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With the development of autonomous driving and the emergence of various intelligent traffic scenarios, object detection technology based on deep learning is more and more widely applied to real traffic scenarios. Commonly used detection devices include LiDAR and cameras. Since the implementation of traffic scene target detection technology requires mass production, the advantages of millimeter-wave radar have emerged, such as low cost and no interference from the external environment. The performance of LiDAR and cameras is greatly reduced due to their sensitivity to light, which affects target detection at night and in bad weather. However, millimeter-wave radar can overcome the influence of these harsh environments and has a great auxiliary effect on safe driving on the road. In this work, we propose a deep-learning-based object detection method considering the radar range–Doppler spectrum in traffic scenarios. The algorithm uses YOLOv8 as the basic architecture, makes full use of the time series characteristics of range–Doppler spectrum data in traffic scenarios, introduces the ConvLSTM network, and exerts the ability to process time series data. In order to improve the model’s ability to detect small objects, an efficient and lightweight Efficient Channel Attention (ECA) module is introduced. Through extensive experiments, our model shows better performance on two publicly available radar datasets, CARRADA and RADDet, compared to other state-of-the-art methods. Compared with other mainstream methods that can only achieve 30–60% mAP performance when the IOU is 0.3, our model can achieve 74.51% and 75.62% on the RADDet and CARRADA datasets, respectively, and has better robustness and generalization ability.

In smart transportation, assisted driving relies on data integration from various sensors, notably LiDAR and cameras. However, their optical performance can degrade under adverse weather conditions, potentially compromising vehicle safety. Millimeter-wave radar, which can overcome these issues more economically, has been re-evaluated. Despite this, developing an accurate detection model is challenging due to significant noise interference and limited semantic information. To address these practical challenges, this paper presents the TC–Radar model, a novel approach that synergistically integrates the strengths of transformer and the convolutional neural network (CNN) to optimize the sensing potential of millimeter-wave radar in smart transportation systems. The rationale for this integration lies in the complementary nature of CNNs, which are adept at capturing local spatial features, and transformers, which excel at modeling long-range dependencies and global context within data. This hybrid approach allows for a more robust and accurate representation of radar signals, leading to enhanced detection performance. A key innovation of our approach is the introduction of the Cross-Attention (CA) module, which facilitates efficient and dynamic information exchange between the encoder and decoder stages of the network. This CA mechanism ensures that critical features are accurately captured and transferred, thereby significantly improving the overall network performance. In addition, the model contains the dense information fusion block (DIFB) to further enrich the feature representation by integrating different high-frequency local features. This integration process ensures thorough incorporation of key data points. Extensive tests conducted on the CRUW and CARRADA datasets validate the strengths of this method, with the model achieving an average precision (AP) of 83.99% and a mean intersection over union (mIoU) of 45.2%, demonstrating robust radar sensing capabilities.

Cutaneous adverse events (CAEs) after treatment with BRAF and MEK inhibitors in patients with melanoma remain incompletely characterized. To determine the association of BRAF and MEK inhibitor treatment with CAEs in patients with melanoma compared with BRAF inhibitor alone. PubMed, Cochrane, Embase and Web of Science were systematically searched for BRAF and MEK inhibitors from database inception through 10 May 2024. Randomized clinical trials reporting on CAEs in patients with melanoma being treated with BRAF and MEK inhibitors compared with patients with melanoma being treated with BRAF inhibitor monotherapy were selected. Pooled Risk ratios (RRs) and 95% CIs were determined using random-effects analyses. The selected end points were alopecia, cutaneous squamous-cell carcinoma, hyperkeratosis, keratoacanthoma, palmoplantar erythrodysaesthesia syndrome, palmoplantar keratoderma, rash, photosensitivity reaction, and skin papilloma. All-grade and high-grade (≥3) CAEs were recorded. Comparing with BRAF and MEK inhibitors, treatment with BRAF inhibitors alone was associated with an increased risk of rash (RR, 0.73; 95% CI, 0.54-0.99; = 0.039; I = 88%), alopecia (RR, 0.28; 95% CI, 0.20-0.41; P < 0.001; I = 76%), hyperkeratosis (RR, 0.30; 95% CI, 0.22-0.41; P < 0.001; I = 56%), palmoplantar erythrodysaesthesia syndrome (RR, 0.21; 95% CI, 0.10-0.47; P < 0.001; I = 81%), palmoplantar keratoderma (RR, 0.39; 95% CI, 0.26-0.57; P < 0.001; I = 29%), Skin papilloma (RR, 0.25; 95% CI, 0.12-0.52; P < 0.001; I = 77%), cutaneous squamous-cell carcinoma (RR, 0.21; 95% CI, 0.11-0.42; P < 0.001; I = 50%), and keratoacanthoma (RR, 0.22; 95% CI, 0.12-0.40; P < 0.001; I = 0%). Therapy with BRAF and MEK inhibitors was associated with a lower risk of CAEs, especially rash, alopecia, hyperkeratosis, palmoplantar erythrodysaesthesia syndrome, palmoplantar keratoderma, skin papilloma, cutaneous squamous-cell carcinoma, and keratoacanthoma, compared with BRAF inhibitor alone. The risks of photosensitivity reaction was similar between the assessed groups. The findings may help to balance between beneficial melanoma treatment and cutaneous morbidity and mortality.

