Category: Statistics

Cell Stem Cell. 2024 Apr 2:S1934-5909(24)00090-0. doi: 10.1016/j.stem.2024.03.008. Online ahead of print.

ABSTRACT

Many patient-derived tumor models have emerged recently. However, their potential to guide personalized drug selection remains unclear. Here, we report patient-derived tumor-like cell clusters (PTCs) for non-small cell lung cancer (NSCLC), capable of conducting 100-5,000 drug tests within 10 days. We have established 283 PTC models with an 81% success rate. PTCs contain primary tumor epithelium self-assembled with endogenous stromal and immune cells and show a high degree of similarity to the original tumors in phenotypic and genotypic features. Utilizing standardized culture and drug-response assessment protocols, PTC drug-testing assays reveal 89% overall consistency in prospectively predicting clinical outcomes, with 98.1% accuracy distinguishing complete/partial response from progressive disease. Notably, PTCs enable accurate prediction of clinical outcomes for patients undergoing anti-PD1 therapy by combining cell viability and IFN-γ value assessments. These findings suggest that PTCs could serve as a valuable preclinical model for personalized medicine and basic research in NSCLC.

PMID:38593797 | DOI:10.1016/j.stem.2024.03.008

J Environ Manage. 2024 Apr 8;358:120815. doi: 10.1016/j.jenvman.2024.120815. Online ahead of print.

ABSTRACT

The present research study investigates the performance of pyrolysis oils recycled from waste tires as a collector in coal flotation. Three different types of pyrolysis oils (namely, POT₁, POT₂, and POT₃) were produced through a two-step pressure pyrolysis method followed by an oil rolling process. The characteristics of POTs were adjusted using various oil-modifying additives such as mineral salts and organic solvents. The chemical structure of POTs was explored by employing necessary instrumental analysis techniques, including microwave-assisted acid digestion (MAD), inductively coupled plasma atomic emission spectroscopy (ICP-AES), Fourier-transform infrared spectroscopy (FT-IR), and gas chromatography-mass spectrometry (GC-MS). The collecting performance of POTs in coal flotation was evaluated using an experimental design based on Response Surface Methodology (RSM), considering the ash content and yield of the final concentrate. The effect of the type and dosage of POTs was evaluated in conjunction with other important operating variables, including the dosage of frother, dosage of depressant, and the type of coal. Results of POTs characterization revealed that the pyrolysis oils were a complex composition of light and heavy hydrocarbon molecules, including naphthalene, biphenyl, acenaphthylene, fluorene, and pyrene. Statistical analysis of experimental results showed that among different POTs, POT₁ exhibited remarkable superiority, achieving not only a 15% higher coal recovery but also a 12% lower ash content. The outstanding performance of POT₁ was attributed to its unique composition, which includes a concentrated presence of carbon chains within the optimal range for efficient flotation. Additionally, the FT-IR spectra of POT₁ reveal specific functional groups, including aromatic and aliphatic compounds, greatly enhancing its interaction with coal surfaces, as confirmed by contact angle measurement. This research provides valuable insights into the specific carbon chains and functional groups that contribute to the effectiveness of POT as a collector, facilitating the optimization of coal flotation processes and underscoring the environmental advantages of employing pyrolysis oils as sustainable alternatives in the mining industry.

PMID:38593739 | DOI:10.1016/j.jenvman.2024.120815

J Environ Manage. 2024 Apr 8;358:120810. doi: 10.1016/j.jenvman.2024.120810. Online ahead of print.

