Recent advances in CAR-T cell therapy for acute myeloid leukaemia.

Acute myeloid leukaemia (AML) is a fatal and refractory haematologic cancer that primarily affects adults. It interferes with bone marrow cell proliferation. Patients have a 5 years survival rate of less than 30% despite the availability of several treatments, including chemotherapy, allogeneic haematopoietic stem cell transplantation (Allo-HSCT), and receptor antagonist drugs. Allo-HSCT is the mainstay of acute myeloid leukaemia treatment. Although it does work, there are severe side effects, such as graft-versus-host disease (GVHD). In recent years, chimeric antigen receptor (CAR)-T cell therapies have made significant progress in the treatment of cancer. These engineered T cells can locate and recognize tumour cells in vivo and release a large number of effectors through immune action to effectively kill tumour cells. CAR-T cells are among the most effective cancer treatments because of this property. CAR-T cells have demonstrated positive therapeutic results in the treatment of acute myeloid leukaemia, according to numerous clinical investigations. This review highlights recent progress in new targets for AML immunotherapy, and the limitations, and difficulties of CAR-T therapy for AML.

Development of Predictive Models to Inform a Novel Risk Categorization Framework for Antibiotic Resistance in Escherichia coli-Caused Uncomplicated Urinary Tract Infection.

BACKGROUND: In clinical practice, challenges in identifying patients with uncomplicated urinary tract infections (uUTIs) at risk of antibiotic nonsusceptibility may lead to inappropriate prescribing and contribute to antibiotic resistance. We developed predictive models to quantify risk of nonsusceptibility to 4 commonly prescribed antibiotic classes for uUTI, identify predictors of nonsusceptibility to each class, and construct a corresponding risk categorization framework for nonsusceptibility. METHODS: Eligible females aged ≥12 years with Escherichia coli-caused uUTI were identified from Optum's de-identified Electronic Health Record dataset (1 October 2015-29 February 2020). Four predictive models were developed to predict nonsusceptibility to each antibiotic class and a risk categorization framework was developed to classify patients' isolates as low, moderate, and high risk of nonsusceptibility to each antibiotic class. RESULTS: Predictive models were developed among 87 487 patients. Key predictors of having a nonsusceptible isolate to ≥3 antibiotic classes included number of previous UTI episodes, prior ß-lactam nonsusceptibility, prior fluoroquinolone treatment, Census Bureau region, and race. The risk categorization framework classified 8.1%, 14.4%, 17.4%, and 6.3% of patients as having isolates at high risk of nonsusceptibility to nitrofurantoin, trimethoprim-sulfamethoxazole, ß-lactams, and fluoroquinolones, respectively. Across classes, the proportion of patients categorized as having high-risk isolates was 3- to 12-fold higher among patients with nonsusceptible isolates versus susceptible isolates. CONCLUSIONS: Our predictive models highlight factors that increase risk of nonsusceptibility to antibiotics for uUTIs, while the risk categorization framework contextualizes risk of nonsusceptibility to these treatments. Our findings provide valuable insight to clinicians treating uUTIs and may help inform empiric prescribing in this population.

A Population-Based Study of Genes Previously Implicated in Breast Cancer.

