE overexpression.

Formononetin Alleviates Ischemic Acute Kidney Injury by Regulating Macrophage Polarization through KLF6/STAT3 Pathway.

Recent research has indicated that formononetin demonstrates a potent anti-inflammatory effect in various diseases. However, its impact on sterile inflammation kidney injury, specifically acute kidney injury (AKI), remains unclear. In this study, we utilized an ischemia/reperfusion-induced AKI (IRI-AKI) mouse model and bone marrow-derived macrophages (BMDMs) to investigate the effects of formononetin on sterile inflammation of AKI and to explore the underlying mechanism. The administration of formononetin significantly preserved kidney function from injury, as evidenced by lower serum creatinine and blood urea nitrogen levels compared to IRI-AKI mice without treatment. This was further confirmed by less pathological changes in renal tubules and low expression of tubular injury markers such as KIM-1 and NGAL in the formononetin-treated IRI-AKI group. Furthermore, formononetin effectively suppressed the expression of pro-inflammatory cytokines (MCP-1, TNF-[Formula: see text], and IL-1[Formula: see text]) and macrophage infiltration into the kidneys of AKI mice. In vitro studies showed that formononetin led to less macrophage polarization towards a pro-inflammatory phenotype in BMDMs stimulated by LPS and IFN-[Formula: see text]. The mechanism involved the KLF6 and p-STAT3 pathway, as overexpression of KLF6 restored pro-inflammatory cytokine levels and pro-inflammatory polarization. Our findings demonstrate that formononetin can significantly improve renal function and reduce inflammation in IRI-AKI, which may be attributed to the inhibition of KLF6/STAT3-mediated macrophage pro-inflammatory polarization. This discovery presents a new promising therapeutic option for the treatment of IRI-AKI.

Inhibition of complement factor C5a or C5aR for cholesterol crystal embolism-related vascular thrombosis with microvascular injury and its consequences.

Cholesterol crystal embolism (CCE) implies immunothrombosis, tissue necrosis, and organ failure but no specific treatments are available. As CCE involves complement activation, we speculated that inhibitors of the C5a/C5aR axis would be sufficient to attenuate the consequences of CCE like that with systemic vasculitis. Cholesterol microcrystal injection into the kidney artery of wild-type mice initiated intra-kidney immunothrombosis within a few hours followed by a sudden drop of glomerular filtration rate and ischemic kidney necrosis after 24 hours. Genetic deficiency of either C3 or C5aR prevented immunothrombosis, glomerular filtration rate drop, and ischemic necrosis at 24 hours as did preemptive treatment with inhibitors of either C5a or C5aR. Delayed C5a blockade after crystal injection still resolved crystal clots and prevented all consequences. Thus, selective blockade of C5a or C5aR is sufficient to attenuate the consequences of established CCE and prospective inhibition in high-risk patients may be clinically feasible and safe.

Unmasking Protein Phosphatase 2A Regulatory Subunit B as a Crucial Factor in the Progression of Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) is one of the major causes of heart failure. Although significant progress has been made in elucidating the underlying mechanisms, further investigation is required for clarifying molecular diagnostic and therapeutic targets. In this study, we found that the mRNA level of protein phosphatase 2 regulatory subunit B' delta (Ppp2r5d) was altered in the peripheral blood plasma of DCM patients. Knockdown of Ppp2r5d in murine cardiomyocytes increased the intracellular levels of reactive oxygen species (ROS) and inhibited adenosine triphosphate (ATP) synthesis. In vivo knockdown of Ppp2r5d in an isoproterenol (ISO)-induced DCM mouse model aggravated the pathogenesis and ultimately led to heart failure. Mechanistically, Ppp2r5d-deficient cardiomyocytes showed an increase in phosphorylation of STAT3 at Y705 and a decrease in phosphorylation of STAT3 at S727. The elevated levels of phosphorylation at Y705 in STAT3 triggered the upregulation of interleukin 6 (IL6) expression. Moreover, the decreased phosphorylation at S727 in STAT3 disrupted mitochondrial electron transport chain function and dysregulated ATP synthesis and ROS levels. These results hereby reveal a novel role for Ppp2r5d in modulating STAT3 pathway in DCM, suggesting it as a potential target for the therapy of the disease.

Diffusion Barrier Performance of Ni-W Layer at Sn/Cu Interfacial Reaction.

