[The association between baseline TPOAb and/or TgAb positivity and thyroid immune-related adverse events in patients with malignancies following treatment with immune checkpoint inhibitors]

Objective: To investigate the association between positive anti-thyroid peroxidase antibody (TPOAb) and/or anti-thyroglobulin antibody (TgAb) and the occurrence of thyroid immune-related adverse events (irAEs) in patients with malignant tumors who treated with immune checkpoint inhibitors (ICIs). Methods: A case-control study. A total of 116 patients with malignant tumor who received ICIs treatment and underwent thyroid function evaluation at Peking Union Medical College Hospital from January 2017 to April 2023 were enrolled retrospectively, including 77 males and 39 females, with a median age of (M(Q1, Q3)) 63.0 (55.0, 70.0) years. The patients were divided into the euthyroid group (n=58) and the thyroid irAEs group (n=58) according to whether thyroid irAEs occurred after ICIs treatment. The clinical characteristics and baseline anti-thyroid antibodies associated with the occurrence of thyroid irAEs after ICIs treatment in patients with malignant tumors were evaluated. Variables with statistical significance in univariate analysis were included in multivariate logistic regression model to analyze the risk factors for thyroid irAEs in patients with malignant tumors who received ICIs treatment. Results: In irAEs group, therewore 4 (3.4%) cases of clinical thyrotoxicosis, 23(19.8%) cases of subclinical thyrotoxicosis, 23 (19.8%) cases of clinical hypothyroidism, and 8(6.9%) cases of subclinical hypothyroidism. The positive rate of anti-thyroid antibodies at baseline in the thyrioid irAEs group was higher than that in the euthyroid group［16/58（27.6%）vs 3/58（5.2%），P=0.001］. After at least one course of ICIs treatment, the incidence of thyroid irAEs in patients with positive anti-thyroid antibodies at baseline was 84.2% (16/19), whereas it was 43.3% (42/97) in patients with negative anti-thyroid antibodies(P=0.001). Univariate logistic regression analysis showed that gender (OR=2.812, 95%CI:1.257-6.293), baseline thyroid autoantibodies were positive (OR=6.984, 95%CI: 1.909-25.547), baseline TgAb positivity (OR=8.909, 95%CI: 1.923-41.280), and baseline TPOAb positivity (OR=7.304, 95%CI: 1.555-34.308) were associated with thyroid irAEs (all P<0.05). Multivariate logistic regression analysis indicated that baseline TgAb positivity (OR=7.637, 95%CI: 1.617-36.072) was a risk factor for thyroid irAEs (P=0.01). Conclusions: The incidence of thyroid irAEs is higher in patients who are positive for baseline TPOAb and/or TgAb compared to those who are negative for TPOAb and TgAb. Patients with positive TgAb at baseline are at high risk of developing thyroid irAEs.

Stronger wind, smaller tree: Testing tree growth plasticity through a modeling approach

Plants exhibit plasticity in response to various external conditions, characterized by changes in physiological and morphological features. Although being non-negligible, compared to the other environmental factors, the effect of wind on plant growth is less extensively studied, either experimentally or computationally. This study aims to propose a modeling approach that can simulate the impact of wind on plant growth, which brings a biomechanical feedback to growth and biomass distribution into a functional-structural plant model (FSPM). Tree reaction to the wind is simulated based on the hypothesis that plants tend to fit in the environment best. This is interpreted as an optimization problem of finding the best growth-regulation sink parameter giving the maximal plant fitness (usually seed weight, but expressed as plant biomass and size). To test this hypothesis in silico, a functional-structural plant model, which simulates both the primary and secondary growth of stems, is coupled with a biomechanical model which computes forces, moments of forces, and breakage location in stems caused by both wind and self-weight increment during plant growth. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to maximize the multi-objective function (stem biomass and tree height) by determining the key parameter value controlling the biomass allocation to the secondary growth. The digital trees show considerable phenotypic plasticity under different wind speeds, whose behavior, as an emergent property, is in accordance with experimental results from works of literature: the height and leaf area of individual trees decreased with wind speed, and the diameter at the breast height (DBH) increased at low-speed wind but declined at higher-speed wind. Stronger wind results in a smaller tree. Such response of trees to the wind is realistically simulated, giving a deeper understanding of tree behavior. The result shows that the challenging task of modeling plant plasticity may be solved by optimizing the plant fitness function. Adding a biomechanical model enriches FSPMs and opens a wider application of plant models.

Genomic origin and EGFR-TKI treatments of pulmonary adenosquamous carcinoma

BACKGROUND

Adenosquamous carcinoma (ASC) of the lung is a heterogeneous disease that is composed of both adenocarcinoma components (ACC) and squamous cell carcinoma components (SCCC). Their genomic profile, genetic origin, and clinical management remain controversial.

PATIENTS AND METHODS

Resected ASC and metastatic tumor in regional lymph nodes (LNs) were collected. The ACC and SCCC were separated by microdissection of primary tumor. The 1021 cancer-related genes were evaluated by next-generation sequencing independently in ACC and SCCC and LNs. Shared and private alterations in the two components were investigated. In addition, genomic profiles of independent cohorts of adenocarcinomas and squamous cell carcinomas were examined for comparison. We have also carried out a retrospective study of ASCs with known EGFR mutation status from 11 hospitals in China for their clinical outcomes.

RESULTS

The most frequent alterations in 28 surgically resected ASCs include EGFR (79%), TP53 (68%), MAP3K1 (14%) mutations, EGFR amplifications (32%), and MDM2 amplifications (18%). Twenty-seven patients (96%) had shared variations between ACC and SCCC, and pure SCCC metastases were not found in metastatic LNs among these patients. Only one patient with geographically separated ACC and SCCC had no shared mutations. Inter-component heterogeneity was a common genetic event of ACC and SCCC. The genomic profile of ASC was similar to that of 170 adenocarcinomas, but different from that of 62 squamous cell carcinomas. The incidence of EGFR mutations in the retrospective analysis of 517 ASCs was 51.8%. Among the 129 EGFR-positive patients who received EGFR-TKIs, the objective response rate was 56.6% and the median progression-free survival was 10.1 months (95% confidence interval: 9.0-11.2).

CONCLUSIONS

The ACC and SCCC share a monoclonal origin, a majority with genetically inter-component heterogeneity. ASC may represent a subtype of adenocarcinoma with EGFR mutation being the most common genomic anomaly and sharing similar efficacy to EGFR TKI.

Managing Traditional Solar Greenhouse With CPSS: A Just-for-Fit Philosophy

The profit of greenhouse production is influenced by management activities (e.g., environmental control and plantation scheduling) as well as social conditions (e.g., price fluctuation). In China, the prevailing horticultural facility is the traditional solar greenhouse. The key existing problem is the lack of knowledge of growers, which in turn leads to inefficient management, low production, or unsalable products. To secure effective greenhouse management, the production planning system must account for the crop growing environment, grower's activities, and the market. This paper presents an agricultural cyber-physical-social system (CPSS) serving agricultural production management, with a case study on the solar greenhouse. The system inputs are derived from social and physical sensors, with the former collecting the price of agricultural products in a wholesale market, and the latter collecting the necessary environmental data in the solar greenhouse. Decision support for the cropping plan is provided by the artificial system, computational experiment, and parallel execution-based method, with description intelligence for estimating the crop development and harvest time, prediction intelligence for optimizing the planting time and area according to the expected targets (stable production or maximum gross profit), and prescription intelligence for online system training. The presented system fits the current technical and economic situation of horticulture in China. The application of agricultural CPSS could decrease waste in labor or fertilizer and support sustainable agricultural production.