BACKGROUND: Population-based estimates of the risk of breast cancer associated with germline pathogenic variants in cancer-predisposition genes are critically needed for risk assessment and management in women with inherited pathogenic variants. METHODS: In a population-based case-control study, we performed sequencing using a custom multigene amplicon-based panel to identify germline pathogenic variants in 28 cancer-predisposition genes among 32,247 women with breast cancer (case patients) and 32,544 unaffected women (controls) from population-based studies in the Cancer Risk Estimates Related to Susceptibility (CARRIERS) consortium. Associations between pathogenic variants in each gene and the risk of breast cancer were assessed. RESULTS: Pathogenic variants in 12 established breast cancer-predisposition genes were detected in 5.03% of case patients and in 1.63% of controls. Pathogenic variants in BRCA1 and BRCA2 were associated with a high risk of breast cancer, with odds ratios of 7.62 (95% confidence interval [CI], 5.33 to 11.27) and 5.23 (95% CI, 4.09 to 6.77), respectively. Pathogenic variants in PALB2 were associated with a moderate risk (odds ratio, 3.83; 95% CI, 2.68 to 5.63). Pathogenic variants in BARD1, RAD51C, and RAD51D were associated with increased risks of estrogen receptor-negative breast cancer and triple-negative breast cancer, whereas pathogenic variants in ATM, CDH1, and CHEK2 were associated with an increased risk of estrogen receptor-positive breast cancer. Pathogenic variants in 16 candidate breast cancer-predisposition genes, including the c.657_661del5 founder pathogenic variant in NBN, were not associated with an increased risk of breast cancer. CONCLUSIONS: This study provides estimates of the prevalence and risk of breast cancer associated with pathogenic variants in known breast cancer-predisposition genes in the U.S. population. These estimates can inform cancer testing and screening and improve clinical management strategies for women in the general population with inherited pathogenic variants in these genes. (Funded by the National Institutes of Health and the Breast Cancer Research Foundation.).

Estimation of Muscle Forces of Lower Limbs Based on CNN-LSTM Neural Network and Wearable Sensor System.

Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN-LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN-LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN-LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.

O-Trifluoromethylation of Carboxylic Acids via the Formation and Activation of Acyloxy(phenyl)trifluoromethyl-λ3-Iodanes.

Here we report the challenging O-trifluoromethylation of carboxylic acids via the formation and activation of acyloxy(phenyl)trifluoromethyl-λ3-iodanes. The method provides an easy access to various potentially valuable and hitherto elusive trifluoromethyl carboxylic esters. A remarkably wide range of substrates with commonly encountered functional groups are compatible with this reaction, including aromatic and aliphatic carboxylic acids, as well as Food and Drug Administration (FDA) approved drugs and pharmaceutically relevant molecules. The reaction mechanism and the origins of the enhanced reactivity by zinc chloride (ZnCl2) were discussed from experimental evidence and density functional theory (DFT) calculation.

A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors.

Gait phase recognition is of great importance in the development of rehabilitation devices. The advantages of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined (LSTM-CNN) in this paper, then a gait phase recognition method based on LSTM-CNN neural network model is proposed. In the LSTM-CNN model, the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features A wireless sensor system including six inertial measurement units (IMU) fixed on the six positions of the lower limbs was developed. The difference in the gait recognition performance of the LSTM-CNN model was estimated using different groups of input data collected by seven different IMU grouping methods. Four phases in a complete gait were considered in this paper including the supporting phase with the right hill strike (SU-RHS), left leg swimming phase (SW-L), the supporting phase with the left hill strike (SU-LHS), and right leg swimming phase (SW-R). The results show that the best performance of the model in gait recognition appeared based on the group of data from all the six IMUs, with the recognition precision and macro-F1 unto 95.03% and 95.29%, respectively. At the same time, the best phase recognition accuracy for SU-RHS and SW-R appeared and up to 96.49% and 95.64%, respectively. The results also showed the best phase recognition accuracy (97.22%) for SW-L was acquired based on the group of data from four IMUs located at the left and right thighs and shanks. Comparably, the best phase recognition accuracy (97.86%) for SU-LHS was acquired based on the group of data from four IMUs located at left and right shanks and feet. Ulteriorly, a novel gait recognition method based on Data Pre-Filtering Long Short-Term Memory and Convolutional Neural Network (DPF-LSTM-CNN) model was proposed and its performance for gait phase recognition was evaluated. The experiment results showed that the recognition accuracy reached 97.21%, which was the highest compared to Deep convolutional neural networks (DCNN) and CNN-LSTM.

Analysis of Ankle Muscle Dynamics during the STS Process Based on Wearable Sensors.