As the integration of chips in 3D integrated circuits (ICs) increases and the size of micro-bumps reduces, issues with the reliability of service due to electromigration and thermomigration are becoming more prevalent. In the practical application of solder joints, an increase in the grain size of intermetallic compounds (IMCs) has been observed during the reflow process. This phenomenon results in an increased thickness of the IMC layer, accompanied by a proportional increase in the volume of the IMC layer within the joint. The brittle nature of IMC renders it susceptible to excessive growth in small-sized joints, which has the potential to negatively impact the reliability of the welded joint. It is therefore of the utmost importance to regulate the formation and growth of IMCs. The following paper presents the electrodeposition of a Ni-W layer on a Cu substrate, forming a barrier layer. Subsequently, the barrier properties between the Sn/Cu reactive couples were subjected to a comprehensive and systematic investigation. The study indicates that the Ni-W layer has the capacity to impede the diffusion of Sn atoms into Cu. Furthermore, the Ni-W layer is a viable diffusion barrier at the Sn/Cu interface. The "bright layer" Ni2WSn4 can be observed in all Ni-W coatings during the soldering reflow process, and its growth was almost linear. The structure of the Ni-W layer is such that it reduces the barrier properties that would otherwise be inherent to it. This is due to the "bright layer" Ni2WSn4 that covers the original Ni-W barrier layer. At a temperature of 300 °C for a duration of 600 s, the Ni-W barrier layer loses its blocking function. Once the "bright layer" Ni2WSn4 has completely covered the original Ni-W barrier layer, the diffusion activation energy for Sn diffusion into the Cu substrate side will be significantly reduced, particularly in areas where the distortion energy is concentrated due to electroplating tension. Both the "bright layer" Ni2WSn4 and Sn will grow rapidly, with the formation of Cu-Sn intermetallic compounds (IMCs). At temperatures of 250 °C, the growth of Ni3Sn4-based IMCs is controlled by grain boundaries. Conversely, the growth of the Ni2WSn4 layer (consumption of Ni-W layer) is influenced by a combination of grain boundary diffusion and bulk diffusion. At temperatures of 275 °C and 300 °C, the growth of Ni3Sn4-based IMCs and the Ni2WSn4 layer (consumption of Ni-W layer) are both controlled by grain boundaries. The findings of this study can inform the theoretical design of solder joints with barrier layers as well as the selection of Ni-W diffusion barrier layers for use in different soldering processes. This can, in turn, enhance the reliability of microelectronic devices, offering significant theoretical and practical value.

High-Performance Memristors Based on Imine-Linked Covalent Organic Frameworks Obtained by Using a Protonation Modification Strategy.

Imine-linked covalent organic frameworks (COFs) are garnering substantial interest in resistive random-access memory, attributed to their superior crystallinity, excellent chemical and thermal stability, and modifiable molecular structures. However, the development of high-performance COF-based memristors impeded by challenges such as low conjugation degree of imine bonds and poor electron delocalization ability. Herein, we report a protonation strategy to modify the imine bonds of donor-acceptor (D-A) type COFs. This modification significantly enhances the electron delocalization capability of imine bonds, lowers the energy barriers for electron injection from electrodes, and stabilizes the conductive charge transfer state, thus markedly improving device performance. The protonated COF-BTT-BPy and COF-BTT-TAPT thin films-based memristors show remarkable device performance with a high ON/OFF current ratio of 105, a low driving voltage, and outstanding endurance exceeding 600 and 1300 cycles, respectively, which is nearly twice the durability of analogous non-protonated COFs-based memristors. Notably, the protonated COF-BTT-TAPT-based memristor exhibit the highest number of cycles reported at present. This work not only unprecedentedly enhances the performance of COF-based memristors, but also provides a universal and promising approach for the molecular design and potential application of D-A type imine-linked COFs.

Removing information from working memory with a delay: Effective but not beneficial.

Ideally, removing outdated information from working memory (WM) should have two consequences: The removed content should be less accessible (removal costs), and other WM content should benefit from the freeing up of WM capacity (removal benefits). Robust removal benefits and removal costs have been demonstrated when people are told to forget items shortly after they were encoded (immediate removal). However, other studies suggest that people might be unable to selectively remove items from an already encoded set of items (delayed removal). In two experiments (n = 219; n = 241), we investigated the effectiveness and consequences of delayed removal by combining a modified version of Ecker's et al. (Journal of Memory and Language, 74, 77-90, 2014) letter updating task with a directed-forgetting in WM paradigm. We found that while delayed removal resulted in reduced memory for the to-be-forgotten item-location relations (removal costs), it failed to enhance performance for existing WM content. This contrasts sharply with immediate removal, where removal benefits can be observed. A fine-grained analysis of removal benefits shows that removal from WM proactively facilitates the subsequent encoding of new information but does not retroactively aid stored WM content.

