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.

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.

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

BACKGROUND: In clinical practice, challenges in identifying patients with uncomplicated urinary tract infections (uUTIs) at risk of antibiotic non-susceptibility may lead to inappropriate prescribing and contribute to antibiotic resistance. We developed predictive models to quantify risk of non-susceptibility to four commonly prescribed antibiotic classes for uUTI, identify predictors of non-susceptibility to each class, and construct a corresponding risk categorization framework for non-susceptibility. METHODS: Eligible females aged ≥12 years with E. coli-caused uUTI were identified from Optum's de-identified Electronic Health Record dataset (10/1/2015‒2/29/2020). Four predictive models were developed to predict non-susceptibility to each antibiotic class and a risk categorization framework was developed to classify patients' isolates as low, moderate, and high risk of non-susceptibility to each antibiotic class. RESULTS: Predictive models were developed among 87487 patients. Key predictors of having a non-susceptible isolate to ≥3 antibiotic classes included number of previous UTI episodes, prior ß-lactam non-susceptibility, 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 non-susceptibility to nitrofurantoin, trimethoprim-sulfamethoxazole, ß-lactams, and fluoroquinolones, respectively. Across classes, the proportion of patients categorized as having high-risk isolates was 3-12 folds higher among patients with non-susceptible isolates versus susceptible isolates. CONCLUSIONS: Our predictive models highlight factors that increase risk of non-susceptibility to antibiotics for uUTIs, while the risk categorization framework contextualizes risk of non-susceptibility to these treatments. Our findings provide valuable insight to clinicians treating uUTIs and may help inform empiric prescribing in this population.

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.

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.

Likelihood of Antimicrobial Resistance in Urinary E coli Isolates among US Female Patients with Recurrent vs Non-Recurrent Uncomplicated Urinary Tract Infection.

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.

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.

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.

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.

Event-free survival as a predictor of overall survival and recurrence burden of patients with non-small cell lung cancer receiving neoadjuvant therapy.

OBJECTIVES: Event-free survival has replaced overall survival as a primary end point in many recent and ongoing clinical trials. This study aims to examine the correlation between real-world event-free survival and overall survival and to assess the clinical and economic burden associated with disease recurrence among patients with resected stage II to III non-small cell lung cancer who received neoadjuvant therapy in the United States. METHODS: This retrospective study used the Surveillance, Epidemiology, and End Results Medicare database (2007-2019) to identify patients with newly diagnosed, resected, stage II to IIIB (N2) non-small cell lung cancer who received neoadjuvant therapy. The correlation between real-world event-free survival and overall survival was assessed using the normal scores rank correlation and landmark analysis. Overall survival, all-cause health care resource use and costs, and non-small cell lung cancer-related health care resource use and costs were compared between patients with and without recurrence. RESULTS: A total of 221 patients met the eligibility criteria (median follow-up time from neoadjuvant treatment initiation: 32.7 months). The mean age was 72.1 years, and 57.0% of patients were male. Real-world, event-free survival and overall survival are positively and significantly correlated (0.68; 95% CI, 0.52-0.79). Patients with recurrence had significantly shorter median overall survival (19.3 vs 116.9 months), 4.59 times increased risk of death (95% CI, 2.56-8.26), and significantly higher all-cause and non-small cell lung cancer-related health care resource use and costs (adjusted mean monthly costs per patient difference: $5758 and $3187, respectively [all P < .001]). CONCLUSIONS: These findings help validate event-free survival as a clinically meaningful end point and strong predictor for overall survival and highlight the need for additional novel therapies that may delay or prevent recurrence in resectable stage II and III non-small cell lung cancer.

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.

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.

Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy.

Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively gives chemical structural information with the Raman spectrometer. Despite its technical advantages, in practical application scenarios, Raman spectroscopy often suffers from interference, such as noises and baseline drifts, resulting in the inability to acquire high-quality Raman spectroscopy signals, which brings challenges to subsequent spectral analysis. The commonly applied spectral preprocessing methods, such as Savitzky-Golay smooth and wavelet transform, can only perform corresponding single-item processing and require manual intervention to carry out a series of tedious trial parameters. Especially, each scheme can only be used for a specific data set. In recent years, the development of deep neural networks has provided new solutions for intelligent preprocessing of spectral data. In this paper, we first creatively started from the basic mechanism of spectral signal generation and constructed a mathematical model of the Raman spectral signal. By counting the noise parameters of the real system, we generated a simulation dataset close to the output of the real system, which alleviated the dependence on data during deep learning training. Due to the powerful nonlinear fitting ability of the neural network, fully connected network model is constructed to complete the baseline estimation task simply and quickly. Then building the Unet model can effectively achieve spectral denoising, and combining it with baseline estimation can realize intelligent joint processing. Through the simulation dataset experiment, it is proved that compared with the classic method, the method proposed in this paper has obvious advantages, which can effectively improve the signal quality and further ensure the accuracy of the peak intensity. At the same time, when the proposed method is applied to the actual system, it also achieves excellent performance compared with the common method, which indirectly indicates the effectiveness of the Raman signal simulation model. The research presented in this paper offers a variety of efficient pipelines for the intelligent processing of Raman spectroscopy, which can adapt to the requirements of different tasks while providing a new idea for enhancing the quality of Raman spectroscopy signals.