Ankle joint moment is an important indicator for evaluating the stability of the human body during the sit-to-stand (STS) movement, so a method to analyze ankle joint moment is needed. In this study, a wearable sensor system that could derive surface-electromyography (sEMG) signals and kinematic signals on the lower limbs was developed for non-invasive estimation of ankle muscle dynamics during the STS movement. Based on the established ankle joint musculoskeletal information and sEMG signals, ankle joint moment during the STS movement was calculated. In addition, based on a four-segment STS dynamic model and kinematic signals, ankle joint moment during the STS movement was calculated using the inverse dynamics method. Ten healthy young people participated in the experiment, who wore a self-developed wearable sensor system and performed STS movements as an experimental task. The results showed that there was a high correlation (all R ≥ 0.88) between the results of the two methods for estimating ankle joint moment. The research in this paper can provide theoretical support for the development of an intelligent bionic joint actuator and clinical rehabilitation evaluation.

Bicyclization of 4-Cyano-1,2-diketones with Amidines: Synthesis of Tetrahydroimidazo[4,5-b]-5-pyridinones.

A new bicyclization of 4-cyano-1,2-diketones with amidines is reported for the construction of tetrahydro-5H-imidazo[4,5-b]pyridin-5-ones. The tandem reaction is proposed to involve the double condensation-cyclization of diketone with amidine, an α-ketol like rearrangement, the hydrolysis of nitrile followed by intramolecular nucleophilic addition, and the elimination of water. Features of the bicyclization method include high yields, good functional group tolerance, and mild reaction conditions.

Trifluoromethylations of Alkenes Using PhICF3Cl as Bifunctional Reagent.

We described a trifluoromethylation of alkenes using PhICF3Cl as bifunctional reagent. Chlorotrifluoromethylated products were obtained when nonconjugated alkenes were treated with PhICF3Cl in 1,4-dioxane at 60 °C, while vinyl C-H trifluoromethylated products were obtained by further elimination of hydrochloride in the case of those conjugated alkene substrates in DMF. Broad substrate scope, especially including complex alkenes bearing biological active motifs, suggests that this mild reaction is feasible for late-stage modification in drug discovery.

Identification of minor effect QTLs for plant architecture related traits using super high density genotyping and large recombinant inbred population in maize (Zea mays).

BACKGROUND: Plant Architecture Related Traits (PATs) are of great importance for maize breeding, and mainly controlled by minor effect quantitative trait loci (QTLs). However, cloning or even fine-mapping of minor effect QTLs is very difficult in maize. Theoretically, large population and high density genetic map can be helpful for increasing QTL mapping resolution and accuracy, but such a possibility have not been actually tested. RESULTS: Here, we employed a genotyping-by-sequencing (GBS) strategy to construct a linkage map with 16,769 marker bins for 1021 recombinant inbred lines (RILs). Accurately mapping of well studied genes P1, pl1 and r1 underlying silk color demonstrated the map quality. After QTL analysis, a total of 51 loci were mapped for six PATs. Although all of them belong to minor effect alleles, the lengths of the QTL intervals, with a minimum and median of 1.03 and 3.40 Mb respectively, were remarkably reduced as compared with previous reports using smaller size of population or small number of markers. Several genes with known function in maize were shown to be overlapping with or close neighboring to these QTL peaks, including na1, td1, d3 for plant height, ra1 for tassel branch number, and zfl2 for tassel length. To further confirm our mapping results, a plant height QTL, qPH1a, was verified by an introgression lines (ILs). CONCLUSIONS: We demonstrated a method for high resolution mapping of minor effect QTLs in maize, and the resulted comprehensive QTLs for PATs are valuable for maize molecular breeding in the future.

Mathematical modeling of the adsorption/desorption characteristics of anthocyanins from muscadine (Vitis rotundifolia cv. Noble) juice pomace on Amberlite FPX66 resin in a fixed bed column.

