A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations.

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations.

PURPOSE OF REVIEW: Patient-reported outcome measures (PROM) play a critical role in evaluating the success of treatment interventions for musculoskeletal conditions. However, predicting which patients will benefit from treatment interventions is complex and influenced by a multitude of factors. Artificial intelligence (AI) may better anticipate the propensity to achieve clinically meaningful outcomes through leveraging complex predictive analytics that allow for personalized medicine. This article provides a contemporary review of current applications of AI developed to predict clinically significant outcome (CSO) achievement after musculoskeletal treatment interventions. RECENT FINDINGS: The highest volume of literature exists in the subspecialties of total joint arthroplasty, spine, and sports medicine, with only three studies identified in the remaining orthopedic subspecialties combined. Performance is widely variable across models, with most studies only reporting discrimination as a performance metric. Given the complexity inherent in predictive modeling for this task, including data availability, data handling, model architecture, and outcome selection, studies vary widely in their methodology and results. Importantly, the majority of studies have not been externally validated or demonstrate important methodological limitations, precluding their implementation into clinical settings. A substantial body of literature has accumulated demonstrating variable internal validity, limited scope, and low potential for clinical deployment. The majority of studies attempt to predict the MCID-the lowest bar of clinical achievement. Though a small proportion of models demonstrate promise and highlight the utility of AI, important methodological limitations need to be addressed moving forward to leverage AI-based applications for clinical deployment.

Malignant peripheral nerve sheath tumor in the kidney of a dog.

A 12-year-old castrated male poodle presented with vomiting and diarrhea. Ultrasonography and computed tomography revealed a protruding mass at the caudal pole of the left kidney. Grossly, the poorly circumscribed abnormal mass was 1.6 × 1.8 × 1.9 cm in size and had multifocal dark-red foci. Microscopically, it was composed of densely or loosely packed variable-sized short spindle or ovoid cells. These neoplastic cells showed high pleomorphism, mitotic figures, and invasive tendency to the adjacent tissue. Immunohistochemically, the neoplastic spindle cells expressed vimentin, S100, neuron-specific enolase, nerve growth factor receptor, and laminin. Therefore, the mass was diagnosed as a malignant peripheral nerve sheath tumor (MPNST). To our knowledge, this is the first report of primary renal MPNST in a dog.

Enhanced mono-aromatics production by the CH4-assisted pyrolysis of microalgae using Zn-based HZSM-5 catalysts.

This study presents the catalytic pyrolysis of microalgae, Chlorella vulgaris (C. vulgaris), using pure CH4 and H2-rich gas evolved from CH4 decomposition on three different HZSM-5 catalysts loaded with Zn, Ga, and Pt, aimed specifically at producing high-value mono-aromatics such as benzene, toluene, ethylbenzene, and xylene (BTEX). In comparison with that for the typical inert N2 environment, a pure CH4 environment increased the bio-oil yield from 32.4 wt% to 37.4 wt% probably due to hydrogen and methyl radical insertion in the bio-oil components. Furthermore, the addition of bimetals further increased bio-oil yield. For example, ZnPtHZ led to a bio-oil yield of 47.7 wt% in pure CH4. ZnGaHZ resulted in the maximum BTEX yield (6.68 wt%), which could be explained by CH4 activation, co-aromatization, and hydrodeoxygenation. The BTEX yield could be further increased to 7.62 wt% when pyrolysis was conducted in H2-rich gas evolved from CH4 decomposition over ZnGaHZ, as rates of aromatization and hydrodeoxygenation were relatively high under this condition. This study experimentally validated that the combination of ZnGaHZ and CH4 decomposition synergistically increases BTEX production using C. vulgaris.

Phase 1/2a Study of Rivoceranib, a Selective VEGFR-2 Angiogenesis Inhibitor, in Patients with Advanced Solid Tumors.