Evaluation of disease-free survival as a predictor of overall survival and assessment of real-world burden of disease recurrence in resected early-stage non-small cell lung cancer.

BACKGROUND: Intermediate endpoints, such as disease-free survival (DFS), have shown good correlation with overall survival (OS) in early-stage non-small cell lung cancer (NSCLC) clinical trials. However, real-world data are limited, and no previous real-world study has quantified the clinical and economic burden of disease recurrence. OBJECTIVE: To examine the association between real-world DFS (rwDFS) and OS and quantify the association between NSCLC recurrence and health care resource utilization (HCRU), health care costs, and OS in patients with resected early-stage NSCLC in the United States. METHODS: Data from the Surveillance, Epidemiology, and End Results-Medicare database (2007-2019) for patients with newly diagnosed stage IB (tumor size ≥ 4 cm) to IIIA (American Joint Committee on Cancer 7th edition) NSCLC who underwent surgery for primary NSCLC were analyzed in this retrospective observational study. Baseline patient demographic and clinical characteristics were described. rwDFS and OS were compared between patients with vs without recurrence using Kaplan-Meier curves and the log-rank test; their correlation was assessed using normal scores rank correlation. All-cause and NSCLC-related HCRU and health care costs were summarized, and mean monthly allcause and NSCLC-related health care costs were compared between cohorts using generalized linear models. RESULTS: Of the 1,761 patients who underwent surgery, 1,182 (67.1%) had disease recurrence; these patients had shorter OS from the index date and shorter subsequent OS at each postsurgery landmark (ie, 1, 3, and 5 years) than those without recurrence (all P < 0.001). OS and rwDFS were significantly correlated (0.57; P < 0.001). Patients with recurrence also had significantly higher all-cause and NSCLC-related HCRU and mean monthly all-cause and NSCLC-related health care costs during the study period. CONCLUSIONS: Postsurgery rwDFS was significantly correlated with OS in patients with early-stage NSCLC. Patients with postsurgery recurrence had a higher risk of death and incurred higher HCRU and health care costs than those without recurrence. These findings highlight the importance of preventing or delaying recurrence in patients with resected NSCLC. DISCLOSURES: Dr West is Senior Medical Director at AccessHope and an Associate Professor at City of Hope. He also serves on the advisory board for Amgen, AstraZeneca, Genentech/Roche, Gilead, Merck, Mirati Therapeutics, Regeneron, Summit Therapeutics, and Takeda and as a speaker for AstraZeneca and Merck. Drs Hu, Chirovsky, and Samkari are employees of Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA and own stock/stock options in Merck & Co., Inc., Rahway, NJ, USA. Drs Zhang, Song, Gao, and Signorovitch, Mr Lerner, and Ms Jiang are employees of Analysis Group, Inc., a consulting company that has provided paid consulting services to Merck Sharp & Dohme LLC., a subsidiary of Merck & Co., Inc., Rahway, NJ, USA, which funded the development and conduct of this study and article. This study used the linked SEER-Medicare database. The interpretation and reporting of these data are the sole responsibility of the authors. The collection of cancer incidence data used in this study was supported by the California Department of Public Health pursuant to California Health and Safety Code Section 103885; Centers for Disease Control and Prevention's National Program of Cancer Registries, under cooperative agreement 5NU58DP006344; the National Cancer Institute's SEER Program under contract HHSN261201800032I awarded to the University of California, San Francisco, contract HHSN261201800015I awarded to the University of Southern California, and contract HHSN261201800009I awarded to the Public Health Institute. The ideas and opinions expressed herein are those of the authors and do not necessarily reflect the opinions of the State of California, Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their contractors and subcontractors.

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.

Treatment Patterns and Outcomes in Resected Early-stage Non-small Cell Lung Cancer: An Analysis of the SEER-Medicare Data.

