DiffPROTACs is a deep learning-based generator for proteolysis targeting chimeras.

PROteolysis TArgeting Chimeras (PROTACs) has recently emerged as a promising technology. However, the design of rational PROTACs, especially the linker component, remains challenging due to the absence of structure-activity relationships and experimental data. Leveraging the structural characteristics of PROTACs, fragment-based drug design (FBDD) provides a feasible approach for PROTAC research. Concurrently, artificial intelligence-generated content has attracted considerable attention, with diffusion models and Transformers emerging as indispensable tools in this field. In response, we present a new diffusion model, DiffPROTACs, harnessing the power of Transformers to learn and generate new PROTAC linkers based on given ligands. To introduce the essential inductive biases required for molecular generation, we propose the O(3) equivariant graph Transformer module, which augments Transformers with graph neural networks (GNNs), using Transformers to update nodes and GNNs to update the coordinates of PROTAC atoms. DiffPROTACs effectively competes with existing models and achieves comparable performance on two traditional FBDD datasets, ZINC and GEOM. To differentiate the molecular characteristics between PROTACs and traditional small molecules, we fine-tuned the model on our self-built PROTACs dataset, achieving a 93.86% validity rate for generated PROTACs. Additionally, we provide a generated PROTAC database for further research, which can be accessed at https://bailab.siais.shanghaitech.edu.cn/service/DiffPROTACs-generated.tgz. The corresponding code is available at https://github.com/Fenglei104/DiffPROTACs and the server is at https://bailab.siais.shanghaitech.edu.cn/services/diffprotacs.

Risk management of sports service supply chain using fuzzy comprehensive evaluation and intelligent neural network.

The sports service supply chain faces various potential risks, such as market fluctuations, logistics issues, and partner uncertainties. To address these risks effectively, this study employs a combination of fuzzy comprehensive evaluation (FCE) methods and intelligent neural networks to create an innovative risk management framework. By considering diverse uncertainties and leveraging the analytical power of intelligent neural networks, this study aims to optimize the operation of the sports service supply chain and explore the risk factors within the public service supply chain of stadiums. This framework provides policy references to promote the healthy and sustainable development of the sports service industry. The main empirical findings, based on a representative survey of experts in China, are as follows: (1) When determining the weights of risk indicators for managing the public service supply chain of stadiums using the FCE method, the customer risk indicator is of paramount importance, with a weight of 0.286, accounting for 95.2 % of the total significance; and (2) In evaluating various risk indicators of the public service supply chain of stadiums through the neural network method, the customer risk indicator scores the highest, achieving a score of 76.02. Notably, the customer complaint risk indicator scores slightly higher at 79.33. Based on these findings, the study recommends focusing on enhancing customer experience within risk management strategies. Additionally, it suggests strengthening the supervision of platforms and third-party activities to ensure the stability and efficient operation of the stadium service supply chain. This study aims to provide theoretical support and reference indicators for evaluating the public service capabilities of stadiums.

Towards higher load capacity: innovative design of a robotic hand with soft jointed structure.

In this paper, the innovative design of a robotic hand with soft jointed structure is carried out and a tendon-driven mechanism, a master-slave motor coordinated drive mechanism, a thumb coupling transmission mechanism and a thumb steering mechanism are proposed. These innovative designs allow for more effective actuation in each finger, enhancing the load capacity of the robotic hand while maintaining key performance indicators such as dexterity and adaptability. A mechanical model of the robotic finger was made to determine the application limitations and load capacity. The robotic hand was then prototyped for a set of experiments. The experimental results showed that the proposed theoretical model were reliable. Also, the fingertip force of the robotic finger could reach up to 10.3N, and the load force could reach up to 72.8N. When grasping target objects of different sizes and shapes, the robotic hand was able to perform the various power grasping and precision grasping in the Cutkosky taxonomy. Moreover, the robotic hand had good flexibility and adaptability by means of adjusting the envelope state autonomously. .

Negative causal exploration of systemic sclerosis: a Mendelian randomization analysis.

Systemic sclerosis (SSc), also known as scleroderma, is an autoimmune-related connective tissue disease with a complex and unknown pathophysiological mechanism with genes association. Several articles have reported a high prevalence of thyroid disease in SSc patients, while one study suggested a potential contribution of appendicitis to the development of SSc. To investigate this causal association, we conducted Mendelian randomization (MR) analysis using instrumental variables (IVs) to assess exposure and outcome. In the MR study involving two cohorts, all analyses were conducted using the TwoSampleMR package in R (version 4.3.0). Single nucleotide polymorphisms (SNPs) meeting a statistically significant threshold of 5E-08 were included in the analysis. Multiple complementary approaches including MR-IVW, MR-Egger, weighted median, simple mode, and weighted mode were employed to estimated the relationship between the exposure and outcome. Leave-one-out analysis and scatter plots were utilized for further investigation. Based on the locus-wide significance level, all of the MR analysis consequences manifested no causal association between the risk of appendicitis with SSc (IVW OR 0.319, 95% CI 0.063-14.055, P = 0.966). Negative causal effects of autoimmune thyroiditis (AT) on SSc (IVW OR 0.131, 95% CI 0.816-1.362, P = 0.686), Graves' disease (GD) on SSc (IVW OR 0.097, 95% CI 0.837-1.222, P = 0.908), and hypothyroidism on SSc (IVW OR 1.136, 95% CI 0.977-1.321, P = 0.096) were derived. The reverse MR revealed no significant causal effect of SSc on thyroid disease. According to the sensitivity analysis, horizontal pleiotropy was unlikely to distort the causal estimates. The consequences indicated no significant association between AT, GD, and hypothyroidism with SSc. Similarly, there was no observed relationship with appendicitis.

