Application of Machine Learning and Data Mining in Medicine: Opportunities and Considerations

With the continuous development of information technology, machine learning and data mining have gradually found widespread applications across various industries. These technologies delve deeper into uncovering intrinsic patterns through the application of computer science. This trend is especially evident in today’s era of advanced artificial intelligence, which marks the anticipated third industrial revolution. By harnessing cutting-edge techniques such as multimodal large-scale models, artificial intelligence is profoundly impacting traditional scientific research methods. The use of machine learning and data mining techniques in medical research has a long-standing history. In addition to traditional methods such as logistic regression, decision trees, and Bayesian analysis, newer technologies such as neural networks, random forests, support vector machines, Histogram-based Gradient Boosting, XGBoost, LightGBM, and CatBoost have gradually gained widespread adoption. Each of these techniques has its own advantages and disadvantages, requiring careful selection based on the specific research objectives in clinical practice. Today, with the emergence of large language models such as ChatGPT 3.5, machine learning and data mining are gaining new meanings and application prospects. ChatGPT offers benefits such as optimized code algorithms and ease of use, saving time and enhancing efficiency for medical researchers. It is worth promoting the use of ChatGPT in clinical research

Bipolar-CMOS-DMOS Process-Based a Robust and High-Accuracy Low Drop-Out Regulator

A 40V BCD process high-accuracy and robust Low Drop-Out Regulator was proposed and tape-out in CSMC; the LDO was integrated in a LED Control and Driver SOC of outdoor applications. The proposed LDO converted the 12V~40V input power to 5V for the low voltage circuits inside the SOC. The robustness of LDO was important because the application condition of the SOC was bad. It was simulated in all process corner, -55℃~150℃ temperature and 12V~40V power voltage conditions. Simulation result shows that the LDO works robustly in conditions mentioned above. The default precision of LDO output voltage is ±2.75% max in all conditions, moreover, by utilizing a trim circuit in the feedback network, the precision can be improved to ±0.5% max after being trimmed by 3 bit digital trim signal Trim[3:1]. The total size of the proposed LDO is 135um*450um and the maximum current consumption is 284uA

Identification of risk factors for hypertension in overweight and obese people and analysis of risk factor interactions: an R-based analysis

ObjectiveThis study identified the independent risk factors for hypertension in overweight and obese people and also analyzed the interaction between the risk factors.MethodsA total of 5,098 overweight and obese people were enrolled in this study. First, the clinical metabolic characteristics of hypertension and control groups were compared. The logistic regression (LR) and classification and regression trees (CRT)-based decision tree (DT) models were used to screen the independent risk factors for hypertension in overweight and obese people. The multiplicative and additive scale analyses were used to analyze the two risk factors with interaction from the perspective of statistics and biological interaction. Finally, the receiver operating characteristic (ROC) and calibration curves were used to analyze the accuracy and identification ability of the LR and DT models.ResultsAge, UA, FPG, SBP, Cr, AST, TG, and FPG were higher in the hypertension group than in the control group (P < 0.05). The results of LR revealed that NAFLD, FPG, age, TG, LDL-c, UA, and Cr were positively correlated with hypertension in overweight and obese people, and GFR was negatively correlated with hypertension in overweight and obese people (P < 0.05). The DT model suggested that the risk factors of age, FPG, and UA interacted with each other. The multiplicative single and multiple factor analysis for FPG + UA, age + UA, age + FPG revealed a positive multiplicative interaction (P < 0.05, B ≠ 0, OR > 1). The additive single and multiple factor analysis for age + UA indicated a positive additive interaction. The ROC and calibration curve analysis indicated that the CRT decision tree, FPG + UA, age + UA, and age + FPG have certain accuracy and discrimination ability.ConclusionThe independent risk factors for hypertension in overweight and obese people included NAFLD, FPG, age, TG, LDL-c, UA, and Cr. Among these, age + UA exhibited synergistic interaction, thereby providing a reference for the prevention and control of hypertension in overweight and obese people

Lineage Tracing of Mammary Epithelial Cells Using Cell-Type-Specific Cre-Expressing Adenoviruses

Summary Lineage tracing using Cre/lox transgenic mice provides a powerful tool for studying normal mammary epithelial cell (MEC) development and the cellular origins of mammary tumors under physiological settings. However, generation of new transgenic mice for lineage-tracing purposes is often time consuming. Here, we report a lineage-tracing tool for MECs based on intraductal injection of lineage-specific Cre-expressing adenovirus (Ad-Cre). Using well-characterized promoters for Keratin 8 and Keratin 14, we generated lineage-specific Ad-Cre lines for luminal and basal MECs, respectively. By pulse-chase lineage tracing using these Ad-Cre lines, we showed that luminal and basal lineages are largely self-sustained and that IRS1 and IRS2 are essential for maintaining the basal lineage; we also showed that heterogeneous mammary tumors can be induced from luminal MECs in mice carrying the Etv6-NTRK3 fusion gene. Overall, we validated the Ad-Cre system as a promising and efficient tool for fate mapping of normal and malignant cells in adult tissues

