Improving the predictions of leaf photosynthesis during and after short-term heat stress with current rice models.

In response to increasing global warming, extreme heat stress significantly alters photosynthetic production. While numerous studies have investigated the temperature effects on photosynthesis, factors like vapour pressure deficit (VPD), leaf nitrogen, and feedback of sink limitation during and after extreme heat stress remain underexplored. This study assessed photosynthesis calculations in seven rice growth models using observed maximum photosynthetic rate (Pmax ) during and after short-term extreme heat stress in multi-year environment-controlled experiments. Biochemical models (FvCB-type) outperformed light response curve-based models (LRC-type) when incorporating observed leaf nitrogen, photosynthetically active radiation, temperatures, and intercellular CO2 concentration (Ci ) as inputs. Prediction uncertainty during heat stress treatment primarily resulted from variation in temperatures and Ci . Improving FVPD (the slope for the linear effect of VPD on Ci /Ca ) to be temperature-dependent, rather than constant as in original models, significantly improved Ci prediction accuracy under heat stress. Leaf nitrogen response functions led to model variation in leaf photosynthesis predictions after heat stress, which was mitigated by calibrated nitrogen response functions based on active photosynthetic nitrogen. Additionally, accounting for observed differences in carbohydrate accumulation between panicles and stems during grain filling improved the feedback of sink limitation, reducing Ci overestimation under heat stress treatments.

Analysis of white-light imaging-based features predictive for determination of lesion depths of superficial flat esophageal squamous cell carcinoma: a retrospective multicenter study from China.

OBJECTIVES: Endoscopic diagnosis of invasion depth of superficial esophageal squamous cell carcinoma (SESCC) by white-light imaging (WLI) modality remains difficult. This study aims to clarify WLI-based features which are predictive for invasion depth of SESCC. METHODS: A two-phase study was performed by enrolling 1288 patients with 1396 SESCC lesions. Endoscopic appearances, clinical characteristics and post-operative pathological outcomes were collected and reviewed. The association between lesion features and invasion depth were analyzed. A predictive nomogram was constructed for prediction of invasion depth. RESULTS: Among 1396 lesions in derivation and validation cohort, 1139 (81.6%), 194 (13.9%) and 63 (4.5%) lesions were diagnosed as lesions confined into the intraepithelium or the lamina propria mucosa (T1a-EP/LPM), lesions invading the muscularis mucosa (T1a-MM) or superficial submucosa (T1b-SM1) and tumor with moderate invasion into the submucosa or deeper submucosal invasion (≥ T1b-SM2), respectively. Lesion length > 2 cm (p < 0.001), wider circumferential extension (p < 0.001, 0.002 and 0.048 for > 3/4, 1/2-3/4 and 1/4-1/2 circumferential extension, respectively), surface unevenness (p < 0.001 for both type 0-IIa/0-IIc lesions and mixed type lesions), spontaneous bleeding (p < 0.001), granularity (p < 0.001) and nodules (p < 0.001) were identified as significant factors predictive for lesion depth. A nomogram based on these factors was constructed and the values of area under the Receiver Operating Characteristics curve were 0.89 and 0.90 in the internal and external patient cohort. CONCLUSIONS: Our study provides six WLI-based morphological features predicting for lesion depth of SESCC. Our findings will make endoscopic evaluation of invasion depth for SESCC more convenient by assessing these profiles.

Artificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white-light endoscopic images by using a generalized system.