Advances in combined neuroendocrine carcinoma of lung cancer.

Lung cancer incidence and mortality rates are increasing worldwide, posing a significant public health challenge and an immense burden to affected families. Lung cancer encompasses distinct subtypes, namely, non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC). In clinical investigations, researchers have observed that neuroendocrine tumors can be classified into four types: typical carcinoid, atypical carcinoid, small-cell carcinoma, and large-cell neuroendocrine carcinoma based on their unique features. However, there exist combined forms of neuroendocrine cancer. This study focuses specifically on combined pulmonary carcinomas with a neuroendocrine component. In this comprehensive review article, the authors provide an overview of combined lung cancers and present two pathological images to visually depict these distinctive subtypes.

Development and external validation of a quantitative diagnostic model for malignant gastric lesions in clinical opportunistic screening: A multicenter real-world study.

BACKGROUND: Clinical opportunistic screening is a cost-effective cancer screening modality. This study aimed to establish an easy-to-use diagnostic model serving as a risk stratification tool for identification of individuals with malignant gastric lesions for opportunistic screening. METHODS: We developed a questionnaire-based diagnostic model using a joint dataset including two clinical cohorts from northern and southern China. The cohorts consisted of 17,360 outpatients who had undergone upper gastrointestinal endoscopic examination in endoscopic clinics. The final model was derived based on unconditional logistic regression, and predictors were selected according to the Akaike information criterion. External validation was carried out with 32,614 participants from a community-based randomized controlled trial. RESULTS: This questionnaire-based diagnostic model for malignant gastric lesions had eight predictors, including advanced age, male gender, family history of gastric cancer, low body mass index, unexplained weight loss, consumption of leftover food, consumption of preserved food, and epigastric pain. This model showed high discriminative power in the development set with an area under the receiver operating characteristic curve (AUC) of 0.791 (95% confidence interval [CI]: 0.750-0.831). External validation of the model in the general population generated an AUC of 0.696 (95% CI: 0.570-0.822). This model showed an ideal ability for enriching prevalent malignant gastric lesions when applied to various scenarios. CONCLUSION: This easy-to-use questionnaire-based model for diagnosis of prevalent malignant gastric lesions may serve as an effective prescreening tool in clinical opportunistic screening for gastric cancer.

Effectiveness of Endoscopic Screening on Esophageal Cancer Incidence and Mortality: A 9-Year Report of the Endoscopic Screening for Esophageal Cancer in China (ESECC) Randomized Trial.

PURPOSE: To evaluate the effectiveness of endoscopic screening against incidence of and mortality from esophageal squamous cell carcinoma (ESCC). METHODS: From January 2012 to September 2016, we conducted a community-based cluster randomized controlled trial involving permanent residents age 45-69 years in a high-risk region for ESCC in northern China. A total of 668 targeted villages were randomly assigned in a 1:1 ratio to the screening group (offered Lugol's chromoendoscopy) or control group (no screening). Intention-to-treat and per-protocol analyses were performed to compare esophageal cancer (EC) incidence and mortality between the two groups. The per-protocol analysis adjusted for nonadherence to the screening procedure. RESULTS: A total of 33,847 participants were included in the analysis: 17,104 in the screening group, 15,165 (88.7%) of whom underwent screening, and 16,743 in the control group. During a maximum follow-up of 9 years, EC incidence in the screening and control groups were 60.9 and 72.5 per 100,000 person-years, respectively; mortality in the screening and control groups were 29.7 and 32.4 per 100,000 person-years, respectively. Compared with the control group, the incidence and mortality of the screening group reduced by 19% (adjusted hazard ratio [aHR], 0.81 [95% CI, 0.60 to 1.09]) and 18% (aHR, 0.82 [95% CI, 0.53 to 1.26]), respectively, in the intention-to-treat analysis; and by 22% (aHR, 0.78 [95% CI, 0.56 to 1.10]) and 21% (aHR, 0.79 [95% CI, 0.49 to 1.30]), respectively, in the per-protocol analysis. CONCLUSION: With a 9-year follow-up, our trial suggests that chromoendoscopic screening induces modest reductions in EC incidence and mortality. A more efficient strategy for EC screening and subsequent patient management should be established to guarantee the effectiveness of endoscopic screening.

Construction and validation of serum Metabolic Risk Score for early warning of malignancy in esophagus.

Using noninvasive biomarkers to identify high-risk individuals prior to endoscopic examination is crucial for optimization of screening strategies for esophageal squamous cell carcinoma (ESCC). We conducted a nested case-control study based on two community-based screening cohorts to evaluate the warning value of serum metabolites for esophageal malignancy. The serum samples were collected at enrollment when the cases had not been diagnosed. We identified 74 differential metabolites and two prominent perturbed metabolic pathways, and constructed Metabolic Risk Score (MRS) based on 22 selected metabolic predictors. The MRS generated an area under the receiver operating characteristics curve (AUC) of 0.815. The model performed well for the within-1-year interval (AUC: 0.868) and 1-to-5-year interval (AUC: 0.845) from blood draw to diagnosis, but showed limited ability in predicting long-term cases (>5 years). In summary, the MRS could serve as a potential early warning and risk stratification tool for establishing a precision strategy of ESCC screening.