ABSTRACT

The rise in oil trade and transportation has led to a continuous increase in the risk of oil spills, posing a serious worldwide concern. However, there is a lack of numerical models for predicting oil spill transport in freshwater, especially under icy conditions. To tackle this challenge, we developed a prediction system for oil with ice modeling by coupling the General NOAA Operational Modeling Environment (GNOME) model with the Great Lakes Operational Forecast System (GLOFS) model. Taking Lake Erie as a pilot study, we used observed drifter data to evaluate the performance of the coupled model. Additionally, we developed six hypothetical oil spill cases in Lake Erie, considering both with and without ice conditions during the freezing, stable, and melting seasons spanning from 2018 to 2022, to investigate the impacts of ice cover on oil spill processes. The results showed the effective performance of the coupled model system in capturing the movements of a deployed drifter. Through ensemble simulations, it was observed that the stable season with high-concentration ice had the most significant impact on limiting oil transport compared to the freezing and melting seasons, resulting in an oil-affected open water area of 49 km² on day 5 with ice cover, while without ice cover it reached 183 km². The stable season with high-concentration ice showed a notable reduction in the probability of oil presence in the risk map, whereas this reduction effect was less prominent during the freezing and melting seasons. Moreover, negative correlations between initial ice concentration and oil-affected open water area were consistent, especially on day 1 with a linear regression R-squared value of 0.94, potentially enabling rapid prediction. Overall, the coupled model system serves as a useful tool for simulating oil spills in the world’s largest freshwater system, particularly under icy conditions, thus enhancing the formulation of effective emergency response strategies.

PMID:38593738 | DOI:10.1016/j.jenvman.2024.120810

Immunobiology. 2024 Apr 7;229(3):152801. doi: 10.1016/j.imbio.2024.152801. Online ahead of print.

ABSTRACT

BACKGROUND: Trauma is statistically a significant cause of mortality among patients across countries. Nevertheless, the precise correlation between genetic diagnostic markers and the intricate mechanism of trauma remains indistinct.

METHODS: Our study exclusively centered on trauma patients and selected three trauma-related datasets from the Gene Expression Omnibus (GEO) database, all of which had blood samples collected within post-traumatic 12 h. Differential gene screening, the WGCNA and Cytoscape software were employed to analyze the two datasets, with a particular emphasis on the top 100 genes selected based on MCC algorithm scores. A logistic diagnostic model was constructed by analyzing the intersection genes in the third dataset, leading to the identification of diagnostic biomarkers with high efficiency. The global immune landscape of these patients was extensively investigated using a multidimensional approach. Meanwhile, the underlying pathological and physiological mechanisms associated with early trauma status are summarized by integrating existing literature.

RESULTS: Out of these two GEO datasets, 21 overlapping genes were identified and incorporated into in the logistic diagnostic model constructed in the GSE36809 dataset. A panel of 9 genes was uncovered as a diagnostic biomarker, and their expression and correlation were subsequently verified. Additionally, by virtue of various algorithms, the findings revealed an upregulation of neutrophil expression and a downregulation of CD8+ T cell expression, indicating characteristic early trauma-induced inflammation activation and immune suppression. The correlation observed between the feature genes and immune cells serves to validate the exceptional diagnostic capability of these 9 genes in identifying trauma status and their promising potential for patients who could benefit from targeted immune interventions. Drawing from these findings, the discussion section offers insights into the underlying pathological and physiological mechanisms at play.

CONCLUSION: Our research has discovered a novel diagnostic biomarker and unveiled its association with post-traumatic immune alterations. This breakthrough enables accurate and timely diagnosis of early trauma, facilitating the implementation of appropriate healthcare interventions.

PMID:38593729 | DOI:10.1016/j.imbio.2024.152801

Epidemics. 2024 Feb 20;47:100745. doi: 10.1016/j.epidem.2024.100745. Online ahead of print.

ABSTRACT

We analyse infectious disease case surveillance data to estimate COVID-19 spread and gain an understanding of the impact of introducing vaccines to counter the disease in Switzerland. The data used in this work is extensive and detailed and includes information on weekly number of cases and vaccination rates by age and region. Our approach takes into account waning immunity. The statistical analysis allows us to determine the effects of choosing alternative vaccination strategies. Our results indicate greater uptake of vaccine would have led to fewer cases with a particularly large effect on undervaccinated regions. An alternative distribution scheme not targeting specific age groups also leads to fewer cases overall but could lead to more cases among the elderly (a potentially vulnerable population) during the early stage of prophylaxis rollout.