BACKGROUND: The use of artificial intelligence (AI) assisted white light imaging (WLI) detection systems for superficial esophageal squamous cell carcinoma (SESCC) is limited by training with images from one specific endoscopy platform. METHODS: In this study, we developed an AI system with a convolutional neural network (CNN) model using WLI images from Olympus and Fujifilm endoscopy platforms. The training dataset consisted of 5892 WLI images from 1283 patients, and the validation dataset included 4529 images from 1224 patients. We assessed the diagnostic performance of the AI system and compared it with that of endoscopists. We analyzed the system's ability to identify cancerous imaging characteristics and investigated the efficacy of the AI system as an assistant in diagnosis. RESULTS: In the internal validation set, the AI system's per-image analysis had a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 96.64%, 95.35%, 91.75%, 90.91%, and 98.33%, respectively. In patient-based analysis, these values were 90.17%, 94.34%, 88.38%, 89.50%, and 94.72%, respectively. The diagnostic results in the external validation set were also favorable. The CNN model's diagnostic performance in recognizing cancerous imaging characteristics was comparable to that of expert endoscopists and significantly higher than that of mid-level and junior endoscopists. This model was competent in localizing SESCC lesions. Manual diagnostic performances were significantly improved with the assistance by AI system, especially in terms of accuracy (75.12% vs. 84.95%, p = 0.008), specificity (63.29% vs. 76.59%, p = 0.017) and PPV (64.95% vs. 75.23%, p = 0.006). CONCLUSIONS: The results of this study demonstrate that the developed AI system is highly effective in automatically recognizing SESCC, displaying impressive diagnostic performance, and exhibiting strong generalizability. Furthermore, when used as an assistant in the diagnosis process, the system improved manual diagnostic performance.

The influence of prescriber and patient gender on the prescription of benzodiazepines: results from the Florida Medicaid Dataset.

BACKGROUND: Benzodiazepine (BZD) prescription rates have increased over the past decade in the United States. Available literature indicates that sociodemographic factors may influence diagnostic patterns and/or prescription behaviour. Herein, the aim of this study is to determine whether the gender of the prescriber and/or patient influences BZD prescription. METHODS: Cross-sectional study using data from the Florida Medicaid Managed Medical Assistance Program from January 1, 2018 to December 31, 2018. Eligible recipients ages 18 to 64, inclusive, enrolled in the Florida Medicaid plan for at least 1 day, and were dually eligible. Recipients either had a serious mental illness (SMI), or non-SMI and anxiety. RESULTS: Total 125 463 cases were identified (i.e., received BZD or non-BZD prescription). Main effect of patient and prescriber gender was significant F(1, 125 459) = 0.105, P = 0 .745, partial η2 < 0.001. Relative risk (RR) of male prescribers prescribing a BZD compared to female prescribers was 1.540, 95% confidence intervals (CI) [1.513, 1.567], whereas the RR of male patients being prescribed a BZD compared to female patients was 1.16, 95% CI [1.14, 1.18]. Main effects of patient and prescriber gender were statistically significant F(1, 125 459) = 188.232, P < 0.001, partial η2 = 0.001 and F(1, 125 459) = 349.704, P < 0.001, partial η2 = 0.013, respectively. CONCLUSIONS: Male prescribers are more likely to prescribe BZDs, and male patients are more likely to receive BZDs. Further studies are required to characterize factors that influence this gender-by-gender interaction.

Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery.

Nitrogen is an important indicator for monitoring wheat growth. The rapid development and wide application of non-destructive detection provide many approaches for estimating leaf nitrogen content (LNC) in wheat. Previous studies have shown that better results have been obtained in the estimation of LNC in wheat based on spectral features. However, the lack of automatically extracted features leads to poor universality of the estimation model. Therefore, a feature fusion method for estimating LNC in wheat by combining spectral features with deep features (spatial features) was proposed. The deep features were automatically obtained with a convolutional neural network model based on the PyTorch framework. The spectral features were obtained using spectral information including position features (PFs) and vegetation indices (VIs). Different models based on feature combination for evaluating LNC in wheat were constructed: partial least squares regression (PLS), gradient boosting decision tree (GBDT), and support vector regression (SVR). The results indicate that the model based on the fusion feature from near-ground hyperspectral imagery has good estimation effect. In particular, the estimation accuracy of the GBDT model is the best (R2 = 0.975 for calibration set, R2 = 0.861 for validation set). These findings demonstrate that the approach proposed in this study improved the estimation performance of LNC in wheat, which could provide technical support in wheat growth monitoring.

