Mammalian adaptation risk in HPAI H5N8: a comprehensive model bridging experimental data with mathematical insights

Understanding the mammalian pathogenesis and interspecies transmission of HPAI H5N8 virus hinges on mapping its adaptive markers. We used deep sequencing to track these markers over five passages in murine lung tissue. Subsequently, we evaluated the growth, selection, and RNA load of eight recombinant viruses with mammalian adaptive markers. By leveraging an integrated non-linear regression model, we quantitatively determined the influence of these markers on growth, adaptation, and RNA expression in mammalian hosts. Furthermore, our findings revealed that the interplay of these markers can lead to synergistic, additive, or antagonistic effects when combined. The elucidation distance method then transformed these results into distinct values, facilitating the derivation of a risk score for each marker. In vivo tests affirmed the accuracy of scores. As more mutations were incorporated, the overall risk score of virus heightened, and the optimal interplay between markers became essential for risk augmentation. Our study provides a robust model to assess risk from adaptive markers of HPAI H5N8, guiding strategies against future influenza threats.

Advances in machine learning with chemical language models in molecular property and reaction outcome predictions

Molecular properties and reactions form the foundation of chemical space. Over the years, innumerable molecules have been synthesized, a smaller fraction of them found immediate applications, while a larger proportion served as a testimony to creative and empirical nature of the domain of chemical science. With increasing emphasis on sustainable practices, it is desirable that a target set of molecules are synthesized preferably through a fewer empirical attempts instead of a larger library, to realize an active candidate. In this front, predictive endeavors using machine learning (ML) models built on available data acquire high timely significance. Prediction of molecular property and reaction outcome remain one of the burgeoning applications of ML in chemical science. Among several methods of encoding molecular samples for ML models, the ones that employ language like representations are gaining steady popularity. Such representations would additionally help adopt well-developed natural language processing (NLP) models for chemical applications. Given this advantageous background, herein we describe several successful chemical applications of NLP focusing on molecular property and reaction outcome predictions. From relatively simpler recurrent neural networks (RNNs) to complex models like transformers, different network architecture have been leveraged for tasks such as de novo drug design, catalyst generation, forward and retro-synthesis predictions. The chemical language model (CLM) provides promising avenues toward a broad range of applications in a time and cost-effective manner. While we showcase an optimistic outlook of CLMs, attention is also placed on the persisting challenges in reaction domain, which would optimistically be addressed by advanced algorithms tailored to chemical language and with increased availability of high-quality datasets.

Attainment and loss of early social-communication skills across neurodevelopmental conditions in the Norwegian Mother, Father and Child Cohort Study

BACKGROUND

Delays and loss of early-emerging social-communication skills are often discussed as unique to autism. However, most studies of regression have relied on retrospective recall and clinical samples. Here, we examine attainment and loss of social-communication skills in the population-based Norwegian Mother, Father and Child Cohort Study (MoBa).

METHODS

Mothers rated their child's attainment of 10 early-emerging social-communication skills at ages 18 and 36 months (N = 40,613, 50.9% male). Prospectively reported loss was defined as skill presence at 18 months but absence at 36 months. At 36 months, mothers also recalled whether the child had lost social-communication skills. The Norwegian Patient Registry was used to capture diagnoses of Autism Spectrum Disorder (autism) and other neurodevelopmental disabilities (NDDs).

RESULTS

Delay in at least one skill was observed in 14% of the sample and loss in 5.4%. Recalled loss of social-communication skills was rare (0.86%) and showed low convergence with prospectively reported loss. Delay and especially loss were associated with elevated odds of an autism diagnosis (n = 383) versus no autism diagnosis (n = 40,230; ≥3 skills delayed: OR = 7.09[4.15,12.11]; ≥3 skills lost: OR = 30.66[17.30,54.33]). They were also associated with an increased likelihood of autism compared to some other NDDs. Delay (relative risk [RR] = 4.16[2.08, 8.33]) and loss (RR = 10.00[3.70, 25.00]) associated with increased likelihood of autism versus ADHD, and loss (RR = 4.35[1.28,14.29]), but not delay (RR = 2.00[0.78,5.26]), associated with increased likelihood of autism compared to language disability. Conversely, delay conferred decreased likelihood of autism versus intellectual disability (RR = 0.11[0.06,0.21]), and loss was not reliably associated with likelihood of autism versus intellectual disability (RR = 1.89[0.44,8.33]).

CONCLUSIONS

This population-based study suggests that loss of early social communication skills is more common than studies using retrospective reports have indicated and is observed across several NDD diagnoses (not just autism). Nevertheless, most children with NDD diagnoses showed no reported delay or loss in these prospectively measured skills.

The "Why" behind including "Y" in your imputation model

Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this recommendation always holds and why this is sometimes true. We examine deterministic imputation (i.e. single imputation with fixed values) and stochastic imputation (i.e. single or multiple imputation with random values) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Moreover, we dispel common misconceptions about deterministic imputation models and demonstrate why the outcome should not be included in these models. This article aims to bridge the gap between imputation in theory and in practice, providing mathematical derivations to explain common statistical recommendations. We offer a better understanding of the considerations involved in imputing missing covariates and emphasize when it is necessary to include the outcome variable in the imputation model.

Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models

Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions.

How to assess and take into account trend in single-case experimental design data

One of the data features that are expected to be assessed when analyzing single-case experimental designs (SCED) data is trend. The current text deals with four different questions that applied researchers can ask themselves when assessing trend and especially when dealing with improving baseline trend: (a) What options exist for assessing the presence of trend?; (b) Once assessed, what criterion can be followed for deciding whether it is necessary to control for baseline trend?; (c) What strategy can be followed for controlling for baseline trend?; and (d) How to proceed in case there is baseline trend only in some A-B comparisons? Several options are reviewed for each of these questions in the context of real data, and tentative recommendations are provided. A new user-friendly website is developed to implement the options for fitting a trend line and a criterion for selecting a specific technique for that purpose. Trend-related and more general data analytical recommendations are provided for applied researchers.Trial registration: ClinicalTrials.gov identifier: NCT04560777.

Enhanced Recovery after Surgery recommendations that most impact patient care: A multi-institutional, multidiscipline analysis in the United States

BACKGROUND

Compliance to the entire Enhanced Recovery after Surgery (ERAS) protocol improves surgical recovery, where higher compliance improves outcomes. However, specific items may predict improved recovery more than others. Studies have evaluated the impact of individual ERAS recommendations though they are either single center, not based in the United States (US), or focus on colorectal procedures only. This study aims to evaluate compliance on surgical outcomes in two large healthcare systems in the US across four surgery types.