PMID:38593727 | DOI:10.1016/j.epidem.2024.100745

Public Health. 2024 Apr 8;231:15-22. doi: 10.1016/j.puhe.2024.03.009. Online ahead of print.

ABSTRACT

OBJECTIVE: This study comprehensively analyzed the temporal and spatial dynamics of COVID-19 cases and deaths within the obstetric population in Brazil, comparing the periods before and during mass COVID-19 vaccination. We explored the trends and geographical patterns of COVID-19 cases and maternal deaths over time. We also examined their correlation with the SARS-CoV-2 variant circulating and the social determinants of health.

STUDY DESIGN: This is a nationwide population-based ecological study.

METHODS: We obtained data on COVID-19 cases, deaths, socioeconomic status, and vulnerability information for Brazil’s 5570 municipalities for both the pre-COVID-19 vaccination and COVID-19 vaccination periods. A Bayesian model was used to mitigate indicator fluctuations. The spatial correlation of maternal cases and fatalities with socioeconomic and vulnerability indicators was assessed using bivariate Moran.

RESULTS: From March 2020 to June 2023, a total of 23,823 cases and 1991 maternal fatalities were recorded among pregnant and postpartum women. The temporal trends in maternal incidence and mortality rates fluctuated over the study period, largely influenced by widespread COVID-19 vaccination and the dominant SARS-CoV-2 variant. There was a significant reduction in maternal mortality due to COVID-19 following the introduction of vaccination. The geographical distribution of COVID-19 cases and maternal deaths exhibited marked heterogeneity in both periods, with distinct spatial clusters predominantly observed in the North, Northeast, and Central West regions. Municipalities with the highest Human Development Index reported the highest incidence rates, while those with the highest levels of social vulnerability exhibited elevated mortality and fatality rates.

CONCLUSION: Despite the circulation of highly transmissible variants of concern, maternal mortality due to COVID-19 was significantly reduced following the mass vaccination. There was a heterogeneous distribution of cases and fatalities in both periods (before and during mass vaccination). Smaller municipalities and those grappling with social vulnerability issues experienced the highest rates of maternal mortality and fatalities.

PMID:38593681 | DOI:10.1016/j.puhe.2024.03.009

J Hazard Mater. 2024 Apr 3;470:134208. doi: 10.1016/j.jhazmat.2024.134208. Online ahead of print.

ABSTRACT

This study introduces an innovative strategy for the rapid and accurate identification of pesticide residues in agricultural products by combining surface-enhanced Raman spectroscopy (SERS) with a state-of-the-art transformer model, termed SERSFormer. Gold-silver core-shell nanoparticles were synthesized and served as high-performance SERS substrates, which possess well-defined structures, uniform dispersion, and a core-shell composition with an average diameter of 21.44 ± 4.02 nm, as characterized by TEM-EDS. SERSFormer employs sophisticated, task-specific data processing techniques and CNN embedders, powered by an architecture features weight-shared multi-head self-attention transformer encoder layers. The SERSFormer model demonstrated exceptional proficiency in qualitative analysis, successfully classifying six categories, including five pesticides (coumaphos, oxamyl, carbophenothion, thiabendazole, and phosmet) and a control group of spinach data, with 98.4% accuracy. For quantitative analysis, the model accurately predicted pesticide concentrations with a mean absolute error of 0.966, a mean squared error of 1.826, and an R² score of 0.849. This novel approach, which combines SERS with machine learning and is supported by robust transformer models, showcases the potential for real-time pesticide detection to improve food safety in the agricultural and food industries.

PMID:38593663 | DOI:10.1016/j.jhazmat.2024.134208

JMIR Diabetes. 2024 Apr 9;9:e55165. doi: 10.2196/55165.