BACKGROUND: Muscadine grape pomace, a by-product of juicing and wine-making, contains significant amounts of anthocyanin 3,5-diglucosides, known to be beneficial to human health. RESULTS: The objective of this research was to use mathematical modeling to investigate the adsorption/desorption characteristics of these anthocyanins from muscadine grape pomace on Amberlite FPX66 resin in a fixed bed column. Anthocyanins were extracted using hot water and ultrasound, and the extracts were loaded onto a resin column at five bed depths (5, 6, 8, 10 and 12 cm) using three flow rates (4, 6 and 8 mL min-1 ). It was found that adsorption on the column fitted the bed depth service time (BDST) model and the empty bed residence time (EBRT) model. Desorption was achieved by eluting the column using ethanol at four concentrations (25, 40, 55 and 70% v/v) and could be described with an empirical sigmoid model. The breakthrough curves of anthocyanins fitted the BDST model for all three flow rates with R2 values of 0.983, 0.992 and 0.984 respectively. The EBRT model was successfully employed to find the operating lines, which allow for column scale-up while still achieving similar results to those found in a laboratory operation. Desorption with 40% (v/v) ethanol achieved the highest recovery rate of anthocyanins at 79.6%. CONCLUSION: The mathematical models established in this study can be used in designing a pilot/industrial- scale column for the separation and concentration of anthocyanins from muscadine juice pomace. © 2018 Society of Chemical Industry.

Contribution of Fetal, but Not Adult, Pulmonary Mesothelium to Mesenchymal Lineages in Lung Homeostasis and Fibrosis.

The lung is enveloped by a layer of specialized epithelium, the pulmonary mesothelium. In other organs, mesothelial cells undergo epithelial-mesenchymal transition and contribute to organ stromal cells. The contribution of pulmonary mesothelial cells (PMCs) to the developing lung has been evaluated with differing conclusions. PMCs have also been indirectly implicated in lung fibrosis in the progressive, fatal lung disease idiopathic pulmonary fibrosis. We used fetal or postnatal genetic pulse labeling of PMCs to assess their fate in murine development, normal lung homeostasis, and models of pulmonary fibrosis. We found that most fetal PMC-derived mesenchymal cells (PMCDCs) expressed markers of pericytes and fibroblasts, only a small minority expressed smooth muscle markers, and none expressed endothelial cell markers. Postnatal PMCs did not contribute to lung mesenchyme during normal lung homeostasis or in models of lung fibrosis. However, fetal PMCDCs were abundant and actively proliferating within fibrotic regions in lung fibrosis models, suggesting that they actively participate in the fibrotic process. These data clarify the role of fetal and postnatal PMCDCs in lung development and disease.

Real-world effectiveness of mepolizumab in severe asthma and chronic rhinosinusitis in the United States: Impact of comorbidity and sinus surgery.

BACKGROUND: Trial data demonstrate that mepolizumab, a humanized anti-interleukin 5 monoclonal antibody, is effective for patients with severe asthma and comorbid chronic rhinosinusitis (CRS) with nasal polyps. This real-world, retrospective cohort study investigated mepolizumab for US patients with severe asthma and CRS with/without sinus surgery. METHODS: IQVIA PharMetrics Plus claims data from baseline and follow-up (12 months before and after mepolizumab initiation) were used to analyze three patient cohorts: cohort 1 (severe asthma only); cohort 2 (severe asthma + comorbid CRS without sinus surgery); and cohort 3 (severe asthma+comorbid CRS+sinus surgery), allowing for cross-cohort comparisons. RESULTS: The analysis included 495, 370, and 85 patients in cohort 1, cohort 2, and cohort 3, respectively. Systemic and oral corticosteroid use was lower for all cohorts after mepolizumab initiation. In cohort 3, asthma rescue inhaler and antibiotic use were lower during follow-up than baseline. Asthma exacerbations were reduced by 28% to 44% comparing follow-up versus baseline, with the largest reduction in cohort 3 (ratio of incidence rate ratio [RR] vs cohort 1: 0.76; p = 0.036). Reductions in oral corticosteroid claims were greater following mepolizumab initiation for cohort 3 versus cohort 1 (RR, 0.72; p = 0.011) and cohort 2 (RR, 0.70; p < 0.01). In cohorts 1 through 3, outpatient and emergency department visits were reduced by 1 to 2 and 0.4 to 0.6 visits annually, asthma-related and asthma exacerbation-related total costs were reduced by $387 to $2580 USD, and medical costs were reduced by $383 to $2438 USD during follow-up. CONCLUSIONS: Consistent with trial data, mepolizumab use in real-world practice shows benefits across comorbid patient cohorts with more a pronounced impact in those with severe asthma+comorbid CRS + sinus surgery.