Background Osteoarthritis (OA) of the knee is a prevalent chronic degenerative joint condition that is having a growing impact on a global scale., posing a challenge in diagnosis which is often reliant on time-consuming and error-prone visual analysis by physicians. There is a critical need for an automated, efficient, and accurate diagnostic method to improve early detection and treatment. Methods We developed a novel Convolutional Neural Network (CNN) module, Dense Multi-Scale (DMS), an advancement over Multi-Scale Convolution (MSC). This module utilizes dense connections in convolutions of varying sizes (1 × 1, 3 × 3, 5 × 5) and across layers, enhancing feature reuse and complexity recognition, thereby improving recognition capabilities. Dense connections also facilitate deeper network architecture and mitigate gradient vanishing problems. We compared our model with a standard baseline model and validated it using an unseen-data test set. Results The DMS model exhibited exceptional performance in unseen-data tests, achieving 73.00% average accuracy (ACC) and 92.73% area under the curve (AUC), surpassing the baseline model’s (DenseNet) 63.52% ACC and 88.76% AUC. This highlights the DMS model’s superior predictive capability for knee OA. Conclusion The DMS model presents a significant advancement in predicting and grading knee OA, holding substantial clinical importance. It promises to aid radiologists in accurate diagnosis and grading, and in choosing appropriate treatments, thereby reducing misdiagnosis and patient burden.

Observational studies have suggested an association between inflammatory markers and low back pain (LBP), but the causal relationship between these factors remains uncertain. We conducted a bidirectional two-sample Mendelian randomization analysis (MR) study to investigate whether there is a causal relationship between inflammatory markers and low back pain. We obtained genetic data for CRP, along with its upstream inflammatory markers IL-6, IL-8, and IL-10, as well as low back pain from publicly available genome-wide association studies (GWAS). We applied several MR methods, including inverse variance weighting, weighted median, MR-Egger, Wald Ratio, and MR-PRESSO, to test for causal relationships. Sensitivity analyses were also conducted to assess the robustness of the results. Our analyses utilizing the Inverse Variance Weighted (IVW) method, the MR-Egger method, and the weighted median method indicated that IL-6 may be associated with an increased risk of LBP (Effect Size: -0.009, 95% Confidence Interval: -0.013-0.006, p = 9.16e-08); however, in the reverse direction, there was no significant causal effect of LBP on inflammatory markers. Our study used a Mendelian randomization approach and found that elevated IL-6 levels may reduce the risk of LBP.

With the development of intelligent transportation, radar and communication on moving platforms are competing for the spectrum. In this paper, we propose and demonstrate a new algorithmic framework for radar-communication spectral coexistence system on moving platform with mutual interference suppression, in which communication rate and the radar signal-to-interference-plus-noise ratio (SINR) are simultaneously optimized, under the energy constraints for the two systems and the radar constant modulus constraint. The radar spatial-temporal filter at the receiver and transmitting waveform are optimized, while the codebook matrix is optimized for the communication system. To cope with the established non-convex problem with triplet variables, we decouple the original problem into multiple subproblems, for which an alternating algorithm based on iterative procedures is derived with lower computational complexity. Specifically, the subproblems of communication codebook and radar filter design are convex and the closed-form solutions can be easily obtained, while the radar waveform optimization is non-convex. Then we propose a novel scheme by exploiting the alternating direction method of multipliers (ADMM) based on minorization-maximization (MM) framework. Finally, to reveal the effectiveness of the proposed algorithm in different scenarios, numerical results are provided.