Dense RGB SLAM with Neural Implicit Maps

There is an emerging trend of using neural implicit functions for map representation in Simultaneous Localization and Mapping (SLAM). Some pioneer works have achieved encouraging results on RGB-D SLAM. In this paper, we present a dense RGB SLAM method with neural implicit map representation. To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder. This design effectively fuses shape cues across different scales to facilitate map reconstruction. Our method simultaneously solves the camera motion and the neural implicit map by matching the rendered and input video frames. To facilitate optimization, we further propose a photometric warping loss in the spirit of multi-view stereo to better constrain the camera pose and scene geometry. We evaluate our method on commonly used benchmarks and compare it with modern RGB and RGB-D SLAM systems. Our method achieves favorable results than previous methods and even surpasses some recent RGB-D SLAM methods. Our source code will be publicly available.Comment: Accepted by ICLR 2023; Pre-Camera-Ready Versio

Diffusion-based Blind Text Image Super-Resolution

Recovering degraded low-resolution text images is challenging, especially for Chinese text images with complex strokes and severe degradation in real-world scenarios. Ensuring both text fidelity and style realness is crucial for high-quality text image super-resolution. Recently, diffusion models have achieved great success in natural image synthesis and restoration due to their powerful data distribution modeling abilities and data generation capabilities. In this work, we propose an Image Diffusion Model (IDM) to restore text images with realistic styles. For diffusion models, they are not only suitable for modeling realistic image distribution but also appropriate for learning text distribution. Since text prior is important to guarantee the correctness of the restored text structure according to existing arts, we also propose a Text Diffusion Model (TDM) for text recognition which can guide IDM to generate text images with correct structures. We further propose a Mixture of Multi-modality module (MoM) to make these two diffusion models cooperate with each other in all the diffusion steps. Extensive experiments on synthetic and real-world datasets demonstrate that our Diffusion-based Blind Text Image Super-Resolution (DiffTSR) can restore text images with more accurate text structures as well as more realistic appearances simultaneously.Comment: Accepted by CVPR202

Inhibition of cyclin-dependent kinase 7 down-regulates yes-associated protein expression in mesothelioma cells.

Cyclin-dependent kinase 7 (CDK7) is a protein kinase that plays a major role in transcription initiation. Yes-associated protein (YAP) is a main effector of the Hippo/YAP signalling pathway. Here, we investigated the role of CDK7 on YAP regulation in human malignant pleural mesothelioma (MPM). We found that in microarray samples of human MPM tissue, immunohistochemistry staining showed correlation between the expression level of CDK7 and YAP (n = 70, r = .513). In MPM cells, CDK7 expression level was significantly correlated with GTIIC reporter activity (r = .886, P = .019). Inhibition of CDK7 by siRNA decreased the YAP protein level and the GTIIC reporter activity in the MPM cell lines 211H, H290 and H2052. Degradation of the YAP protein was accelerated after CDK7 knockdown in 211H, H290 and H2052 cells. Inhibition of CDK7 reduced tumour cell migration and invasion, as well as tumorsphere formation ability. Restoration of the CDK7 gene rescued the YAP protein level and GTIIC reporter activity after siRNA knockdown in 211H and H2052 cells. Finally, we performed a co-immunoprecipitation analysis using an anti-YAP antibody and captured the CDK7 protein in 211H cells. Our results suggest that CDK7 inhibition reduces the YAP protein level by promoting its degradation and suppresses the migration and invasion of MPM cells. Cyclin-dependent kinase 7 may be a promising therapeutic target for MPM

Biomembrane-wrapped gene delivery nanoparticles for cancer therapy

As a promising strategy, gene delivery for cancer treatment accepts encouraging progress due to its high efficacy, low toxicity, and exclusive selectivity. However, the delivery efficiency, specific biological distribution, targeted uptake, and biosafety of naked nucleic acid agents still face serious challenges, which limit further clinical application. To overcome the above bottleneck, safe and efficient functional nanovectors are developed to improve the delivery efficiency of nucleic acid agents. In recent years, emerging membrane-wrapped biomimetic nanoparticles (MBNPs) based on the concept of “imitating nature” are well known for their advantages, such as low immunogenicity and long cycle time, and especially play a crucial role in improving the overall efficiency of gene delivery and reducing adverse reactions. Therefore, combining MBNPs and gene delivery is an effective strategy to enhance tumor treatment efficiency. This review presents the mechanism of gene therapy and the current obstacles to gene delivery. Remarkably, the latest development of gene delivery MBNPs and the strategies to overcome these obstacles are summarized. Finally, the future challenges and prospects of gene delivery MBNPs toward clinical transformation are introduced. The principal purpose of this review is to discuss the biomedical potential of gene delivery MBNPs for cancer therapy and to provide guidance for further enhancing the efficiency of tumor gene therapy