Purpose: To report the results from an early-phase study of rivoceranib, an oral tyrosine kinase inhibitor highly selective for vascular endothelial growth factor receptor 2, in patients with advanced solid tumors. Materials and Methods: In this open-label, single-arm, dose-escalating, multicenter three-part phase 1/2a trial, patients had advanced solid tumors refractory to conventional therapy. Part 1 evaluated the safety and pharmacokinetics of five ascending once-daily doses of rivoceranib from 81 mg to 685 mg. Part 2 evaluated the safety and antitumor activity of once-daily rivoceranib 685 mg. Part 3 was conducted later, due to lack of MTD determination in part 1, to evaluate the safety and preliminary efficacy of once-daily rivoceranib 805 mg in patients with unresectable or advanced gastric cancer. Results: A total of 61 patients were enrolled in parts 1 (n=25), 2 (n=30), and 3 (n=6). In parts 1 and 2, patients were white (45.5%) or Asian (54.5%), and 65.6% were male. The most common grade ≥3 adverse events (AEs) were hypertension (32.7%), hyponatremia (10.9%), and hypophosphatemia (10.9%). The overall response rate (ORR) was 15.2%. In part 3, dose-limiting toxicities occurred in 2 out of 6 patients: grade 3 febrile neutropenia decreased appetite, and fatigue. The ORR was 33%. Conclusion: The recommended phase 2 dose of rivoceranib was determined to be 685 mg once daily, which showed adequate efficacy with a manageable safety profile. (NCT01497704 and NCT02711969).

Comparative biochemical kinase activity analysis identifies rivoceranib as a highly selective VEGFR2 inhibitor.

Vascular endothelial growth factor receptor 2 (VEGFR2), a key regulator of tumor angiogenesis, is highly expressed across numerous tumor types and has been an attractive target for anti-cancer therapy. However, clinical application of available VEGFR2 inhibitors has been challenged by limited efficacy and a wide range of side effects, potentially due to inadequate selectivity for VEGFR2. Thus, development of potent VEGFR2 inhibitors with improved selectivity is needed. Rivoceranib is an orally administered tyrosine kinase inhibitor that potently and selectively targets VEGFR2. A comparative understanding of the potency and selectivity of rivoceranib and approved inhibitors of VEGFR2 is valuable to inform rationale for therapy selection in the clinic. Here, we performed biochemical analyses of the kinase activity of VEGFR2 and of a panel of 270 kinases to compare rivoceranib to 10 FDA-approved kinase inhibitors ("reference inhibitors") with known activity against VEGFR2. Rivoceranib demonstrated potency within the range of the reference inhibitors, with a VEGFR2 kinase inhibition IC50 value of 16 nM. However, analysis of residual kinase activity of the panel of 270 kinases showed that rivoceranib displayed greater selectivity for VEGFR2 compared with the reference inhibitors. Differences in selectivity among compounds within the observed range of potency of VEGFR2 kinase inhibition are clinically relevant, as toxicities associated with available VEGFR2 inhibitors are thought to be partly due to their effects against kinases other than VEGFR2. Together, this comparative biochemical analysis highlights the potential for rivoceranib to address clinical limitations associated with off-target effects of currently available VEGFR2 inhibitors.

Artificial intelligence for automated identification of total shoulder arthroplasty implants.