METHODS

Compliance to individual recommendations, limited patient characteristics, and outcomes data from two US ERAS Centers of Excellence (CoE) for hepatectomy, pancreatectomy, radical cystectomy, and head and neck (HN) resections were evaluated. Outcomes included 30-day Clavien-Dindo≥3, readmission, mortality, and length of stay (LOS). Multivariate regressions were performed as appropriate for the data for each surgery type. Clavien≥3 was included to control for severity of complications, and the CoE variable was force-retained.

RESULTS

A total of 2886 records were analyzed. Controlling for CoE and severity of patient complications, early removal of Foley catheter was associated with significant reductions in LOS in the liver, pancreas, and HN procedures and reductions in complications in the liver and pancreas. Limited use of NG tubes reduced LOS in the pancreas and complications in urology. Oral carbohydrate loading reduced LOS in the pancreas, and patient education reduced mortality in HN patients.

CONCLUSIONS

This study reports the effect of ERAS compliance on outcomes, by surgery type, in a multi-institutional US setting. Future studies should validate these findings and consider surgery-specific predictive models comprised of individual ERAS recommendations in real-world applications.

Development of a Predictive Model for Iron Levels in Bovine Muscle Tissue Using Hair as a Predictor

The assessment of iron levels in cattle muscle tissue is crucial for livestock management because it influences both animal health and meat quality, key factors in sustainable development. This study aimed to develop an optimal model for noninvasively predicting the iron content in Hereford cattle muscle tissue, contributing to a comprehensive understanding of the animals' elemental status. The research involved the atomic absorption analysis of muscle tissue and hair samples from cattle. A regression model was constructed using the least squares method to identify the most effective approach. These findings have ecological applications, aiding in evaluating environmental health and establishing acceptable iron thresholds for animals. The proposed mathematical model utilizing biomarkers (levels of Mg, K, Fe, Al, Cr in hair) will allow for the assessment of iron levels in cattle muscle tissue throughout the period of productive use, with the possibility of adjustment and tracking the changes in elemental status over time. The utilization of the developed method will enable the diagnosis of animal elementosis and assessment of the iron level burden. Subsequently, this will allow for the improvement of the qualitative characteristics of the final product. Thus, the obtained data contribute to fundamental knowledge regarding the content and variability of iron levels in the muscle tissue of cattle.

Association of Coagulopathy and Inflammatory Biomarkers with Severity in SARS-CoV-2-Infected Individuals of the Al-Qunfudhah Region of Saudi Arabia

BACKGROUND

Identifying prognosticators/predictors of COVID-19 severity is the principal focus for early prediction and effective management of the disease in a time-bound and cost-effective manner. We aimed to evaluate COVID-19 severity-dependent alteration in inflammatory and coagulopathy biomarkers.

METHODS

A hospital-dependent retrospective observational study (total: n = 377; male, n = 213; and female, n = 164 participants) was undertaken. COVID-19 exposure was assessed by performing real-time PCR on nasopharyngeal (NP) swabs. Descriptive and inferential statistics were applied for both continuous and categorical variables using Rstudio-version-4.0.2. Pearson correlation and regression were executed with a cut-off of p < 0.05 for evaluating significance. Data representation by R-packages and ggplot2.

RESULTS

A significant variation in the mean ± SD (highly-sever (HS)/moderately severe (MS)) of CRP (HS/MS: 102.4 ± 22.9/21.3 ± 6.9, p-value < 0.001), D-dimer (HS/MS: 661.1 ± 80.6/348.7 ± 42.9, p-value < 0.001), and ferritin (HS/MS: 875.8 ± 126.8/593.4 ± 67.3, p-value < 0.001) were observed. Thrombocytopenia, high PT, and PTT exhibited an association with the HS individuals (p < 0.001). CRP was correlated with neutrophil (r = 0.77), ferritin (r = 0.74), and WBC (r = 0.8). D-dimer correlated with platelets (r = -0.82), PT (r = 0.22), and PTT (r = 0.37). The adjusted odds ratios (Ad-OR) of CRP, ferritin, D-dimer, platelet, PT, and PTT for HS compared to MS were 1.30 (95% CI -1.137, 1.50; p < 0.001), 1.048 (95% CI -1.03, 1.066; p < 0.001), 1.3 (95% CI -1.24, 1.49, p > 0.05), -0.813 (95% CI -0.734, 0.899, p < 0.001), 1.347 (95% CI -1.15, 1.57, p < 0.001), and 1.234 (95% CI -1.16, 1.314, p < 0.001), respectively.

CONCLUSION

SARS-CoV-2 caused alterations in vital laboratory parameters and raised ferritin, CRP, and D-dimer presented an association with disease severity at a significant level.

Multidimensional Analysis of a Social Behavior Identifies Regression and Phenotypic Heterogeneity in a Female Mouse Model for Rett Syndrome

Regression is a key feature of neurodevelopmental disorders such as autism spectrum disorder, Fragile X syndrome, and Rett syndrome (RTT). RTT is caused by mutations in the X-linked gene methyl-CpG-binding protein 2 (MECP2). It is characterized by an early period of typical development with subsequent regression of previously acquired motor and speech skills in girls. The syndromic phenotypes are individualistic and dynamic over time. Thus far, it has been difficult to capture these dynamics and syndromic heterogeneity in the preclinical Mecp2-heterozygous female mouse model (Het). The emergence of computational neuroethology tools allows for robust analysis of complex and dynamic behaviors to model endophenotypes in preclinical models. Toward this first step, we utilized DeepLabCut, a marker-less pose estimation software to quantify trajectory kinematics and multidimensional analysis to characterize behavioral heterogeneity in Het in the previously benchmarked, ethologically relevant social cognition task of pup retrieval. We report the identification of two distinct phenotypes of adult Het: Het that display a delay in efficiency in early days and then improve over days like wild-type mice and Het that regress and perform worse in later days. Furthermore, regression is dependent on age and behavioral context and can be detected in the initial days of retrieval. Together, the novel identification of two populations of Het suggests differential effects on neural circuitry, opens new avenues to investigate the underlying molecular and cellular mechanisms of heterogeneity, and designs better studies for stratifying therapeutics.

