Revisiting implications of early family economic conditions for adolescent adaptations: An integrative cascade model.

Implications of family economic conditions (FECs) for child development have been extensively examined. What remains sparse is research spanning multiple life stages to delineate the far-reaching influences of early FECs for child subsequent development in different domains and how various family stress and investment processes jointly account for such association. To address these gaps, using data from 929 families in the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (NICHD Early Child Care Research Network, 2001, 2005), this study examined how family income-to-needs ratio (FITNR) when children were 1-36 months old was associated with child language skills, social competence, externalizing, and internalizing problems at 6th grade. Parental investment and maternal/paternal depressive symptoms and sensitivity when children were 54 months old and in 3rd grade were tested as potential mediators. Results indicated that early FITNR shaped child cognitive, social, and behavioral adaptation in early adolescence indirectly through parental investment, depressive symptoms, and sensitive parenting in the preschool period and middle childhood. Parental investment, depressive symptoms, and sensitive parenting played such mediating roles above and beyond each other. Parental investment primarily accounted for the association between early FITNR and child later language skills, whereas parental depressive symptoms and sensitive parenting uniquely explained the associations between early FITNR and child subsequent internalizing symptoms, externalizing problems, social competence, and language skills. Theoretical/practical implications of such findings were discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The driving factors and spatial evolution of industrial water pollution governance efficiency in the Yangtze River Delta urban agglomeration in China.

In order to explore the effective path to improve the efficiency of industrial water pollution governance efficiency (WGE), this study takes 27 prefecture-level cities in the Yangtze River Delta urban agglomeration in China as the research object, measures WGE through the improved SBM model, and tests the impact of WGE drivers using the spatial Durbin model (SDM). The study found that environmental pollution governance investment (EPGI) is positively correlated with WGE, and industrial agglomeration status (IAS) has inhibitory effects on the improvement of WGE. By testing IAS2, it was determined that the impact of IAS on WGE has a "U"-shaped relationship. The direct impact of EPGI on WGE is 0.5016, and the indirect impact on WGE is 0.6428; the direct impact of IAS on WGE is -0.3036, and the indirect impact on WGE is -0.5158. Among the other tested impact drivers, per capita GDP (PCG), industrial structure (IS), and level of technological innovation (TIL) are positively correlated with the dependent variable WGE, while energy consumption intensity (ECI), environmental regulation intensity (ERI), and degree of openness to foreign investment (FIR) are negatively correlated with the dependent variable WGE. In addition to the impact of the aforementioned main drivers, IAS and EPGI, these six drivers also largely influence and determine the final impact on WGE.

Producing personalized statin treatment plans to optimize clinical outcomes using big data and machine learning.

Almost half of Americans 65 years of age and older take statins, which are highly effective in lowering low-density lipoprotein cholesterol, preventing atherosclerotic cardiovascular disease (ASCVD), and reducing all-cause mortality. Unfortunately, ∼50% of patients prescribed statins do not obtain these critical benefits because they discontinue use within one year of treatment initiation. Therefore, statin discontinuation has been identified as a major public health concern due to the increased morbidity, mortality, and healthcare costs associated with ASCVD. In clinical practice, statin-associated symptoms (SAS) often result in dose reduction or discontinuation of these life-saving medications. Currently, physician decision-making in statin prescribing typically relies on only a few patient data elements. Physicians then employ reactive strategies to manage SAS concerns after they manifest (e.g., offering an alternative statin treatment plan or a statin holiday). A preferred approach would be a proactive strategy to identify the optimal treatment plan (statin agent + dosage) to prevent/minimize SAS and statin discontinuation risks for a particular individual prior to initiating treatment. Given that using a single patient's data to identify the optimal statin regimen is inadequate to ensure that the harms of statin use are minimized, alternative tactics must be used to address this problem. In this proof-of-concept study, we explore the use of a machine-learning personalized statin treatment plan (PSTP) platform to assess the numerous statin treatment plans available and identify the optimal treatment plan to prevent/minimize harms (SAS and statin discontinuation) for an individual. Our study leveraged de-identified administrative insurance claims data from the OptumLabs® Data Warehouse, which includes medical and pharmacy claims, laboratory results, and enrollment records for more than 130 million commercial and Medicare Advantage (MA) enrollees, to successfully develop the PSTP platform. In this study, we found three results: (1) the PSTP platform recommends statin prescription with significantly lower risks of SAS and discontinuation compared with standard-practice, (2) because machine learning can consider many more dimensions of data, the performance of the proactive prescription strategy with machine-learning support is better, especially the artificial neural network approach, and (3) we demonstrate a method of incorporating optimization constraints for individualized patient-centered medicine and shared decision making. However, more research into its clinical use is needed. These promising results show the feasibility of using machine learning and big data approaches to produce personalized healthcare treatment plans and support the precision-health agenda.

Improvement of fruiting body production in Cordyceps militaris by molecular assessment.

Cordyceps militaris is a heterothallic ascomycetous fungus that has been cultivated as a medicinal mushroom. This study was conducted to improve fruiting body production by PCR assessment. Based on single-ascospore isolates selected from wild and cultivated populations, the conserved sequences of α-BOX in MAT1-1 and HMG-BOX in MAT1-2 were used as markers for the detection of mating types by PCR. PCR results indicated that the ratio of mating types is consistent with a theoretical ratio of 1:1 (MAT1-1:MAT1-2) in wild (66:70) and cultivated (71:60) populations. Cross-mating between the opposite mating types produced over fivefold more well-developed fruiting bodies than self- or cross-mating between strains within the same mating type. This study may serve as a valuable reference for artificial culturing of C. militaris and other edible and medicinal mushrooms and may be useful to develop an efficient process for the selection, domestication, and management of strains for industrial-scale production.

Immunohistochemical assessment of angiogenesis in hepatocellular carcinoma and surrounding cirrhotic liver tissues.

AIM: To investigate whether vascular endothelial growth factor (VEGF) was over-expressed in hepatocellular carcinoma (HCC) or in surrounding cirrhotic liver tissues. METHODS: Immunohistochemistry was performed to investigate the expression of VEGF proteins in HCC tissues from 105 consecutive patients undergoing curative resection for HCC. The immunostaining results and related clinicopathologic materials were analyzed with statistical methods. Kaplan-Meier method was used to calculate survival curves, and Log-rank test was performed to compare differences in survival rates of the patients with positive HCC staining and negative VEGF. RESULTS: VEGF-positive expression was found in 72 of 105 HCC patients (68.6%). Capsular infiltration (P = 0.005), vascular invasion (P = 0.035) and intrahepatic metastasis (P = 0.008) were observed more frequently in patients with VEGF-positive expression than in those with VEGF-negative expression. Kaplan-Meier curves showed that VEGF-positive expression was associated with a shorter overall survival (P = 0.014). VEGF-positive expression was found in 47 of tissues 68 HCC (69.1%), and VEGF-positive expression was found in 54 of 68 surrounding cirrhotic liver tissues (79.4%). VEGF-positive expression was significantly higher in surrounding cirrhotic liver tissues than in HCC (P = 0.017). CONCLUSION: VEGF may play an important role in the angiogenesis and prognosis of HCC, as well as in the angiogenesis of liver cirrhosis.