BACKGROUND: Accurate and rapid identification of implant manufacturer and model is critical in the evaluation and management of patients requiring revision total shoulder arthroplasty (TSA). Failure to correctly identify implant designs in these circumstances may lead to delay in care, unexpected intraoperative challenges, increased morbidity, and excess health care costs. Deep learning (DL) permits automated image processing and holds the potential to mitigate such challenges while improving the value of care rendered. The purpose of this study was to develop an automated DL algorithm to identify shoulder arthroplasty implants from plain radiographs. METHODS: A total of 3060 postoperative images from patients who underwent TSA between 2011 and 2021 performed by 26 fellowship-trained surgeons at 2 independent tertiary academic hospitals in the Pacific Northwest and Mid-Atlantic Northeast were included. A DL algorithm was trained using transfer learning and data augmentation to classify 22 different reverse TSA and anatomic TSA prostheses from 8 implant manufacturers. Images were split into training and testing cohorts (2448 training and 612 testing images). Optimized model performance was assessed using standardized metrics including the multiclass area under the receiver operating characteristic curve (AUROC) and compared with a reference standard of implant data from operative reports. RESULTS: The algorithm classified implants at a mean speed of 0.079 seconds (±0.002 seconds) per image. The optimized model discriminated between 8 manufacturers (22 unique implants) with AUROCs of 0.994-1.000, accuracy of 97.1%, and sensitivities between 0.80 and 1.00 on the independent testing set. In the subset of single-institution implant predictions, a DL model identified 6 specific implants with AUROCs of 0.999-1.000, accuracy of 99.4%, and sensitivity >0.97 for all implants. Saliency maps revealed key differentiating features across implant manufacturers and designs recognized by the algorithm for classification. CONCLUSION: A DL model demonstrated excellent accuracy in identifying 22 unique TSA implants from 8 manufacturers. This algorithm may provide a clinically meaningful adjunct in assisting with preoperative planning for the failed TSA and allows for scalable expansion with additional radiographic data and validation efforts.

An Interpretable Machine Learning Model for Predicting 10-Year Total Hip Arthroplasty Risk.

BACKGROUND: As the demand for total hip arthroplasty (THA) rises, a predictive model for THA risk may aid patients and clinicians in augmenting shared decision-making. We aimed to develop and validate a model predicting THA within 10 years in patients using demographic, clinical, and deep learning (DL)-automated radiographic measurements. METHODS: Patients enrolled in the osteoarthritis initiative were included. DL algorithms measuring osteoarthritis- and dysplasia-relevant parameters on baseline pelvis radiographs were developed. Demographic, clinical, and radiographic measurement variables were then used to train generalized additive models to predict THA within 10 years from baseline. A total of 4,796 patients were included [9,592 hips; 58% female; 230 THAs (2.4%)]. Model performance using 1) baseline demographic and clinical variables 2) radiographic variables, and 3) all variables was compared. RESULTS: Using 110 demographic and clinical variables, the model had a baseline area under the receiver operating curve (AUROC) of 0.68 and area under the precision recall curve (AUPRC) of 0.08. Using 26 DL-automated hip measurements, the AUROC was 0.77 and AUPRC was 0.22. Combining all variables, the model improved to an AUROC of 0.81 and AUPRC of 0.28. Three of the top five predictive features in the combined model were radiographic variables, including minimum joint space, along with hip pain and analgesic use. Partial dependency plots revealed predictive discontinuities for radiographic measurements consistent with literature thresholds of osteoarthritis progression and hip dysplasia. CONCLUSION: A machine learning model predicting 10-year THA performed more accurately with DL radiographic measurements. The model weighted predictive variables in concordance with clinical THA pathology assessments.

Complication Rate After Primary Total Hip Arthroplasty Using the Posterior Approach and Enabling Technology: A Consecutive Series of 2,888 Hips.

BACKGROUND: Total hip arthroplasty (THA) is a safe and effective procedure; however, complications such as dislocation, fracture, and infection still occur. It is still unclear whether the dislocation rate via the posterior approach (PA) is better, equal, or worse than the direct anterior approach. Our aim was to report the primary THA dislocation rate via the PA using enabling technology in a large consecutive series of patients. METHODS: A retrospective cohort of 2,888 primary THAs were reviewed at a single, high-volume, academic institution from January 2018 to September 2021. All patients underwent a THA by 4 fellowship-trained orthopaedic surgeons through the PA with enabling technology. Overall dislocation and readmission rates within 90 days and up to 3 years were analyzed. RESULTS: Of the 2,888 procedures, a total of 39 patients had complications related to the surgery during the 3-year follow-up period. There were 10 patients (0.35%) who experienced a dislocation, with half undergoing surgical revision. Of the 39 patients who experienced complications, 37 (1.3%) were readmitted and 2 underwent revision during their hospital stay. Postoperative periprosthetic fractures were the most common cause for readmission and reoperation at a rate of 0.52% and 0.52%, respectively. CONCLUSION: The dislocation rate of 0.35% is one of the lowest reported rates via the PA at a mean follow up of 2.1 years and is comparable to previously published rates using alternate approaches. Using contemporary THA with enabling technology, the PA is a reliable approach with respect to dislocation and complication rates after primary THA.