Complex associations between cancer progression and immune gene expression reveals early influence of transmissible cancer on Tasmanian devils

The world's largest extant carnivorous marsupial, the Tasmanian devil, is challenged by Devil Facial Tumor Disease (DFTD), a fatal, clonally transmitted cancer. In two decades, DFTD has spread across 95% of the species distributional range. A previous study has shown that factors such as season, geographic location, and infection with DFTD can impact the expression of immune genes in Tasmanian devils. To date, no study has investigated within-individual immune gene expression changes prior to and throughout the course of DFTD infection. To explore possible changes in immune response, we investigated four locations across Tasmania that differed in DFTD exposure history, ranging between 2 and >30 years. Our study demonstrated considerable complexity in the immune responses to DFTD. The same factors (sex, age, season, location and DFTD infection) affected immune gene expression both across and within devils, although seasonal and location specific variations were diminished in DFTD affected devils. We also found that expression of both adaptive and innate immune genes starts to alter early in DFTD infection and continues to change as DFTD progresses. A novel finding was that the lower expression of immune genes MHC-II, NKG2D and CD8 may predict susceptibility to earlier DFTD infection. A case study of a single devil with regressed tumor showed opposite/contrasting immune gene expression patterns compared to the general trends observed across devils with DFTD infection. Our study highlights the complexity of DFTD's interactions with the host immune system and the need for long-term studies to fully understand how DFTD alters the evolutionary trajectory of devil immunity.

Durability of the Effectiveness of Heterologous COVID-19 Vaccine Regimens in Thailand: Retrospective Cohort Study Using National Registration Data

BACKGROUND

The durability of heterologous COVID-19 vaccine effectiveness (VE) has been primarily studied in high-income countries, while evaluation of heterologous vaccine policies in low- and middle-income countries remains limited.

OBJECTIVE

We aimed to evaluate the duration during which the VE of heterologous COVID-19 vaccine regimens in mitigating serious outcomes, specifically severe COVID-19 and death following hospitalization with COVID-19, remains over 50%.

METHODS

We formed a dynamic cohort by linking records of Thai citizens aged ≥18 years from citizen vital, COVID-19 vaccine, and COVID-19 cases registry databases between May 2021 and July 2022. Encrypted citizen identification numbers were used to merge the data between the databases. This study focuses on 8 common heterologous vaccine sequences: CoronaVac/ChAdOx1, ChAdOx1/BNT162b2, CoronaVac/CoronaVac/ChAdOx1, CoronaVac/ChAdOx1/ChAdOx1, CoronaVac/ChAdOx1/BNT162b2, BBIBP-CorV/BBIBP-CorV/BNT162b2, ChAdOx1/ChAdOx1/BNT162b2, and ChAdOx1/ChAdOx1/mRNA-1273. Nonimmunized individuals were considered for comparisons. The cohort was stratified according to the vaccination status, age, sex, province location, month of vaccination, and outcome. Data analysis employed logistic regression to determine the VE, accounting for potential confounders and durability over time, with data observed over a follow-up period of 7 months.

RESULTS

This study includes 52,580,841 individuals, with approximately 17,907,215 and 17,190,975 receiving 2- and 3-dose common heterologous vaccines (not mutually exclusive), respectively. The 2-dose heterologous vaccinations offered approximately 50% VE against severe COVID-19 and death following hospitalization with COVID-19 for 2 months; however, the protection significantly declined over time. The 3-dose heterologous vaccinations sustained over 50% VE against both outcomes for at least 8 months, as determined by logistic regression with durability time-interaction modeling. The vaccine sequence consisting of CoronaVac/CoronaVac/ChAdOx1 demonstrated >80% VE against both outcomes, with no evidence of VE waning. The final monthly measured VE of CoronaVac/CoronaVac/ChAdOx1 against severe COVID-19 and death following hospitalization at 7 months after the last dose was 82% (95% CI 80.3%-84%) and 86.3% (95% CI 83.6%-84%), respectively.

CONCLUSIONS

In Thailand, within a 7-month observation period, the 2-dose regimens could not maintain a 50% VE against severe and fatal COVID-19 for over 2 months, but all of the 3-dose regimens did. The CoronaVac/CoronaVac/ChAdOx1 regimen showed the best protective effect against severe and fatal COVID-19. The estimated durability of 50% VE for at least 8 months across all 3-dose heterologous COVID-19 vaccine regimens supports the adoption of heterologous prime-boost vaccination strategies, with a primary series of inactivated virus vaccine and boosting with either a viral vector or an mRNA vaccine, to prevent similar pandemics in low- and middle-income countries.

Sex Differences in Hydration Biomarkers and Test-Retest Reliability Following Passive Dehydration

This study investigated (a) differences between males and females for changes in serum, tear, and urine osmolality, hematocrit, and urine specific gravity following acute passive dehydration and (b) assessed the reliability of these biomarkers separately for each sex. Fifteen males (age: 26.3 ± 3.5 years, body mass: 76 ± 7 kg) and 15 females (age: 28.8 ± 6.4 years, body mass: 63 ± 7 kg) completed a sauna protocol twice (5-28 days apart), aiming for 4% body mass loss (BML). Urine, blood, and tear markers were collected pre- and postdehydration, and change scores were calculated. Male BML was significantly greater than that of females in Trial 1 (3.53% ± 0.55% vs. 2.53% ± 0.43%, p < .001) and Trial 2 (3.36% ± 0.66% vs. 2.53% ± 0.44%, p = .01). Despite significant differences in BML, change in hematocrit was the only change marker that displayed a significant difference in Trial 1 (males: 3% ± 1%, females: 2% ± 1%, p = .004) and Trial 2 (males: 3% ± 1%, females: 1% ± 1%, p = .008). Regression analysis showed a significant effect for sex (male) predicting change in hematocrit (β = 0.8, p = .032) and change in serum osmolality (β = -3.3, p = .005) when controlling for BML but not for urinary or tear measures. The intraclass correlation coefficients for females (ICC 2, 1) were highest for change in urine specific gravity (ICC = .62, p = .006) and lowest for change in tear osmolarity (ICC = -.14, p = .689), whereas for males, it was posthematocrit (ICC = .65, p = .003) and post tear osmolarity (ICC = .18, p = .256). Generally, biomarkers showed lower test-retest reliability in males compared with females but, overall, were classified as poor-moderate in both sexes. These findings suggest that the response and reliability of hydration biomarkers are sex specific and highlight the importance of accounting for BML differences.