Pelvic congestion syndrome analysis through quantitative 2-dimensional phase-contrast magnetic resonance imaging: A promising vision from an observational cohort study.

BACKGROUND: To examine the application of quantitative 2-dimensional phase-contrast magnetic resonance imaging (2D PC-MRI) for treating patients with pelvic congestion syndrome (PCS). MATERIALS AND METHODS: We conducted a retrospective cross-sectional analysis by using quantitative 2D PC-MRI data enrolled between April 2017 and Sep 2023. In addition, 32 healthy female controls (HCs) were included. RESULTS: Most patients with PCS presented with chronic pelvic pain and more than half had extra-pelvic venous symptoms (80/81, 98% and 45/81, 56%, respectively). Quantitative 2D PC-MRI analyzed the 81 patients with PCS, 239 patients without PCS, and 32 HCs. The patients with PCS had higher stroke volume (SV), absolute SV (ASV), and mean flux (MF) in the calf region (interstitial pixel shift) than did the HCs. In the left gonadal vein, the patients with PCS had higher SV, backward flow volume (BFV), ASV, and MF and lower forward flow volume (FFV), stroke distance (SD), and mean velocity (MV) than did the HCs. However, the patients with PCS had lower SV, FFV, MF, SD, and MV in the great saphenous veins. Quantitative 2D PC-MRI analysis revealed that the PCS group had higher SV, FFV, BFV, ASV, and MF in the calf region than did the non-PCS group. The variables that most strongly differentiated the patients with PCS from the HCs were SV in the great saphenous veins, SD in the great saphenous veins and left gonadal vein, and MV in the great saphenous veins and left gonadal vein. Caudal flow in the left gonadal vein was identified in half of the patients with PCS (39/81, 48.1%); 14 of them received embolization for left gonadal vein. CONCLUSIONS: In additional to providing an objective 3-dimensional morphology of the pelvic veins and extra-pelvic leaks, quantitative 2D PC-MRI analysis reveals distinct hemodynamic profiles between patients with PCS, those without PCS, and HCs, especially in the gonadal veins and regional perfusion of the calves.

Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients.

Introduction: Acute kidney injury (AKI) is a prevalent complication in older people, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models for predicting the occurrence of AKD, AKI and mortality in older patients. Methods: We retrospectively reviewed the medical records of older patients (aged 65 years and above). To explore the trajectory of kidney dysfunction, patients were categorized into four groups: no kidney disease, AKI recovery, AKD without AKI, or AKD with AKI. We developed eight machine learning models to predict AKD, AKI, and mortality. The best-performing model was identified based on the area under the receiver operating characteristic curve (AUC) and interpreted using the Shapley additive explanations (SHAP) method. Results: A total of 22,005 patients were finally included in our study. Among them, 4,434 patients (20.15%) developed AKD, 4,000 (18.18%) occurred AKI, and 866 (3.94%) patients deceased. Light gradient boosting machine (LGBM) outperformed in predicting AKD, AKI, and mortality, and the final lite models with 15 features had AUC values of 0.760, 0.767, and 0.927, respectively. The SHAP method revealed that AKI stage, albumin, lactate dehydrogenase, aspirin and coronary heart disease were the top 5 predictors of AKD. An online prediction website for AKD and mortality was developed based on the final models. Discussion: The LGBM models provide a valuable tool for early prediction of AKD, AKI, and mortality in older patients, facilitating timely interventions. This study highlights the potential of machine learning in improving older adult care, with the developed online tool offering practical utility for healthcare professionals. Further research should aim at external validation and integration of these models into clinical practice.

A cell-free fluorescence biosensor based on allosteric transcription factor NalC for detection of pentachlorophenol.