Targeted Fusogenic Liposomes for Effective Tumor Delivery and Penetration of Lipophilic Cargoes.

Nanoparticle-based drug delivery has been widely used for effective anticancer treatment. However, a key challenge restricting the efficacy of nanotherapeutics is limited tissue penetration within solid tumors. Here, we report a targeted fusogenic liposome (TFL) that can selectively deliver lipophilic cargo to the plasma membranes of tumor cells. TFL is prepared by directly attaching tumor-targeting peptides to the surface of FL instead of the cationic moieties. The lipophilic cargo loaded in the membrane of TFL is transferred to the plasma membranes of tumor cells and subsequently packaged in the extracellular vesicles (EVs) released by the cells. Systemically administered TFL accumulates in the perivascular region of tumors, where the lipophilic cargo is unloaded to the tumor cell membranes and distributed autonomously throughout the tumor tissue via extracellular vesicle-mediated intercellular transfer. When loaded with a lipophilic pro-apoptotic drug, thapsigargin (Tg), TFL significantly inhibits tumor growth in a mouse colorectal cancer model. Furthermore, the combination treatment with TFL (Tg) potentiates the antitumor efficacy of FDA-approved liposomal doxorubicin, whose therapeutic effect is limited to perivascular regions without significant toxicity.

A practical guide to the development and deployment of deep learning models for the orthopedic surgeon: part II.

Deep learning has the potential to be one of the most transformative technologies to impact orthopedic surgery. Substantial innovation in this area has occurred over the past 5 years, but clinically meaningful advancements remain limited by a disconnect between clinical and technical experts. That is, it is likely that few orthopedic surgeons possess both the clinical knowledge necessary to identify orthopedic problems, and the technical knowledge needed to implement deep learning-based solutions. To maximize the utilization of rapidly advancing technologies derived from deep learning models, orthopedic surgeons should understand the steps needed to design, organize, implement, and evaluate a deep learning project and its workflow. Equipping surgeons with this knowledge is the objective of this three-part editorial review. Part I described the processes involved in defining the problem, team building, data acquisition, curation, labeling, and establishing the ground truth. Building on that, this review (Part II) provides guidance on pre-processing and augmenting the data, making use of open-source libraries/toolkits, and selecting the required hardware to implement the pipeline. Special considerations regarding model training and evaluation unique to deep learning models relative to "shallow" machine learning models are also reviewed. Finally, guidance pertaining to the clinical deployment of deep learning models in the real world is provided. As in Part I, the focus is on applications of deep learning for computer vision and imaging.

Standardized Fixation Zones and Cone Assessments for Revision Total Knee Arthroplasty Using Deep Learning.

BACKGROUND: Achieving adequate implant fixation is critical to optimize survivorship and postoperative outcomes after revision total knee arthroplasty (rTKA). Three anatomical zones (ie, epiphysis, metaphysis, and diaphysis) have been proposed to assess fixation, but are not well-defined. The purpose of the study was to develop a deep learning workflow capable of automatically delineating rTKA zones and cone placements in a standardized way on postoperative radiographs. METHODS: A total of 235 patients who underwent rTKA were randomly partitioned (6:2:2 training, validation, and testing split), and a U-Net segmentation workflow was developed to delineate rTKA fixation zones and assess revision cone placement on anteroposterior radiographs. Algorithm performance for zone delineation and cone placement were compared against ground truths from a fellowship-trained arthroplasty surgeon using the dice segmentation coefficient and accuracy metrics. RESULTS: On the testing cohort, the algorithm defined zones in 98% of images (8 seconds/image) using anatomical landmarks. The dice segmentation coefficient between the model and surgeon was 0.89 ± 0.08 (interquartile range [IQR]:0.88-0.94) for femoral zones, 0.91 ± 0.08 (IQR: 0.91-0.95) for tibial zones, and 0.90 ± 0.05 (IQR:0.88-0.94) for all zones. Cone identification and zonal cone placement accuracy were 98% and 96%, respectively, for the femur and 96% and 89%, respectively, for the tibia. CONCLUSION: A deep learning algorithm was developed to automatically delineate revision zones and cone placements on postoperative rTKA radiographs in an objective, standardized manner. The performance of the algorithm was validated against a trained surgeon, suggesting that the algorithm demonstrated excellent predictive capabilities in accordance with relevant anatomical landmarks used by arthroplasty surgeons in practice.