Referee Abuse, Intention to Quit, and Well-Being

There are growing levels of abuse toward match officials in sport as well as general problems of their recruitment and retention. Purpose: This study analyzes the role that physical and nonphysical abuse has on association football referees' intentions to quit and their personal well-being. Methods: Drawing on pooled survey data of association football referees from the UK and Canada, this paper employs probit, ordinary least squares, and treatment effects regression analyses to explore the casual relationship between the physical and nonphysical abuse faced by referees, their intention to quit and their well-being. Results: Although physical abuse is less common than nonphysical abuse both affect the intention to quit and well-being of officials. Moreover, those that do not contemplate quitting also face reductions in their well-being. Conclusion: The research recommends a zero-tolerance approach to all forms of abuse of officials in sport and identifies that organizations have a duty of care for the well-being of their officials.

The moderating role of trust in pandemic-relevant institutions on the relation between pandemic fatigue and vaccination intentions

This research helps to clarify the relation between pandemic fatigue (PF) and vaccination intentions (VI). Theoretically, two patterns seem plausible. First, as with any other health protective measure, PF might reduce the motivation to get vaccinated. Second, PF might increase the motivation to get vaccinated because vaccination reduces the number of (other) health protective measure needed. We tested these two opposing predictions and further explored the moderating role of trust in pandemic-relevant institutions on the link between PF and VI in two large-scale survey studies from Denmark and Germany (collected between 2020 and 2021; total N > 22,000). Data was analyzed using multiple regression models. Analyses reveal a negative link between PF and VI that is less pronounced for people high in trust. Results remain stable when accounting for covariates and quadratic trends. Thus, trust might buffer the negative relation between PF and VI.

Ileal phosphorus digestibility of soybean meal for broiler chickens remains consistent across institutions in a collaborative study regardless of non-phytate phosphorus concentration in the pre-experimental starter diet

The same experimental protocol was used in 4 institutions to evaluate the impact of non-phytate phosphorus (nPP) concentration in the starter diet on regression method-derived ileal P digestibility of soybean meal (SBM) during the subsequent grower phase. A total of 1,536 Ross 308 male broiler chickens on d 0 post hatching were allotted to 2 pre-experimental starter diets that contained 3.5 or 4.5 g nPP/kg (96 replicate cages per diet, 8 birds per cage) for 18 d. Subsequently, 576 birds from each starter diet were selected and allocated to 3 experimental semi-purified grower diets containing 400, 510, or 620 g SBM/kg (32 replicate cages per diet, 6 birds per cage) for 3 d until collection of ileal digesta. Statistical analysis was conducted as a randomized complete block design with the starter period as whole plot and the grower period as split-plot. The only significant 2-way interaction was between grower diet and experimental institution (P < 0.05) on BW gain and gain to feed ratio. The main effect of institution and grower diet impacted (P < 0.05) feed intake, the digestibility of DM, P, and calcium, and disappearance of inositol hexakisphosphate (InsP6) in the grower diets. Birds fed the 3.5 g nPP/kg starter diet had lower (P < 0.05) BW gain and feed intake during the grower period, but presented higher (P < 0.05) digestibility of P and disappearance of InsP6 compared with the birds that were fed the 4.5 g nPP/kg starter diet. Regression method-derived ileal P digestibility of SBM was determined to be 46 or 42% for the respective 3.5 or 4.5 g nPP/kg pre-experimental starter diet and was not affected by the nPP concentration or by the institution. In conclusion, the experimental protocol used in the current study resulted in similar estimates across multiple institutions and is thus endorsed for future application in studies that aim to expand the database of digestible P content in plant source feed ingredients.

On the Selection of Item Scores or Composite Scores for Clinical Prediction

Recent shifts to prioritize prediction, rather than explanation, in psychological science have increased applications of predictive modeling methods. However, composite predictors, such as sum scores, are still commonly used in practice. The motivations behind composite test scores are largely intertwined with reducing the influence of measurement error in answering explanatory questions. But this may be detrimental for predictive aims. The present paper examines the impact of utilizing composite or item-level predictors in linear regression. A mathematical examination of the bias-variance decomposition of prediction error in the presence of measurement error is provided. It is shown that prediction bias, which may be exacerbated by composite scoring, drives prediction error for linear regression. This may be particularly salient when composite scores are comprised of heterogeneous items such as in clinical scales where items correspond to symptoms. With sufficiently large training samples, the increased prediction variance associated with item scores becomes negligible even when composite scores are sufficient. Practical implications of predictor scoring are examined in an empirical example predicting suicidal ideation from various depression scales. Results show that item scores can markedly improve prediction particularly for symptom-based scales. Cross-validation methods can be used to empirically justify predictor scoring decisions.

Multitask Learning for Mental Health: Depression, Anxiety, Stress (DAS) Using Wearables

This study investigates the prediction of mental well-being factors-depression, stress, and anxiety-using the NetHealth dataset from college students. The research addresses four key questions, exploring the impact of digital biomarkers on these factors, their alignment with conventional psychology literature, the time-based performance of applied methods, and potential enhancements through multitask learning. The findings reveal modality rankings aligned with psychology literature, validated against paper-based studies. Improved predictions are noted with temporal considerations, and further enhanced by multitasking. Mental health multitask prediction results show aligned baseline and multitask performances, with notable enhancements using temporal aspects, particularly with the random forest (RF) classifier. Multitask learning improves outcomes for depression and stress but not anxiety using RF and XGBoost.