Descriptive Analysis of Types and Diagnoses Associated with Lower Extremity Amputation: Analysis of the US Veterans Health Administration Database 2019-2023.

INTRODUCTION: Veterans in the US have higher rates of lower extremity amputation (LEA) compared to the general population and these rates have increased between 2008 and 2018. There is limited data which directly evaluate the potential underlying comorbidities associated with LEA in the veterans' population especially with the most recent data. Such information is critical to help inform clinical management strategies to reduce the risk of amputations among our veterans. METHODS: This was a retrospective observational study of adults in the Veterans Health Administration database who underwent LEA from January 1, 2019 to December 31, 2023. The date of the first LEA procedure was defined as the index date. Index LEA type, patient demographic at baseline, and clinical characteristics (including diagnoses for conditions associated with LEA and other comorbidities) 1 year before and 30 days after the index LEA procedure (except for bacterial infections which the identification period was 30 days before and 30 days after the index LEA procedure) were described. RESULTS: Of the 27,134 Veterans with LEA, 67.3% were ≥ 65 years of age, 97.0% were male, and 65.3% were non-Hispanic white. The most common type of LEA was transmetatarsal (52.9%), followed by toe (21.9%), above-knee (15.4%), and below-knee (9.8%). The most prevalent diagnoses associated with LEA were diabetes (81.6%), bacterial infections (79.1%), and peripheral artery disease (PAD; 63.3%). Only 15 Veterans (< 0.1%) had a diagnosis for combat-related injuries to lower extremities. CONCLUSION: Diabetes and PAD are highly prevalent and among the main conditions associated with LEA among US Veterans. Earlier and more effective preventative and clinical management of these conditions offer an opportunity to significantly reduce the rates of LEA in this population.

Spectral encoder to extract the efficient features of Raman spectra for reliable and precise quantitative analysis.

Raman spectroscopy has become a powerful analytical tool highly demanded in many applications such as microorganism sample analysis, food quality control, environmental science, and pharmaceutical analysis, owing to its non-invasiveness, simplicity, rapidity and ease of use. Among them, quantitative research using Raman spectroscopy is a crucial application field of spectral analysis. However, the entire process of quantitative modeling largely relies on the extraction of effective spectral features, particularly for measurements on complex samples or in environments with poor spectral signal quality. In this paper, we propose a method of utilizing a spectral encoder to extract effective spectral features, which can significantly enhance the reliability and precision of quantitative analysis. We built a latent encoded feature regression model; in the process of utilizing the autoencoder for reconstructing the spectrometer output, the latent feature obtained from the intermediate bottleneck layer is extracted. Then, these latent features are fed into a deep regression model for component concentration prediction. Through detailed ablation and comparative experiments, our proposed model demonstrates superior performance to common methods on single-component and multi-component mixture datasets, remarkably improving regression precision while without needing user-selected parameters and eliminating the interference of irrelevant and redundant information. Furthermore, in-depth analysis reveals that latent encoded feature possesses strong nonlinear feature representation capabilities, low computational costs, wide adaptability, and robustness against noise interference. This highlights its effectiveness in spectral regression tasks and indicates its potential in other application fields. Sufficient experimental results show that our proposed method provides a novel and effective feature extraction approach for spectral analysis, which is simple, suitable for various methods, and can meet the measurement needs of different real-world scenarios.