Radiographic findings involved in knee osteoarthritis progression are associated with pain symptom frequency and baseline disease severity: a population-level analysis using deep learning.

PURPOSE: To (1) develop a deep-learning (DL) algorithm capable of producing limb-length and knee-alignment measurements, and (2) determine the association between limb-length discrepancy (LLD), coronal-plane alignment, osteoarthritis (OA) severity, and patient-reported knee pain. METHODS: A multicenter, prospective patient cohort from the Osteoarthritis Initiative between 2004 and 2015 with full-limb standing radiographs at 12 month follow-up was included. A convolutional neural network was developed to automate measurements of the hip-knee-ankle (HKA) angle, femur, and tibia lengths, and LLD. At 12 month follow-up, patients reported their frequency of knee pain since enrollment and current level of knee pain. RESULTS: A total of 1011 patients (2022 knees, 52.3% female) with an average age of 61.2 ± 9.0 years were included. The algorithm performed 12,312 measurements in 5.4 h. ICC values of HKA and LLD ranged between 0.87 and 1.00 when compared against trained radiologist measurements. Knees producing pain most days of the month were significantly more varus (mean HKA:- 3.9° ± 2.8°) or valgus (mean HKA:2.8° ± 2.3°) compared to knees that did not produce any pain (p < 0.05). In varus knees, those producing pain on most days were part of the shorter limb compared to nonpainful knees (p < 0.05). Baseline Kellgren-Lawrence grade was significantly associated with HKA magnitude, LLD, and pain frequency at 12 month follow-up (p < 0.05 all). CONCLUSION: A higher frequency of knee pain was associated with more severe coronal plane deformity, with valgus deviation being one degree less than varus on average, suggesting that the knee tolerates less valgus deformation before symptoms become more consistent. Knee pain frequency was also associated with greater LLD and baseline KL grade, suggesting an association between radiographically apparent joint degeneration and pain frequency. LEVEL OF EVIDENCE: IV case series.

Effects of food and race on the pharmacokinetics of lazertinib in healthy subjects and patients with EGFR mutation-positive advanced non-small cell lung cancer.

OBJECTIVES: Lazertinib is a potent, irreversible, brain-penetrant, mutant-selective, and wild type-sparing third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor indicated for advanced non-small cell lung cancer (NSCLC). The study aimed to evaluate the effects of food and race on the pharmacokinetics (PK) of lazertinib from a healthy volunteer trial and PK data from NSCLC patients with EGFR mutation. MATERIALS AND METHODS: An open-label, single-dose, two-period, single-sequence crossover study was conducted in healthy subjects with two race groups (non-Asian and Asian). Subjects orally received a single dose of lazertinib 240 mg in fasted and fed state (high-fat meal) in each period separated by a 21-day washout. An open-label, multicenter, phase 1/2 study was conducted in Asian and non-Asian patients with NSCLC. Patients were given oral lazertinib 20-320 mg once daily in fasted state continuously in 21-day cycles. PK parameters were evaluated using non-compartmental analysis. RESULTS: A total of 24 healthy subjects (12 non-Asians and 12 Asians) and 52 NSCLC patients (22 non-Asians and 30 Asians) were evaluated. The change in the overall systemic exposure of lazertinib at fed state was less than 15%. Non-Asians showed 58-76% of the systemic exposure than Asians in healthy subjects. In contrast, there were no significant differences in systemic exposure by race both after single and multiple doses among NSCLC patients. CONCLUSION: Lazertinib can be taken with or without food considering the comparable systemic exposures related to food. Although effect of race was not consistent across studies, there was no evidence for dose adjustment based on race.