Some productive and reproductive performance, eggshell quality, serum metabolites and immune responses due to L-threonine supplementation in Japanese quail breeders' diet

A dose-response experiment was conducted to evaluate the effects of graded levels of dietary digestible threonine (dThr) during the first laying cycle on productive and reproductive performance, egg quality and immune responses of Japanese quail breeders (Coturnix coturnix japonica). Also, dThr requirements were determined based on nutrient dose-response data. A total of 450 (360 females and 90 males) 11-week-old breeders were allocated to five increment (+0.03%) levels of dThr (0.49%, 0.52%, 0.55%, 0.58%, 0.61% and 0.64%) with five replicates per treatment and 15 (12 females and three males) birds each. The experiment lasted for 12 weeks. In response to increasing dietary dThr levels, egg production, egg mass, feed efficiency, egg specific gravity, eggshell relative weight, eggshell thickness, egg fertility (EF) and immune response against sheep red blood cell (SRBC) inoculation were improved with quadratic trends and egg hatchability as set eggs was improved with linear trends. Japanese quail breeders fed a diet with 0.58% dThr concentration (threonine/lysine ratio of 59%) showed the productive performance traits, EF, eggshell quality and immune response against SRBC inoculation in the highest values. However, feed intake, egg weight, egg albumen and yolk relative weight, egg shape index, haugh unit and egg composition were not affected by increasing dietary dThr level. Based on the broken-line regression model, the dThr requirements to optimize productive performance, eggshell quality, EF and immune response against SRBC inoculation were estimated at 159-188, 169-183, 175 and 178 mg/bird per day, respectively. It is concluded, in the Japanese quail breeders during the first laying phase a daily dThr intake of 188 mg/bird, dietary dThr concentration at 0.58% (threonine/lysine ratio of 59%) is adequate for optimized productive and reproductive performance, eggshell quality and immune responses. The estimated requirements depend on what production parameter is taken into considered for optimization.

N-of-1 Trials of Antimicrobial Stewardship Interventions to Optimize Antibiotic Prescribing for Upper Respiratory Tract Infection in Emergency Departments: Protocol for a Quasi-Experimental Study

BACKGROUND

Antimicrobial stewardship programs attempting to optimize antibiotic therapy and clinical outcomes mainly focus on inpatient and outpatient settings. The lack of antimicrobial stewardship program studies in the emergency department (ED) represents a gap in tackling the problem of antimicrobial resistance as EDs treat a substantial number of upper respiratory tract infection cases throughout the year.

OBJECTIVE

We intend to implement two evidence-based interventions: (1) patient education and (2) providing physician feedback on their prescribing rates. We will incorporate evidence from a literature review and contextualizing the interventions based on findings from a local qualitative study.

METHODS

Our study uses a quasi-experimental design to evaluate the effects of interventions over time in the EDs of 4 public hospitals in Singapore. We will include an initial control period of 18 months. In the next 6 months, we will randomize 2 EDs to receive 1 intervention (ie, patient education) and the other 2 EDs to receive the alternative intervention (ie, physician feedback). All EDs will receive the second intervention in the subsequent 6 months on top of the ongoing intervention. Data will be collected for another 6 months to assess the persistence of the intervention effects. The information leaflets will be handed to patients at the EDs before they consult with the physician, while feedback to individual physicians by senior doctors is in the form of electronic text messages. The feedback will contain the physicians' antibiotic prescribing rate compared with the departments' overall antibiotic prescribing rate and a bite-size message on good antibiotic prescribing practices.

RESULTS

We will analyze the data using segmented regression with difference-in-difference estimation to account for concurrent cluster comparisons.

CONCLUSIONS

Our proposed study assesses the effectiveness of evidence-based, context-specific interventions to optimize antibiotic prescribing in EDs. These interventions are aligned with Singapore's national effort to tackle antimicrobial resistance and can be scaled up if successful.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05451863; https://clinicaltrials.gov/study/NCT05451836.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50417.

Trehalose promotes atherosclerosis regression in female mice

Introduction

Atherosclerosis is a chronic inflammatory disease caused by the deposition of lipids within the artery wall. During atherogenesis, efficient autophagy is needed to facilitate efferocytosis and cholesterol efflux, limit inflammation and lipid droplet buildup, and eliminate defective mitochondria and protein aggregates. Central to the regulation of autophagy is the transcription factor EB (TFEB), which coordinates the expression of lysosomal biogenesis and autophagy genes. In recent years, trehalose has been shown to promote TFEB activation and protect against atherogenesis. Here, we sought to investigate the role of autophagy activation during atherosclerosis regression.

Methods and results

Atherosclerosis was established in C57BL/6N mice by injecting AAV-PCSK9 and 16 weeks of Western diet feeding, followed by switching to a chow diet to induce atherosclerosis regression. During the regression period, mice were either injected with trehalose concomitant with trehalose supplementation in their drinking water or injected with saline for 6 weeks. Female mice receiving trehalose had reduced atherosclerosis burden, as evidenced by reduced plaque lipid content, macrophage numbers and IL-1β content in parallel with increased plaque collagen deposition, which was not observed in their male counterparts. In addition, trehalose-treated female mice had lower levels of circulating leukocytes, including inflammatory monocytes and CD4+ T cells. Lastly, we found that autophagy flux in male mice was basally higher than in female mice during atherosclerosis progression.

Conclusions

Our data demonstrate a sex-specific effect of trehalose in atherosclerosis regression, whereby trehalose reduced lipid content, inflammation, and increased collagen content in female mice but not in male mice. Furthermore, we discovered inherent differences in the autophagy flux capacities between the sexes: female mice exhibited lower plaque autophagy than males, which rendered the female mice more responsive to atherosclerosis regression. Our work highlights the importance of understanding sex differences in atherosclerosis to personalize the development of future therapies to treat cardiovascular diseases.

The Modification of Useful Injection-Molded Parts' Properties Induced Using High-Energy Radiation

The modification of polymer materials' useful properties can be applicable in many industrial areas due to the ability to make commodity and technical plastics (plastics that offer many benefits, such as processability, by injection molding) useful in more demanding applications. In the case of injection-molded parts, one of the most suitable methods for modification appears to be high-energy irradiation, which is currently used primarily for the modification of mechanical and thermal properties. However, well-chosen doses can effectively modify the properties of the surface layer as well. The purpose of this study is to provide a complex description of high-energy radiation's (β radiation) influence on the useful properties of injection-molded parts made from common polymers. The results indicate that β radiation initiates the cross-linking process in material and leads to improved mechanical properties. Besides the cross-linking process, the material also experiences oxidation, which influences the properties of the surface layer. Based on the measured results, the main outputs of this study are appropriately designed regression models that determine the optimal dose of radiation.

Recent Methodological Trends in Epidemiology: No Need for Data-Driven Variable Selection?