Treatment for opioid use disorder in the Florida medicaid population: Using a cascade of care model to evaluate quality.

Background: A cascade of care (CoC) model may improve understanding of gaps in addiction treatment availability and quality over current single measure methods. Despite increased funding, opioid overdose rates remain high. Therefore, it is critical to understand where the health-care system is failing to provide appropriate care for people with opioid use disorder (OUD) diagnoses, and to assess disparities in receipt of medication for OUD (MOUD).Objective: Using a CoC framework, assess treatment quality and outcomes for OUD in the Florida Medicaid population in 2017/2018 by demographics and primary vs. secondary diagnosis.Methods: Data from Florida Medicaid claims for 2017 and 2018 were used to calculate the number of enrollees who were diagnosed, began MOUD, were retained on medication for a minimum of 180 days, and who died.Results: Only 28% of those diagnosed with OUD began treatment with an FDA approved MOUD (buprenorphine, methadone, or injectable naltrexone). Once on medication, 38% of newly diagnosed enrollees were retained in treatment for180 days. Those who remained on MOUD for 180 days had a hazard ratio of death of 0.226 (95% CI = 0.174 to 0.294) compared to those that did not initiate MOUD, a reduction in mortality from 10% without MOUD to 2% with MOUD.Conclusions: Initiating medication after OUD diagnosis offers the greatest opportunity for intervention to reduce overdose deaths, though efforts to increase retention are also warranted. Analyzing claims data with CoC identifies system functioning for specific populations, and suggests policies and clinical pathways to target for improvement.

Adverse neonatal outcomes associated with maternal severe mental health diagnoses and opioid use.

OBJECTIVE: Determine odds ratios for neonatal abstinence syndrome (NAS) and neonatal intensive care unit (NICU) admissions for babies born to women associated with severe mental illness (SMI) and gestational opioid use. STUDY DESIGN: A retrospective pharmacoepidemiologic study using Medicaid data included 17,130 mothers with and 170,430 mothers without SMI, and their babies. Odds ratios for NAS and NICU admissions among babies born to mothers associated with SMI diagnoses and associated with varying degrees of gestational opioid use were determined using logistic regression. RESULTS: The adjusted odds ratio for a baby in the methadone or buprenorphine group having NAS was 168.93 [95% confidence interval (CI) 148.78-191.71, P < 0.001] and was 9.64 (95% CI 8.74-10.65, P < 0.001) for NICU admissions compared to babies with no opioid exposure. CONCLUSIONS: Chronicity of prescription maternal opioid use was the strongest factor associated with NAS and NICU admissions.

Genetic polymorphism association analysis of SNPs on the species conservation genes of Tan sheep and Hu sheep.

For further understanding the genetic control mechanisms of growth and development in Tan sheep, and culturing good traits on meat performance, which is very important to both in developing local species and improving economic efficaciously. In our study, we recruited a total of 250 Tan sheep and 174 healthy Hu sheep to detect 32 SNPs in GH, GHR, NPY, Leptin, H-FABP, MSTN, and CAST by using direct sequencing techniques, in order to explore genetic marking loci which were an association with growth characters. From the results, we found different SNPs with an obvious difference for the growth traits. In the different genetic model analysis, we found SNP12, SNP29, SNP41, SNP8, SNP34, SNP35, SNP9, SNP10, SNP36, SNP45, and SNP39 were a significantly negative association with the two kinds of sheep. And SNP46, SNP42, and SNP69 with the positive association between the different trait in sheep were analyzed. From the LD and haplotype analysis, we found three blocks with the positive association in growth traits between Tan sheep and Hu sheep. The block of SNP29, SNP32, SNP34, SNP35, SNP36, SNP39, SNP41, SNP42, SNP45, and SNP46 with the genotype "AATCTACTTA" is the most significantly association with the traits. In summary, the study initially explored the genes for growth and reproduction between Tan sheep and Hu sheep and found some statistically significant results which demonstrate that there are genetic differences. These differential molecular markers may provide a scientific theoretical basis for the preferred species of Tan sheep which with good meat performance and better utilization of species resources.