The EDN1/EDNRA/ß­arrestin axis promotes colorectal cancer progression by regulating STAT3 phosphorylation.

Endothelin receptor A (EDNRA) has been reported to play various crucial physiological roles and has been shown to be associated with the pathology of several diseases, including colorectal cancer (CRC). However, the molecular mechanisms of EDNRA in the development of human CRC have not been fully elucidated to date. In this context, the present study was performed to investigate biological functions and novel downstream signaling pathways affected by EDNRA, during CRC progression. First, using public data repositories, it was observed that the EDRNA expression levels were markedly increased in CRC tissues, as compared to normal tissues. Patients with CRC with an increased EDNRA expression exhibited a significantly decreased survival rate in comparison with those with a lower EDNRA expression. Furthermore, a positive correlation between the levels of EDNRA and its ligand, EDN1, was found in CRC tissues. The ectopic expression of EDNRA or its ligand, EDN1, promoted, whereas the silencing of EDNRA or EDN1 decreased cell proliferation and migration in vitro. To elucidate the signaling pathways involved in the regulation of EDNRA expression in CRC cells, a phosphokinase array analysis was performed, and it was observed that the knockdown of EDNRA substantially suppressed the phosphorylation of signal transducer and activator of transcription 3 (STAT3) in CRC cells. Of note, STAT3 silencing simultaneously decreased EDN1 and EDNRA expression, with the expression of EDN1 and/or EDNRA appearing to be directly regulated by binding STAT3 to their promoter region, according to chromatin immunoprecipitation and promoter assays, ultimately indicating a positive feedback loop in the expression of EDNRA and EDN1. It was also observed that treatment with an EDNRA antagonist (macitentan), alone or in combination with cisplatin, suppressed cell growth and migration ability, and induced cell apoptosis. Collectively, these data suggest a critical role of the EDN1/EDNRA signaling pathway in CRC progression. Thus, the pharmacological intervention of this signaling pathway may prove to be a potential therapeutic approach for patients with CRC.

The modern Burch-Schneider antiprotrusio cage for the treatment of acetabular defects: is it still an option? A systematic review.

BACKGROUND: A number of papers have been published about the clinical performance of modern rough-blasted titanium Burch-Schneider antiprotrusio cages (BS-APCs) for the treatment of acetabular bone defects. However, no systematic review of the literature has been published to date. METHODS: The US National Library of Medicine (PubMed/MEDLINE), EMBASE, and the Cochrane Database of Systematic Reviews were queried for publications using keywords pertinent to Burch-Schneider antiprotrusio cage, revision THA, and clinical outcomes. RESULTS: 8 articles were found to be suitable for inclusion in the present study in which 374 cases (370 patients) had been treated with modern BS-APCs. Most acetabular bone defects were type 3 according to the Paprosky classification (type 2C: 18.1%, 3A: 51%, and 3B: 28.9%). The overall re-revision rate for the 374 acetabular reconstructions with modern BS-APCs was 11.5% (43 cases). The short-term survival rate of the modern BS-APC construct was 90.6% (339 out of 374 cases), while the mid-term survival rate was 85.6% (320 out of 374 cases), and the long-term survival rate 62% (54 out of 87 cases). The most common reasons for revision were aseptic loosening (5.6%), periprosthetic joint infection (3.8%), dislocation (2.7%), and acetabular periprosthetic fracture (1.9%). CONCLUSIONS: There was moderate quality evidence to show that the use of modern rough blasted titanium BS-APCs in cases of acetabular bone loss has an unacceptably high failure rate (38%). Given that antiprotrusio cages do not provide any biological fixation, we would not recommend the routine use of modern BS-APCs in complex revision THA cases. By contrast, the satisfactory short- to mid-term outcome of modern BS-APCs in combination with their low cost compared to highly porous acetabular implants, make us feel that BS-APCs might still be used in selected elderly or low-demand patients without severe superomedial acetabular bone loss.