Variable selection in regression models is a particularly important issue in epidemiology, where one usually encounters observational studies. In contrast to randomized trials or experiments, confounding is often not controlled by the study design, but has to be accounted for by suitable statistical methods. For instance, when risk factors should be identified with unconfounded effect estimates, multivariable regression techniques can help to adjust for confounders. We investigated the current practice of variable selection in 4 major epidemiologic journals in 2019 and found that the majority of articles used subject-matter knowledge to determine a priori the set of included variables. In comparison with previous reviews from 2008 and 2015, fewer articles applied data-driven variable selection. Furthermore, for most articles the main aim of analysis was hypothesis-driven effect estimation in rather low-dimensional data situations (i.e., large sample size compared with the number of variables). Based on our results, we discuss the role of data-driven variable selection in epidemiology.

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)-i.e., one of the state-of-the-art multi-building and multi-floor localization models-and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of "by a single building", where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.

Comparison of different protein sources on the phosphorus digestibility of soybean meal for broiler chickens determined using the regression method

A study was conducted to evaluate the P digestibility in soybean meal (SBM) using the regression method with different basal diet that varied in protein sources. The treatments were organized in a 4 × 3 factorial arrangement, where 4 semipurified diets were formulated with varying source of protein (no protein supplement, or added casein, potato protein isolate (PPI), or dried egg albumen (DEA) at 60 g/kg) and 3 dietary levels of SBM (290, 370, or 450 g/kg). The study was a randomized complete block design with 8 replicate cages of 6 birds per cage. The experimental diets were fed from d 19 to 22 posthatching, excreta samples were collected from d 20 to 22, and ileal digesta samples were collected on d 22. Data were analyzed as a 2-way ANOVA using the GLM procedure. The digestibility and total tract utilization of DM, P, Ca, and N were affected by protein source (P < 0.05). Diets with casein presented the highest (P < 0.05) digestibility of N. Increasing SBM level in all dietary protein sources reduced (P < 0.001) the digestibility and total tract utilization of DM. The regression-derived ileal digestibility of P in SBM was 57.8 (SE = 3.78), 63.2 (SE = 5.02), 58.8 (SE = 4.53), and 35.3% (SE = 5.27) for diets without protein supplement, with casein, PPI, or DEA, respectively; the corresponding P retention were 52.2 (SE = 11.09), 83.4 (SE = 14.89), 42.6 (SE = 15.88), and 51.9% (SE = 14.67). The protein source affected (P < 0.05) the slope and intercept of P digestibility in SBM. A comparison of the coefficients using confidence intervals demonstrated that the ileal digestibility of P in SBM determined in diets with DEA was lower (P < 0.05) than the other protein sources, which did not differ from one another. These results indicate that the selection of dietary protein supplements may affect P digestibility assays using the regression method.

Hotter is not (always) better: Embracing unimodal scaling of biological rates with temperature

Rate-temperature scaling relationships have fascinated biologists for nearly two centuries and are increasingly important in our era of global climate change. These relationships are hypothesized to originate from the temperature-dependent kinetics of rate-limiting biochemical reactions of metabolism. Several prominent theories have formalized this hypothesis using the Arrhenius model, which characterizes a monotonic temperature dependence using an activation energy E. However, the ubiquitous unimodal nature of biological temperature responses presents important theoretical, methodological, and conceptual challenges that restrict the promise for insight, prediction, and progress. Here we review the development of key hypotheses and methods for the temperature-scaling of biological rates. Using simulations, we examine the constraints of monotonic models, illustrating their sensitivity to data nuances such as temperature range and noise, and their tendency to yield variable and underestimated E, with critical consequences for climate change predictions. We also evaluate the behaviour of two prominent unimodal models when applied to incomplete and noisy datasets. We conclude with recommendations for resolving these challenges in future research, and advocate for a shift to unimodal models that better characterize the full range of biological temperature responses.

Tumour regression predicts better response to interferon therapy in melanoma patients: a retrospective single centre study

We hypothesise that regression may have an impact on the effectiveness of adjuvant IFN therapy, based on its role in the host immune response. Our purpose is to investigate regression and ulceration as prognostic factors in case of interferon-alpha (IFN)-treated melanoma patients. We followed 357 IFN-treated melanoma patients retrospectively, investigating progression-free survival (PFS) and overall survival (OS) depending on the presence of ulceration and regression. A Kaplan-Meier analysis was performed, and we used a Cox regression analysis to relate risk factors. The survival function of the Cox regression was used to measure the effect of regression and ulceration on PFS and OS depending on the Breslow thickness (T1-T4) of the primary tumour. Regression was significantly positively related to PFS ( P  = 0.0018, HR = 0.352) and OS ( P  = 0.0112, HR = 0.380), while ulceration showed a negative effect (PFS: P  = 0.0001, HR = 2.629; OS: P  = 0.0003, HR = 2.388). They influence survival independently. The most favourable outcome was measured in the regressed/non-ulcerated group, whereas the worse was in the non-regressed/ulcerated one. Of risk factors, Breslow thickness is the most significant predictor. The efficacy of regression is regardless of Breslow thickness, though the more favourable the impact of regression was in the thicker primary lesions. Our results indicate that regression is associated with a more favourable outcome for IFN-treated melanoma patients, whereas ulceration shows an inverse relation. Further studies are needed to analyse the survival benefit of regression in relation to innovative immune checkpoint inhibitors.

eHealth in the Management of Depressive Episodes in Catalonia's Primary Care From 2017 to 2022: Retrospective Observational Study

BACKGROUND

The reasons for mental health consultations are becoming increasingly relevant in primary care. The Catalan health care system is undergoing a process of digital transformation, where eHealth is becoming increasingly relevant in routine clinical practice.

OBJECTIVE

This study aimed to analyze the approach to depressive episodes and the role of eHealth in the Catalan health care system from 2017 to 2022.

METHODS

A retrospective observational study was conducted on diagnostic codes related to depressive episodes and mood disorders between 2017 and 2022 using data from the Catalan Institute of Health. The sociodemographic evolution and prevalence of depression and mood disorders in Catalonia were analyzed between 2017 and 2022. Sociodemographic variables were analyzed using absolute frequency and percentage. The prevalence of depressive episodes was calculated, highlighting the year-to-year changes. The use of eHealth for related consultations was assessed by comparing the percentages of eHealth and face-to-face consultations. A comparison of sociodemographic variables based on attendance type was conducted. Additionally, a logistic regression model was used to explore factors influencing face-to-face attendance. The analysis used R software (version 4.2.1), with all differences examined using 95% CIs.

