Cannabis is cultivated for therapeutic and recreational purposes where delta-9 tetrahydrocannabinol (THC) is a main target for its therapeutic effects. As the global cannabis industry and research into cannabinoids expands, more efficient and cost-effective analysis methods for determining cannabinoid concentrations will be beneficial to increase efficiencies and maximize productivity. The utilization of machine learning tools to develop near-infrared (NIR) spectroscopy-based prediction models, which have been validated from accurate and sensitive chemical analysis, such as gas chromatography (GC) or liquid chromatography mass spectroscopy (LCMS), is essential. Previous research on cannabinoid prediction models targeted decarboxylated cannabinoids, such as THC, rather than the naturally occurring precursor, tetrahydrocannabinolic acid (THCA), and utilize finely ground cannabis inflorescence. The current study focuses on building prediction models for THCA concentrations in whole cannabis inflorescences prior to harvest, by employing non-destructive screening techniques so cultivators may rapidly characterize high-performing cultivars for chemotype in real time, thus facilitating targeted optimization of crossbreeding efforts. Using NIR spectroscopy and LCMS to create prediction models we can differentiate between high-THCA and even ratio classes with 100% prediction accuracy. We have also developed prediction models for THCA concentration with a = 0.78 with a prediction error average of 13%. This study demonstrates the viability of a portable handheld NIR device to predict THCA concentrations on whole cannabis samples before harvest, allowing the evaluation of cannabinoid profiles to be made earlier, therefore increasing high-throughput and rapid capabilities.
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http://dx.doi.org/10.3390/s24165081 | DOI Listing |
Front Oncol
December 2024
Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Background: For esophageal squamous cell carcinoma (ESCC), universally accepted pathological criteria for classification by differentiation degree are lacking. Tumor budding, single-cell invasion, and nuclear grade, recognized as prognostic factors in other carcinomas, have rarely been investigated for their correlation with differentiation and prognosis in ESCC. This study aims to determine if pathological findings can predict differentiation degree and prognosis in ESCC.
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December 2024
Institute for Head and Neck Studies and Education, University of Birmingham, Birmingham, United Kingdom.
Background: The limitations of the traditional TNM system have spurred interest in multivariable models for personalized prognostication in laryngeal and hypopharyngeal cancers (LSCC/HPSCC). However, the performance of these models depends on the quality of data and modelling methodology, affecting their potential for clinical adoption. This systematic review and meta-analysis (SR-MA) evaluated clinical predictive models (CPMs) for recurrence and survival in treated LSCC/HPSCC.
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December 2024
Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
Background: This study aimed to develop and validate a multiregional radiomic-based composite model to predict symptomatic radiation pneumonitis (SRP) in non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT).
Materials And Methods: 189 patients from two institutions were allocated into training, internal validation and external testing cohorts. The associations between the SRP and clinic-dosimetric factors were analyzed using univariate and multivariate regression.
Front Oncol
December 2024
Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Objective: To evaluate the efficacy of a machine learning model for predicting prostate-specific antigen (PSA) persistence after radical prostatectomy (RP).
Methods: Data from 470 patients who underwent RP at the Affiliated Hospital of Qingdao University from January 2018 to June 2021 were retrospectively analyzed. Ten risk factors, including age, body mass index (BMI), preoperative PSA, biopsy Gleason score, total prostate specific antigen density (PSAD), clinical tumor stage, clinical lymph node status, seminal vesicle invasion, capsular invasion and positive surgical margin, were included in the analysis.
Open Med (Wars)
December 2024
Department of Anesthesiology, Shanghai United Family Hospital, Shanghai 200050, China.
Background: Postoperative cognitive dysfunction (POCD) frequently occurs following endovascular therapy for acute ischemic stroke (AIS). Given the complexity of predicting AIS clinically, there is a pressing need to develop a preemptive prediction model and investigate the impact of anesthesia depth on AIS.
Methods: A total of 333 patients diagnosed with AIS were included in the study, comprising individuals with non-POCD ( = 232) or POCD ( = 101).
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