PCW-A1001, AI-assisted de novo design approach to design a selective inhibitor for FLT-3(D835Y) in acute myeloid leukemia.

Treating acute myeloid leukemia (AML) by targeting FMS-like tyrosine kinase 3 (FLT-3) is considered an effective treatment strategy. By using AI-assisted hit optimization, we discovered a novel and highly selective compound with desired drug-like properties with which to target the FLT-3 (D835Y) mutant. In the current study, we applied an AI-assisted de novo design approach to identify a novel inhibitor of FLT-3 (D835Y). A recurrent neural network containing long short-term memory cells (LSTM) was implemented to generate potential candidates related to our in-house hit compound (PCW-1001). Approximately 10,416 hits were generated from 20 epochs, and the generated hits were further filtered using various toxicity and synthetic feasibility filters. Based on the docking and free energy ranking, the top compound was selected for synthesis and screening. Of these three compounds, PCW-A1001 proved to be highly selective for the FLT-3 (D835Y) mutant, with an IC50 of 764 nM, whereas the IC50 of FLT-3 WT was 2.54 µM.

Particulate matter promotes cancer metastasis through increased HBEGF expression in macrophages.

Although many cohort studies have reported that long-term exposure to particulate matter (PM) can cause lung cancer, the molecular mechanisms underlying the PM-induced increase in cancer metastasis remain unclear. To determine whether PM contributes to cancer metastasis, cancer cells were cultured with conditioned medium from PM-treated THP1 cells, and the migration ability of the treated cancer cells was assessed. The key molecules involved were identified using RNA-seq analysis. In addition, metastatic ability was analyzed in vivo by injection of cancer cells into the tail vein and intratracheal injection of PM into the lungs of C57BL/6 mice. We found that PM enhances the expression of heparin-binding EGF-like growth factor (HBEGF) in macrophages, which induces epithelial-to-mesenchymal transition (EMT) in cancer cells, thereby increasing metastasis. Macrophage stimulation by PM results in activation and subsequent nuclear translocation of the aryl hydrocarbon receptor and upregulation of HBEGF. Secreted HBEGF activates EGFR on the cancer cell surface to induce EMT, resulting in increased migration and invasion in vitro and increased metastasis in vivo. Therefore, our study reveals a critical PM-macrophage-cancer cell signaling axis mediating EMT and metastasis and provides an effective therapeutic approach for PM-induced malignancy.

Validating the use of 3D biplanar radiography versus CT when measuring femoral anteversion after total hip arthroplasty : a comparative study.

AIMS: Although CT is considered the benchmark to measure femoral version, 3D biplanar radiography (hipEOS) has recently emerged as a possible alternative with reduced exposure to ionizing radiation and shorter examination time. The aim of our study was to evaluate femoral stem version in postoperative total hip arthroplasty (THA) patients and compare the accuracy of hipEOS to CT. We hypothesize that there will be no significant difference in calculated femoral stem version measurements between the two imaging methods. METHODS: In this study, 45 patients who underwent THA between February 2016 and February 2020 and had both a postoperative CT and EOS scan were included for evaluation. A fellowship-trained musculoskeletal radiologist and radiological technician measured femoral version for CT and 3D EOS, respectively. Comparison of values for each imaging modality were assessed for statistical significance. RESULTS: Comparison of the mean postoperative femoral stem version measurements between CT and 3D hipEOS showed no significant difference (p = 0.862). In addition, the two version measurements were strongly correlated (r = 0.95; p < 0.001), and the mean paired difference in postoperative femoral version for CT scan and 3D biplanar radiography was -0.09° (95% confidence interval -1.09 to 0.91). Only three stem measurements (6.7%) were considered outliers with a > 5° difference. CONCLUSION: Our study supports the use of low-dose biplanar radiography for the postoperative assessment of femoral stem version after THA, demonstrating high correlation with CT. We found no significant difference for postoperative femoral version when comparing CT to 3D EOS. We believe 3D EOS is a reliable option to measure postoperative femoral version given its advantages of lower radiation dosage and shorter examination time.Cite this article: Bone Joint J 2022;104-B(11):1196-1201.