ABSTRACT

BACKGROUND: Black adolescents with type 1 diabetes (T1D) are at increased risk for suboptimal diabetes health outcomes; however, evidence-based interventions for this population are lacking. Depression affects a high percentage of youth with T1D and increases the likelihood of health problems associated with diabetes.

OBJECTIVE: Our aim was to test whether baseline levels of depression moderate the effects of a brief eHealth parenting intervention delivered to caregivers of young Black adolescents with T1D on youths’ glycemic control.

METHODS: We conducted a multicenter randomized controlled trial at 7 pediatric diabetes clinics located in 2 large US cities. Participants (N=149) were allocated to either the intervention group or a standard medical care control group. Up to 3 intervention sessions were delivered on a tablet computer during diabetes clinic visits over a 12-month period.

RESULTS: In a linear mixed effects regression model, planned contrasts did not show significant reductions in hemoglobin A_1c (HbA_1c) for intervention adolescents compared to controls. However, adolescents with higher baseline levels of depressive symptoms who received the intervention had significantly greater improvements in HbA_1c levels at 6-month follow-up (0.94%; P=.01) and 18-month follow-up (1.42%; P=.002) than those with lower levels of depression. Within the intervention group, adolescents had a statistically significant reduction in HbA_1c levels from baseline at 6-month and 18-month follow-up.

CONCLUSIONS: A brief, culturally tailored eHealth parenting intervention improved health outcomes among Black adolescents with T1D and depressive symptoms.

TRIAL REGISTRATION: ClinicalTrials.gov NCT03168867; https://clinicaltrials.gov/study/NCT03168867.

PMID:38593428 | DOI:10.2196/55165

JMIR Ment Health. 2024 Apr 9;11:e55988. doi: 10.2196/55988.

ABSTRACT

BACKGROUND: Large language models (LLMs) hold potential for mental health applications. However, their opaque alignment processes may embed biases that shape problematic perspectives. Evaluating the values embedded within LLMs that guide their decision-making have ethical importance. Schwartz’s theory of basic values (STBV) provides a framework for quantifying cultural value orientations and has shown utility for examining values in mental health contexts, including cultural, diagnostic, and therapist-client dynamics.

OBJECTIVE: This study aimed to (1) evaluate whether the STBV can measure value-like constructs within leading LLMs and (2) determine whether LLMs exhibit distinct value-like patterns from humans and each other.

METHODS: In total, 4 LLMs (Bard, Claude 2, Generative Pretrained Transformer [GPT]-3.5, GPT-4) were anthropomorphized and instructed to complete the Portrait Values Questionnaire-Revised (PVQ-RR) to assess value-like constructs. Their responses over 10 trials were analyzed for reliability and validity. To benchmark the LLMs’ value profiles, their results were compared to published data from a diverse sample of 53,472 individuals across 49 nations who had completed the PVQ-RR. This allowed us to assess whether the LLMs diverged from established human value patterns across cultural groups. Value profiles were also compared between models via statistical tests.

RESULTS: The PVQ-RR showed good reliability and validity for quantifying value-like infrastructure within the LLMs. However, substantial divergence emerged between the LLMs’ value profiles and population data. The models lacked consensus and exhibited distinct motivational biases, reflecting opaque alignment processes. For example, all models prioritized universalism and self-direction, while de-emphasizing achievement, power, and security relative to humans. Successful discriminant analysis differentiated the 4 LLMs’ distinct value profiles. Further examination found the biased value profiles strongly predicted the LLMs’ responses when presented with mental health dilemmas requiring choosing between opposing values. This provided further validation for the models embedding distinct motivational value-like constructs that shape their decision-making.