Three dysregulated microRNAs in serum as novel biomarkers for gastric cancer screening.

Gastric cancer (GC) is one of the most threatening diseases. The symptoms of GC are complex and hard to detect, which also contribute to the poor prognosis of GC. Besides, the current diagnosis for GC is expensive and invasive. Thus, a fast, noninvasive biomarker is urgently needed for GC screening. MicroRNAs (miRNAs) are small noncoding RNAs, which are involved in a great variety of pathological processes, particularly carcinogenesis. MiRNAs are stable in gastric juice, plasma as well as serum, which facilitate it to be a promising biomarker for cancer. In this study, we selected three novel miRNAs, i.e., miR-233, miR-16, and miR-100, to investigate their potential diagnostic value in GC screening. A total of 50 GC patients and 47 healthy controls were involved in this study. Blood serum samples were collected; RNAs were extracted and normalized with U6 snRNA as the internal control; qRT-PCR was performed for relative expression of target miRNAs. Levels of miRNAs expression were compared by Student's t test for the comparison between two groups, and one-way ANOVA was used for multiple comparisons. The expression of miR-223, miR-16, and miR-100 was all significantly higher in GC patients than controls (all P < 0.001). All the tested miRNAs were manifested to be valuable biomarkers for GC. Relative expression of these miRNAs was significantly correlated with clinical characteristics of GC patients, such as TNM stage (P = 0.036 for miR-223; P < 0.001 for miR-100), metastatic status (P = 0.045 for miR-223; P = 0.031 for miR-16; P = 0.006 for miR-100), tumor size (P = 0.042 for miR-223; P = 0.031 for miR-16; P < 0.001 for miR-100), and differentiation grade (P = 0.036 for miR-223; P = 0.030 for miR-16; P = 0.034 for miR-100). However, in T classification, which considered both tumor size and direct extent of primary tumor, the difference in target miRNAs expression was not significant. In summary, we confirmed the diagnostic value of serum miR-223, miR-16, and miR-100 in GC. Significantly elevated expression of the three miRNAs was also observed in advanced GC patients, which suggested their availability in cancer staging.

Different regulatory processes control pollen hydration and germination in Arabidopsis.

To elucidate the functional differences in how Arabidopsis stigmas regulate pollen hydration and germination, we analyzed receptivity of stigmas, epidermal surfaces (leaves, stems of inflorescence bolts, and floral organs), and an abiotic surface (cover glass) for pollen hydration and germination. Using 65% relative humidity (RH), we found that mature pollen grains were able to hydrate and germinate on stigmas at flower developmental stages 9-13, but not on the distal end of pistils at stage 8, epidermal surfaces, or glass. Furthermore, under 100% RH, pollen grains could hydrate on all tested surfaces, but pollen germination was observed only on the young floral organs (stages 9-12) and the stigmas at stages 9-13. The distal ends of pistils at stage 8, the epidermal surfaces, and the cover glass did not support pollen germination even under 100% RH. Our results indicate that pistil factors regulating pollen hydration and germination are synthesized at stage 9 when stigmatic papillar cells begin to develop. Although pistil factors involved in pollen hydration may only be present on the stigma, the factors involved in pollen germination may localize on both the stigma and surfaces of unopened floral organs.

Use of HOS data in Florida.

The Medicare Health Outcomes Survey (HOS) is a longitudinal cohort study that assesses physical and mental functioning of Medicare enrollees in MCPs. Realizing the potential of HOS data to improve health care, the Florida Medicare Quality Improvement Organization (QIO) analyzed HOS scores and shared them with M+COs to assist in evaluating the efficacy of their disease management programs. The QIO also discusses additional uses for HOS data such as cross-linking with a patient satisfaction survey and sharing with health care organizations that collaborate with the QIO.