RESULTS

From 2017 to 2022, there was an 86.6% increase in the prevalence of depression and mood disorders, with women consistently more affected (20,950/31,197, 67.2% in 2017 and 22,078/33,169, 66.6% in 2022). In 2022, a significant rise in depression diagnoses was observed in rural areas (difference 0.71%, 95% CI 0.04%-1.43%), contrasting with a significant decrease in urban settings (difference -0.7%, 95% CI -1.35% to -0.05%). There was a significant increase in antidepressant use in 2022 compared to 2017 (difference 2.4%, 95% CI 1.87%-3.06%) and the proportion of eHealth visits rose from 4.34% (1240/28,561) in 2017 to 26.3% (8501/32,267) in 2022. Logistic regression analysis indicated that men (odds ratio [OR] 1.06, 95% CI 1.04-1.09) and younger individuals had a higher likelihood of eHealth consultations in 2022. Furthermore, individuals using eHealth consultations were more likely to use antidepressants (OR 1.54, 95% CI 1.50-1.57) and anxiolytics (OR 1.06, 95% CI 1.03-1.09).

CONCLUSIONS

The prevalence of depression in Catalonia has significantly increased in the last 6 years, likely influenced by the COVID-19 pandemic. Despite ongoing digital transformation since 2011, eHealth usage remained limited as of 2017. During the lockdown period, eHealth accounted for nearly half of all health care consultations, representing a quarter of consultations by 2022. In the immediate aftermath of the COVID-19 pandemic, emerging evidence suggests a significant role of eHealth in managing depression-related consultations, along with an apparent likelihood of patients being prescribed antidepressants and anxiolytics. Further research is needed to understand the long-term impact of eHealth on diagnostic practices and medication use.

A breast cancer-specific combinational QSAR model development using machine learning and deep learning approaches

Breast cancer is the most prevalent and heterogeneous form of cancer affecting women worldwide. Various therapeutic strategies are in practice based on the extent of disease spread, such as surgery, chemotherapy, radiotherapy, and immunotherapy. Combinational therapy is another strategy that has proven to be effective in controlling cancer progression. Administration of Anchor drug, a well-established primary therapeutic agent with known efficacy for specific targets, with Library drug, a supplementary drug to enhance the efficacy of anchor drugs and broaden the therapeutic approach. Our work focused on harnessing regression-based Machine learning (ML) and deep learning (DL) algorithms to develop a structure-activity relationship between the molecular descriptors of drug pairs and their combined biological activity through a QSAR (Quantitative structure-activity relationship) model. 11 popularly known machine learning and deep learning algorithms were used to develop QSAR models. A total of 52 breast cancer cell lines, 25 anchor drugs, and 51 library drugs were considered in developing the QSAR model. It was observed that Deep Neural Networks (DNNs) achieved an impressive R2 (Coefficient of Determination) of 0.94, with an RMSE (Root Mean Square Error) value of 0.255, making it the most effective algorithm for developing a structure-activity relationship with strong generalization capabilities. In conclusion, applying combinational therapy alongside ML and DL techniques represents a promising approach to combating breast cancer.

SolPredictor: Predicting Solubility with Residual Gated Graph Neural Network

Computational methods play a pivotal role in the pursuit of efficient drug discovery, enabling the rapid assessment of compound properties before costly and time-consuming laboratory experiments. With the advent of technology and large data availability, machine and deep learning methods have proven efficient in predicting molecular solubility. High-precision in silico solubility prediction has revolutionized drug development by enhancing formulation design, guiding lead optimization, and predicting pharmacokinetic parameters. These benefits result in considerable cost and time savings, resulting in a more efficient and shortened drug development process. The proposed SolPredictor is designed with the aim of developing a computational model for solubility prediction. The model is based on residual graph neural network convolution (RGNN). The RGNNs were designed to capture long-range dependencies in graph-structured data. Residual connections enable information to be utilized over various layers, allowing the model to capture and preserve essential features and patterns scattered throughout the network. The two largest datasets available to date are compiled, and the model uses a simplified molecular-input line-entry system (SMILES) representation. SolPredictor uses the ten-fold split cross-validation Pearson correlation coefficient R2 0.79±0.02 and root mean square error (RMSE) 1.03±0.04. The proposed model was evaluated using five independent datasets. Error analysis, hyperparameter optimization analysis, and model explainability were used to determine the molecular features that were most valuable for prediction.

Pediatric and Young Adult Household Transmission of the Initial Waves of SARS-CoV-2 in the United States: Administrative Claims Study

BACKGROUND

The correlates responsible for the temporal changes of intrahousehold SARS-CoV-2 transmission in the United States have been understudied mainly due to a lack of available surveillance data. Specifically, early analyses of SARS-CoV-2 household secondary attack rates (SARs) were small in sample size and conducted cross-sectionally at single time points. From these limited data, it has been difficult to assess the role that different risk factors have had on intrahousehold disease transmission in different stages of the ongoing COVID-19 pandemic, particularly in children and youth.

OBJECTIVE

This study aimed to estimate the transmission dynamic and infectivity of SARS-CoV-2 among pediatric and young adult index cases (age 0 to 25 years) in the United States through the initial waves of the pandemic.

METHODS

Using administrative claims, we analyzed 19 million SARS-CoV-2 test records between January 2020 and February 2021. We identified 36,241 households with pediatric index cases and calculated household SARs utilizing complete case information. Using a retrospective cohort design, we estimated the household SARS-CoV-2 transmission between 4 index age groups (0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 to 25 years) while adjusting for sex, family size, quarter of first SARS-CoV-2 positive record, and residential regions of the index cases.

RESULTS

After filtering all household records for greater than one member in a household and missing information, only 36,241 (0.85%) of 4,270,130 households with a pediatric case remained in the analysis. Index cases aged between 0 and 17 years were a minority of the total index cases (n=11,484, 11%). The overall SAR of SARS-CoV-2 was 23.04% (95% CI 21.88-24.19). As a comparison, the SAR for all ages (0 to 65+ years) was 32.4% (95% CI 32.1-32.8), higher than the SAR for the population between 0 and 25 years of age. The highest SAR of 38.3% was observed in April 2020 (95% CI 31.6-45), while the lowest SAR of 15.6% was observed in September 2020 (95% CI 13.9-17.3). It consistently decreased from 32% to 21.1% as the age of index groups increased. In a multiple logistic regression analysis, we found that the youngest pediatric age group (0 to 4 years) had 1.69 times (95% CI 1.42-2.00) the odds of SARS-CoV-2 transmission to any family members when compared with the oldest group (18 to 25 years). Family size was significantly associated with household viral transmission (odds ratio 2.66, 95% CI 2.58-2.74).