CONCLUSIONS: This study leveraged the STBV to map the motivational value-like infrastructure underpinning leading LLMs. Although the study demonstrated the STBV can effectively characterize value-like infrastructure within LLMs, substantial divergence from human values raises ethical concerns about aligning these models with mental health applications. The biases toward certain cultural value sets pose risks if integrated without proper safeguards. For example, prioritizing universalism could promote unconditional acceptance even when clinically unwise. Furthermore, the differences between the LLMs underscore the need to standardize alignment processes to capture true cultural diversity. Thus, any responsible integration of LLMs into mental health care must account for their embedded biases and motivation mismatches to ensure equitable delivery across diverse populations. Achieving this will require transparency and refinement of alignment techniques to instill comprehensive human values.

PMID:38593424 | DOI:10.2196/55988

Hum Reprod. 2024 Apr 9:deae070. doi: 10.1093/humrep/deae070. Online ahead of print.

ABSTRACT

STUDY QUESTION: Can the application of the theory of planned behavior (TPB) help predict heterosexual parents’ disclosure of donor conception to their children?

SUMMARY ANSWER: Parents with a stronger will to act in accordance with social norms favoring disclosure were more likely to start the disclosure process within the next 5-9 years.

WHAT IS KNOWN ALREADY: In contrast to single mothers by choice and same-sex couples, heterosexual couples need to make an active decision to disclose their use of donor conception to their child. While disclosure at an early age is encouraged by international guidelines, many heterosexual-couple parents struggle with this. A previous study has found an association between parental scores of TPB factors and disclosure intention, but so far, no study has applied the TPB to predict parents’ disclosure behavior.

STUDY DESIGN, SIZE, DURATION: The present study is based on the fourth and fifth waves of data collection (T4 and T5) in a nation-wide longitudinal study. Participating parents had conceived through identity-release oocyte donation (n = 68, response rate 65%) and sperm donation (n = 62, response rate 56%) as part of a heterosexual couple.

PARTICIPANTS/MATERIALS, SETTING, METHODS: The present study is part of the prospective longitudinal Swedish Study on Gamete Donation (SSGD). Consecutive recruitment of couples starting oocyte or sperm donation treatment was conducted at all seven fertility clinics providing gamete donation in Sweden during a 3-year period (2005-2008). Participants were requested to complete postal surveys at five time points. The present study includes heterosexual-couple parents following oocyte or sperm donation who participated at the two latest time points when their children were 7-8 years old (T4), and 13-17 years old (T5). At T4, participants completed the study-specific TPB Disclosure Questionnaire (TPB-DQ) measuring attitudes and intentions to disclose the donor conception to the child, and disclosure behavior was assessed at both T4 and T5. Data from those participants who had not yet disclosed at T4 were analyzed using survival analysis with Cox regressions.

MAIN RESULTS AND THE ROLE OF CHANCE: Forty participants had not disclosed the donor conception to their children at T4 and, out of these, 13 had still not disclosed at T5. We found a significant association between scores of the TPB factor Subjective norms at T4 and their subsequent disclosure behavior at T5 (HR = 2.019; 95% CI: 1.36-3.01). None of the other factors were significantly associated with disclosure behavior.

LIMITATIONS, REASONS FOR CAUTION: The present study concerns heterosexual-couple parents with children conceived following treatment with gametes from open-identity donors, which limits the generalizability of our findings to other groups and contexts. Other limitations include the risk of systematic attrition due to the longitudinal study design and decreased statistical power due to few participants.

WIDER IMPLICATIONS OF THE FINDINGS: Our findings highlight the importance of perceived subjective norms for parents’ disclosure behavior and indicate that the co-parent’s opinion about disclosure is of particular relevance in this regard. Counselors should focus on supporting prospective parents to initiate and maintain a healthy and open dialogue about concerns around building a family with donor conception.

STUDY FUNDING/COMPETING INTEREST(S): The study was funded by the Swedish Research Council. The authors have no competing interests to declare.

TRIAL REGISTRATION NUMBER: N/A.

PMID:38593421 | DOI:10.1093/humrep/deae070