Double-network polyphenol chitosan hydrogels with instant aldehyde-ß-cyclodextrin-based structure as potential for treating bacterially infected wounds.

Treatment of multiple bacterial infected wounds by eliminating bacteria and promoting tissue regeneration remains a clinical challenge. Herein, dual-network hydrogels (CS-GA/A-ß-CD) with snap-structure were designed to achieve curcumin immobilization, using gallic acid-grafted chitosan (CS-GA) and aldehyde-ß-cyclodextrin (A-ß-CD) crosslinked. A-ß-CD were able to achieve rapid dissolution (≥222.35 mg/mL H2O), and helped CS-GA/A-ß-CD achieve rapid gelation (≤66.23 s). By adjusting the ratio of aldehyde groups of A-ß-CD, mechanical properties and drug release can be controlled. CS-GA/A-ß-CD/Cur exhibited excellent antimicrobial properties against S. aureus, E. coli, and P. aeruginosa. In vivo experiments demonstrated that CS-GA/A-ß-CD/Cur achieved acute bacterial infection wound healing after 20th days, proving its great potential for wound dressing.

Likelihood of Antimicrobial Resistance in Urinary E. coli Isolates Among US Female Patients with Recurrent Versus Non-Recurrent uUTI.

OBJECTIVE: To assess the relative likelihood of antimicrobial resistance (AMR) and multi-drug resistance (MDR) among E. coli isolates from outpatients with recurrent versus non-recurrent uncomplicated urinary tract infection (uUTI). METHODS: In this retrospective observational US cohort study, female outpatients (≥12 years) with uUTI, positive E. coli culture, and treated with ≥1 oral antibiotic within ±5 days of diagnosis were grouped into recurrent and non-recurrent uUTI cohorts per their UTI history (past 12 months). AMR to specific drug classes was evaluated at index. Univariable and multivariable logistic regression models estimated the likelihood of not-susceptible E. coli isolates (AMR/MDR) among patients with recurrent uUTI versus non-recurrent uUTI. RESULTS: Recurrent (N = 12,234) and non-recurrent (N = 68,033) uUTI cohorts had similar distributions (race, ethnicity, region). Patients with recurrent uUTI had a higher prevalence of E. coli resistance to trimethoprim-sulfamethoxazole (21.8% vs 18.7%) and fluoroquinolones (14.2% vs 8.6%), and more isolates were extended-spectrum ß-lactamase-producing (5.9% vs 4.1%) compared to non-recurrent uUTI patients. Patients with recurrent uUTI had a higher likelihood (odds ratio [95% confidence interval]) of any AMR (1.28 [1.22-1.34]), single drug-class resistance (1.18 [1.12-1.24]), and resistance to 2 (1.53 [1.41-1.67]) or ≥3 drug classes (1.70 [1.48-1.96]) (all P <.001). CONCLUSION: This study delineated the likelihood of AMR and MDR among E. coli isolates from patients with recurrent versus non-recurrent uUTI. While some treatment guidelines support empiric therapy in recurrent uUTI, the increased likelihood of resistance among these patients suggests that culture and susceptibility testing should be undertaken to inform recurrent uUTI treatment.

Ergosterols with rare peroxide, oxetane ring moiety, and a lactone ring from Aspergillus spectabilis and their immunosuppressive activities.

Ten ergostane-type steroids, including seven undescribed ones named spectasteroids A-G, were obtained from Aspergillus spectabilis. Their structures and absolute configurations were determined based on HRESIMS, NMR, ECD calculations, and single-crystal X-ray diffraction analyses. Structurally, spectasteroid A was a unique example of aromatic ergostane-type steroid that featured a rare peroxide ring moiety; spectasteroid B contained a rare oxetane ring system formed between C-9 and C-14; and spectasteroid C was an unusual 3,4-seco-ergostane steroid with an extra lactone ring between C-3 and C-9. Spectasteroids F and G specifically showed inhibitory effects against concanavalin A-induced T lymphocyte proliferation and lipopolysaccharide-induced B lymphocyte proliferation, with IC50 values ranging from 2.33 to 4.22 µM. Spectasteroid F also showed excellent antimultidrug resistance activity, which remarkable enhanced the inhibitory activity of PTX on the colony formation of SW620/Ad300 cells.