Pentachlorophenol (PCP) was once used as a pesticide, germicide, and preservative due to its stable properties and resistance to degradation. This study aimed to design a biosensor for the quantitative and prompt detection of capable of PCP. A cell-free fluorescence biosensor was developed while employing NalC, an allosteric Transcription Factor responsive to PCP and In Vitro Transcription. By adding a DNA template and PCP and employing Electrophoretic Mobility Shift Assay while monitoring the dynamic fluorescence changes in RNA, this study offers evidence of NalC's potential applicability in sensor systems developed for the specific detection of PCP. The biosensor showed the capability for the quantitative detection of PCP, with a Limit of Detection (LOD) of 0.21 µM. Following the addition of Nucleic Acid Sequence-Based Amplification, the fluorescence intensity of RNA revealed an excellent linear relationship with the concentration of PCP, showing a correlation coefficient (R2) of 0.9595. The final LOD was determined to be 0.002 µM. This study has successfully translated the determination of PCP into a fluorescent RNA output, thereby presenting a novel approach for detecting PCP within environmental settings.

Emergence of plasmid-borne tet(X4) resistance gene in clinical isolate of eravacycline- and omadacycline-resistant Klebsiella pneumoniae ST485.

Omadacycline and eravacycline are gradually being used as new tetracycline antibiotics for the clinical treatment of Gram-negative pathogens. Affected by various tetracycline-inactivating enzymes, there have been reports of resistance to eravacycline and omadacycline in recent years. We isolated a strain carrying the mobile tigecycline resistance gene tet(X4) from the feces of a patient in Zhejiang Province, China. The strain belongs to the rare ST485 sequence type. The isolate was identified as Klebsiella pneumoniae by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The MICs of antimicrobial agents were determined using either the agar dilution method or the micro broth dilution method. The result showed that the isolate was resistant to eravacycline (MIC = 32 mg/L), omadacycline (MIC > 64 mg/L), and tigecycline (MIC > 32 mg/L). Whole-genome sequencing revealed that the tet(X4) resistance gene is located on the IncFII(pCRY) conjugative plasmid. tet(X4) is flanked by ISVsa3, and we hypothesize that this association contributes to the spread of the resistance gene. Plasmids were analyzed by S1-nuclease pulsed-field gel electrophoresis (S1-PFGE), Southern blotting, and electrotransformation experiment. We successfully transferred the plasmid carrying tet(X4) to the recipient bacteria by electrotransformation experiment. Compared with the DH-5α, the MICs of the transformant L3995-DH5α were increased by eight-fold for eravacycline and two-fold higher for omadacycline. Overall, the emergence of plasmid-borne tet(X4) resistance gene in a clinical isolate of K. pneumoniae ST485 underscores the essential requirement for the ongoing monitoring of tet(X4) to prevent and control its further dissemination in China.IMPORTANCEThere are still limited reports on Klebsiella pneumoniae strains harboring tetracycline-resistant genes in China, and K. pneumoniae L3995hy adds a new example to those positive for the tet(X4) gene. Importantly, our study raises concerns that plasmid-mediated resistance to omadacycline and eravacycline may spread further to a variety of ecological and clinical pathogens, limiting the choice of medication for extensively drug-resistant bacterial infections. Therefore, it is important to continue to monitor the prevalence and spread of tet(X4) and other tetracyclines resistance genes in K. pneumoniae and diverse bacterial populations.

Combined deep learning and radiomics in pretreatment radiation esophagitis prediction for patients with esophageal cancer underwent volumetric modulated arc therapy.

PURPOSE: To develop a combined radiomics and deep learning (DL) model in predicting radiation esophagitis (RE) of a grade ≥ 2 for patients with esophageal cancer (EC) underwent volumetric modulated arc therapy (VMAT) based on computed tomography (CT) and radiation dose (RD) distribution images. MATERIALS AND METHODS: A total of 273 EC patients underwent VMAT were retrospectively reviewed and enrolled from two centers and divided into training (n = 152), internal validation (n = 66), and external validation (n = 55) cohorts, respectively. Radiomic and dosiomic features along with DL features using convolutional neural networks were extracted and screened from CT and RD images to predict RE. The performance of these models was evaluated and compared using the area under curve (AUC) of the receiver operating characteristic curves (ROC). RESULTS: There were 5 and 10 radiomic and dosiomic features were screened, respectively. XGBoost achieved a best AUC of 0.703, 0.694 and 0.801, 0.729 with radiomic and dosiomic features in the internal and external validation cohorts, respectively. ResNet34 achieved a best prediction AUC of 0.642, 0.657 and 0.762, 0.737 for radiomics based DL model (DLR) and RD based DL model (DLD) in the internal and external validation cohorts, respectively. Combined model of DLD + Dosiomics + clinical factors achieved a best AUC of 0.913, 0.821 and 0.805 in the training, internal, and external validation cohorts, respectively. CONCLUSION: Although the dose was not responsible for the prediction accuracy, the combination of various feature extraction methods was a factor in improving the RE prediction accuracy. Combining DLD with dosiomic features was promising in the pretreatment prediction of RE for EC patients underwent VMAT.