This study systematically evaluates the effectiveness of combining problem-based learning with the seminar teaching method and the traditional lecture-based learning model in medical education by meta-analysis. A computer-based search of major domestic and international literature databases was conducted, including PubMed, EMBASE, Web of Science Core Collection, Cochrane Library, China National Knowledge Infrastructure(CNKI), Wanfang Database, VIP Chinese Science and Technology Periodicals Database, and China Biology Medicine disk (CBMdisc). The search period spanned from the inception of the databases to 30 August 2024. Quantitative synthesis was performed using the RevMan V.5.4 software, following the Cochrane Reviewer's Handbook guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. A total of 13 articles involving 857 medical students were included. The meta-analysis results revealed statistically significant differences between the experimental and control groups in the following areas: theoretical knowledge scores (MD = 4.99, 95% CI: 4.29-5.69, < 0.00001); clinical skill scores (MD = 4.98,95% CI: 4.21-5.75, < 0.00001); case analysis ability (SMD = 3.07, 95% CI: 2.66-3.47, < 0.00001); Learning interest (SMD = 2.46, 95% CI: 1.89-3.03, < 0.00001); Active learning (SMD = 3.26, 95% CI: 2.66-3.85, < 0.00001); teamwork abilities (SMD = 1.66, 95% CI: 1.27-2.05, < 0.00001); students' research and academic ability (MD = 26.85, 95% CI: 24.79-28.91, < 0.00001). The experimental group demonstrated superior outcomes in all areas compared to the control group. This meta-analysis showed that the integration of problem-based learning and seminar teaching methods is an effective method for improving theoretical knowledge scores, clinical skill scores, case analysis ability, learning interest, active learning, teamwork abilities and research and academic ability.

[This corrects the article DOI: 10.3389/fimmu.2023.1174656.].

The impact of knee osteoarthritis on individuals' daily functioning is significant. In recent years, Vitamin D supplements cure osteoarthritis has garnered attention from medical professionals and patients due to its simplicity and portability. Several systematic reviews (SRs) and meta-analyses (MAs) have examined the efficacy of vitamin D supplementation for knee osteoarthritis, yet there is variability in their methodology and quality. To search, gather, and analyze data on the characteristics and quantitative results of SR/MA in patients with KOA treated with Vitamin D supplementation, and objectively evaluate the efficacy of supplements. Then, provides clinical evidence and recommendations the clinical use of vitamin D supplementation. Two individuals reviewed and collected data from four databases until October 2023. AMSTAR-2, ROBIS, PRISMA 2020, and GRADE tools were used to evaluate the methodological quality, bias risk, reporting quality, and evidence strength of all SR/MA. Additionally, we applied the corrected covered area (CCA) method to measure overlap in randomized controlled trials (RCTs) cited among the SR/MA. 3 SRs and 6 MAs were included in the analysis: 3 studies were low quality by AMSTAR-2, and 6 studies were very low quality. According to ROBIS, 6 studies were high-risk and 3 were low-risk. In PRISMA 2020 reporting quality, most studies showed deficiencies in comprehensive literature search strategy, reasons for literature exclusion, data preprocessing for meta-analysis, exploration of reasons for heterogeneity, sensitivity analysis, publication bias, and disclosure of funding and conflicts of interest. Grading the quality of evidence in GRADE consisted of 5 items of moderate quality, 14 items of low quality, and 10 items of very low quality. Bias risk and imprecision were the main factors for downgrading. The calculation of RCT overlap between SR/MA using CCA showed a high degree of overlap. Vitamin D supplementation may show potential efficacy in ameliorating symptoms of KOA. The evidence indicates that Vitamin D supplements for knee osteoarthritis can improve patients' Total WOMAC scores and synovial fluid volume in the joints. Nevertheless, due to the generally low quality of current studies, future research should prioritize improving the quality of primary studies to establish the efficacy of vitamin D supplementation for KOA with more robust scientific evidence. The protocol of this overview was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (https://www.crd.york.ac.uk/PROSPERO/) with the registration number CRD42024535841.

In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models. Simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data. Supplementary materials for this article are available online.