BACKGROUND: As the non-small cell lung cancer (NSCLC) adjuvant treatment landscape evolves, an evaluation of treatment patterns and outcomes of patients with early-stage, resected NSCLC eligible for adjuvant treatment in routine clinical practice is needed to better understand the unmet needs in this patient population. MATERIALS AND METHODS: Data from the Surveillance, Epidemiology, and End Results (SEER)-Medicare database (2007-2019) were used to identify patients with newly diagnosed stage IB (tumor size ≥4cm)-IIIA (AJCC 7th edition) NSCLC who received primary surgery (index date). We assessed adjuvant treatment patterns, real-world disease-free survival (rwDFS; time from index date to first recurrence or death) and overall survival (OS; time from index date to death), and loco-regional recurrence pattern and treatment distribution. RESULTS: Among 1761 patients with primary surgery, mean age was 73.8 years; 47.9% were male; and 83.9% were white. Approximately 41% of patients received adjuvant chemotherapy; median time from surgery to adjuvant chemotherapy initiation was 48 days, and the most frequently observed adjuvant chemotherapy regimen was carboplatin+paclitaxel (24.5%). In the overall population, median rwDFS was 24.8 months and OS was 76.7 months; 5-year rwDFS and OS rates were 29.3% and 57.5%, respectively. Among 392 patients with loco-regional recurrence, the most frequently observed treatment was curative radiation monotherapy (28.2%). CONCLUSION: Despite clinical guideline recommendations, rate of adjuvant chemotherapy among patients with resected early-stage NSCLC was low in clinical practice. Overall, among patients with early-stage NSCLC treated with conventional primary surgery, poor survival outcomes were observed, highlighting the need for and importance of more effective adjuvant treatments.

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.

Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries.

BACKGROUND: Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk. METHODS: Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds. RESULTS: Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases. CONCLUSION: Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.

Germline Pathogenic Variants in Cancer Predisposition Genes Among Women With Invasive Lobular Carcinoma of the Breast.

PURPOSE: To determine the contribution of germline pathogenic variants (PVs) in hereditary cancer testing panel genes to invasive lobular carcinoma (ILC) of the breast. MATERIALS AND METHODS: The study included 2,999 women with ILC from a population-based cohort and 3,796 women with ILC undergoing clinical multigene panel testing (clinical cohort). Frequencies of germline PVs in breast cancer predisposition genes (ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, PALB2, PTEN, RAD51C, RAD51D, and TP53) were compared between women with ILC and unaffected female controls and between women with ILC and infiltrating ductal carcinoma (IDC). RESULTS: The frequency of PVs in breast cancer predisposition genes among women with ILC was 6.5% in the clinical cohort and 5.2% in the population-based cohort. In case-control analysis, CDH1 and BRCA2 PVs were associated with high risks of ILC (odds ratio [OR] > 4) and CHEK2, ATM, and PALB2 PVs were associated with moderate (OR = 2-4) risks. BRCA1 PVs and CHEK2 p.Ile157Thr were not associated with clinically relevant risks (OR < 2) of ILC. Compared with IDC, CDH1 PVs were > 10-fold enriched, whereas PVs in BRCA1 were substantially reduced in ILC. CONCLUSION: The study establishes that PVs in ATM, BRCA2, CDH1, CHEK2, and PALB2 are associated with an increased risk of ILC, whereas BRCA1 PVs are not. The similar overall PV frequencies for ILC and IDC suggest that cancer histology should not influence the decision to proceed with genetic testing. Similar to IDC, multigene panel testing may be appropriate for women with ILC, but CDH1 should be specifically discussed because of low prevalence and gastric cancer risk.

Genome-wide homozygosity and risk of four non-Hodgkin lymphoma subtypes.

AIM: Recessive genetic variation is thought to play a role in non-Hodgkin lymphoma (NHL) etiology. Runs of homozygosity (ROH), defined based on long, continuous segments of homozygous SNPs, can be used to estimate both measured and unmeasured recessive genetic variation. We sought to examine genome-wide homozygosity and NHL risk. METHODS: We used data from eight genome-wide association studies of four common NHL subtypes: 3061 chronic lymphocytic leukemia (CLL), 3814 diffuse large B-cell lymphoma (DLBCL), 2784 follicular lymphoma (FL), and 808 marginal zone lymphoma (MZL) cases, as well as 9374 controls. We examined the effect of homozygous variation on risk by: (1) estimating the fraction of the autosome containing runs of homozygosity (FROH); (2) calculating an inbreeding coefficient derived from the correlation among uniting gametes (F3); and (3) examining specific autosomal regions containing ROH. For each, we calculated beta coefficients and standard errors using logistic regression and combined estimates across studies using random-effects meta-analysis. RESULTS: We discovered positive associations between FROH and CLL (ß = 21.1, SE = 4.41, P = 1.6 × 10-6) and FL (ß = 11.4, SE = 5.82, P = 0.02) but not DLBCL (P = 1.0) or MZL (P = 0.91). For F3, we observed an association with CLL (ß = 27.5, SE = 6.51, P = 2.4 × 10-5). We did not find evidence of associations with specific ROH, suggesting that the associations observed with FROH and F3 for CLL and FL risk were not driven by a single region of homozygosity. CONCLUSION: Our findings support the role of recessive genetic variation in the etiology of CLL and FL; additional research is needed to identify the specific loci associated with NHL risk.