Real-World Neoadjuvant Treatment Patterns and Outcomes in Resected Non-Small-Cell Lung Cancer.

BACKGROUND: Novel neoadjuvant chemoimmunotherapy treatments are being investigated for locally advanced non-small-cell lung cancer (NSCLC), but real-world outcomes for neoadjuvant treatments are poorly understood. This study examined neoadjuvant treatment patterns, real-world event-free survival (rwEFS) and overall survival (OS) in patients with resected, stage II-III NSCLC in the United States (US). METHODS: This retrospective study identified patients in the SEER-Medicare database (2007-2019) with newly diagnosed stage II, IIIA, and IIIB (N2) NSCLC (AJCC 8th edition) treated with neoadjuvant chemo/chemoradiotherapy and resection (index date: neoadjuvant therapy initiation). Neoadjuvant treatment regimens were described. rwEFS (time from index to first recurrence or death, whichever occurred first) and OS (time from index to death) were summarized by Kaplan-Meier analysis for overall population, by disease stage at diagnosis, and by neoadjuvant treatment modality. RESULTS: 221 patients (stage II, N=70; stage III, N=151) met eligibility criteria. The median follow-up from index was 32.7 months. All patients received neoadjuvant chemotherapy (51%) or chemoradiotherapy (49%) prior to surgery; 97% of patients received platinum-based regimens, among which carboplatin+paclitaxel was the most frequent (45%). In all patients, median rwEFS was 17.6 months and 5-year rwEFS was 20.9%; median OS was 48.5 months and 5-year OS was 44.9%. 71% of patients had disease recurrence during follow-up; among them, 28% developed locoregional recurrence as the first recurrence event. CONCLUSIONS: Patients with resected, stage II-III NSCLC who received neoadjuvant chemo/chemoradiotherapy have high rates of disease recurrence and poor survival outcomes, highlighting need for more effective treatments to improve survival rates.

Automated Student Classroom Behaviors' Perception and Identification Using Motion Sensors.

With the rapid development of artificial intelligence technology, the exploration and application in the field of intelligent education has become a research hotspot of increasing concern. In the actual classroom scenarios, students' classroom behavior is an important factor that directly affects their learning performance. Specifically, students with poor self-management abilities, particularly specific developmental disorders, may face educational and academic difficulties owing to physical or psychological factors. Therefore, the intelligent perception and identification of school-aged children's classroom behaviors are extremely valuable and significant. The traditional method for identifying students' classroom behavior relies on statistical surveys conducted by teachers, which incurs problems such as being time-consuming, labor-intensive, privacy-violating, and an inaccurate manual intervention. To address the above-mentioned issues, we constructed a motion sensor-based intelligent system to realize the perception and identification of classroom behavior in the current study. For the acquired sensor signal, we proposed a Voting-Based Dynamic Time Warping algorithm (VB-DTW) in which a voting mechanism is used to compare the similarities between adjacent clips and extract valid action segments. Subsequent experiments have verified that effective signal segments can help improve the accuracy of behavior identification. Furthermore, upon combining with the classroom motion data acquisition system, through the powerful feature extraction ability of the deep learning algorithms, the effectiveness and feasibility are verified from the perspectives of the dimensional signal characteristics and time series separately so as to realize the accurate, non-invasive and intelligent children's behavior detection. To verify the feasibility of the proposed method, a self-constructed dataset (SCB-13) was collected. Thirteen participants were invited to perform 14 common class behaviors, wearing motion sensors whose data were recorded by a program. In SCB-13, the proposed method achieved 100% identification accuracy. Based on the proposed algorithms, it is possible to provide immediate feedback on students' classroom performance and help them improve their learning performance while providing an essential reference basis and data support for constructing an intelligent digital education platform.