Diversity and inclusion: A hidden additional benefit of Open Data.

The recent imperative by the National Institutes of Health to share scientific data publicly underscores a significant shift in academic research. Effective as of January 2023, it emphasizes that transparency in data collection and dedicated efforts towards data sharing are prerequisites for translational research, from the lab to the bedside. Given the role of data access in mitigating potential bias in clinical models, we hypothesize that researchers who leverage open-access datasets rather than privately-owned ones are more diverse. In this brief report, we proposed to test this hypothesis in the transdisciplinary and expanding field of artificial intelligence (AI) for critical care. Specifically, we compared the diversity among authors of publications leveraging open datasets, such as the commonly used MIMIC and eICU databases, with that among authors of publications relying exclusively on private datasets, unavailable to other research investigators (e.g., electronic health records from ICU patients accessible only to Mayo Clinic analysts). To measure the extent of author diversity, we characterized gender balance as well as the presence of researchers from low- and middle-income countries (LMIC) and minority-serving institutions (MSI) located in the United States (US). Our comparative analysis revealed a greater contribution of authors from LMICs and MSIs among researchers leveraging open critical care datasets (treatment group) than among those relying exclusively on private data resources (control group). The participation of women was similar between the two groups, albeit slightly larger in the former. Notably, although over 70% of all articles included at least one author inferred to be a woman, less than 25% had a woman as a first or last author. Importantly, we found that the proportion of authors from LMICs was substantially higher in the treatment than in the control group (10.1% vs. 6.2%, p<0.001), including as first and last authors. Moreover, we found that the proportion of US-based authors affiliated with a MSI was 1.5 times higher among articles in the treatment than in the control group, suggesting that open data resources attract a larger pool of participants from minority groups (8.6% vs. 5.6%, p<0.001). Thus, our study highlights the valuable contribution of the Open Data strategy to underrepresented groups, while also quantifying persisting gender gaps in academic and clinical research at the intersection of computer science and healthcare. In doing so, we hope our work points to the importance of extending open data practices in deliberate and systematic ways.

Identification of potential therapeutic target SPP1 and related RNA regulatory pathway in keloid based on bioinformatics analysis.

OBJECTIVE: To explore the complex mechanisms of keloid, new approaches have been developed by different strategies. However, conventional treatment did not significantly reduce the recurrence rate. This study aimed to identify new biomarkers and mechanisms for keloid progression through bioinformatics analyses. METHODS: In our study, microarray datasets for keloid were downloaded from the GEO database. Differentially expressed genes (DEGs) were identified by R software. Multiple bioinformatics tools were used to identify hub genes, and reverse predict upstream miRNAs and lncRNA molecules of target hub genes. Finally, the total RNA-sequencing technique and miRNA microarray were combined to validate the identified genes. RESULTS: Thirty-one DEGs were screened out and the upregulated hub gene SPP1 was finally identified, which was consistent with our RNA-sequencing analysis results and validation dataset. In addition, a ceRNA network of mRNA (SPP1)-miRNA (miR-181a-5p)-lncRNA (NEAT1, MALAT1, LINC00667, NORAD, XIST and MIR4458HG) was identified by the bioinformatics databases. The results of our miRNA microarray showed that miR-181a-5p was upregulated in keloid, also we found that the lncRNA NEAT1 could affect keloid progression by retrieving the relevant literature. CONCLUSIONS: We speculate that SPP1 is a potential candidate biomarker and therapeutic target for patients with keloid, and NEAT1/miR-181a-5p/SPP1 might be the RNA regulatory pathway that regulates keloid formation.

Identify new biomarkers in keloid, potentially improve disease diagnosis and treatment.Through a variety of bioinformatics analysis tools, we found that the miRNA pathway NEAT1/miR-181a-5p/SPP1 may participant in controlling disease progression in the keloid.Providing insight into the mechanisms of disease development in the keloid at the transcriptome level.