CONCLUSIONS

Using retrospective claims data, the pediatric index transmission of SARS-CoV-2 during the initial waves of the COVID-19 pandemic in the United States was associated with location and family characteristics. Pediatric SAR (0 to 25 years) was less than the SAR for all age other groups. Less than 1% (n=36,241) of all household data were retained in the retrospective study for complete case analysis, perhaps biasing our findings. We have provided measures of baseline household pediatric transmission for tracking and comparing the infectivity of later SARS-CoV-2 variants.

Confounding by Indication, Confounding Variables, Covariates, and Independent Variables: Knowing What These Terms Mean and When to Use Which Term

The terms independent variables, covariates, confounding variables, and confounding by indication are often imprecisely used in the context of regression. Independent variables are the full set of variables whose influence on the outcome is studied. Covariates are the independent variables that are included not because they are of interest but because their influence on the outcome can be adjusted for, leaving a more precise understanding of how the single remaining independent variable influences the outcome. Confounding variables are variables that are associated with both independent variables and outcomes; so, the relationship identified between independent variables and outcomes may be due to the confounding variable rather than to the independent variable. Potential confounders should be identified, measured, and adjusted for in regression, just as other covariates are. Confounding by indication occurs when the presence of the independent variable is driven by the confounding variable. Confounding by indication is a special kind of confounding; a confounding variable is a special kind of covariate; and a covariate is a special kind of independent variable in regression analysis. These terms and concepts are explained with the help of examples.

Histopathology and electron microscopy evaluation of the sildenafil effect on diabetic rats' retinae

PURPOSE

Although there is increasing evidence that phosphodiesterase-5 (PDE-5) inhibitors modify the effect of diabetes on different tissues, its effect on diabetic retinopathy is not well studied.

METHODS

Forty male Sprague-Dawley (SD) rats were divided into four groups: group I = control group that received no treatment; group II (diabetic group), in which diabetes was induced by a single streptozotocin injection; group III (sildenafil small dose, SSD), in which diabetes was similarly introduced (however, rats received daily oral 1 mg/kg sildenafil citrate (SC) for 3 months); and group IV (sildenafil large dose, SLD), which was as in group 3, but SC was 2.5 mg/kg. After 3 months, globes were removed and retinae were dissected; one globe from each rat was examined by light microscopy (LM), and the other by electron microscopy (EM).

RESULTS

In contrast to the control group, diabetic rats in group II demonstrated well-established diabetic changes in the form of capillary congestion, decreased cell population, hyaline changes of capillary walls, and degenerated nerve fiber layer by LM. Similarly, EM demonstrated photoreceptor degeneration, mitochondrial cristolysis, and vacuolated depleted cells among other features in group II. These diabetic features were less prominent in group III and nearly absent in group IV.

CONCLUSION

SC use in the early stages of DR may prevent/delay diabetic retinopathy development or progression in diabetic rat models, an effect that seems to be dose-related.

Methodology for Creating a Digital Bathymetric Model Using Neural Networks for Combined Hydroacoustic and Photogrammetric Data in Shallow Water Areas

This study uses a neural network to propose a methodology for creating digital bathymetric models for shallow water areas that are partially covered by a mix of hydroacoustic and photogrammetric data. A key challenge of this approach is the preparation of the training dataset from such data. Focusing on cases in which the training dataset covers only part of the measured depths, the approach employs generalized linear regression for data optimization followed by multilayer perceptron neural networks for bathymetric model creation. The research assessed the impact of data reduction, outlier elimination, and regression surface-based filtering on neural network learning. The average values of the root mean square (RMS) error were successively obtained for the studied nearshore, middle, and deep water areas, which were 0.12 m, 0.03 m, and 0.06 m, respectively; moreover, the values of the mean absolute error (MAE) were 0.11 m, 0.02 m, and 0.04 m, respectively. Following detailed quantitative and qualitative error analyses, the results indicate variable accuracy across different study areas. Nonetheless, the methodology demonstrated effectiveness in depth calculations for water bodies, although it faces challenges with respect to accuracy, especially in preserving nearshore values in shallow areas.

Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models

Sleep disorder is a disease that can be categorized as both an emotional and physical problem. It imposes several difficulties and problems, such as distress during the day, sleep-wake disorders, anxiety, and several other problems. Hence, the main objective of this research was to utilize the strong capabilities of machine learning in the prediction of sleep disorders. In specific, this research aimed to meet three main objectives. These objectives were to identify the best regression model, the best classification model, and the best learning strategy that highly suited sleep disorder datasets. Considering two related datasets and several evaluation metrics that were related to the tasks of regression and classification, the results revealed the superiority of the MultilayerPerceptron, SMOreg, and KStar regression models compared with the other twenty three regression models. Furthermore, IBK, RandomForest, and RandomizableFilteredClassifier showed superior performance compared with other classification models that belonged to several learning strategies. Finally, the Function learning strategy showed the best predictive performance among the six considered strategies in both datasets and with respect to the most evaluation metrics.

Serum metabolomics characteristics and fatty-acid-related mechanism of cirrhosis with histological response in chronic hepatitis B

Background and aims: The serum metabolites changes in patients with hepatitis B virus (HBV)-related cirrhosis as progression. Peroxisome proliferator-activated receptor gamma (PPARγ) is closely related to lipid metabolism in cirrhotic liver. However, the relationship between fatty acids and the expression of hepatic PPARγ during cirrhosis regression remains unknown. In this study, we explored the serum metabolic characteristics and expression of PPARγ in patients with histological response to treatment with entecavir. Methods: Sixty patients with HBV-related cirrhosis were selected as the training cohort with thirty patients each in the regression (R) group and non-regression (NR) group based on their pathological changes after 48-week treatment with entecavir. Another 72 patients with HBV-related cirrhosis and treated with entecavir were collected as the validation cohort. All of the serum samples were tested using ultra-performance liquid chromatography coupled to tandem mass spectrometry. Data were processed through principal component analysis and orthogonal partial least square discriminant analysis. Hepatic PPARγ expression was observed using immunohistochemistry. The relationship between serum fatty acids and PPARγ was calculated using Pearson's or Spearman's correlation analysis. Results: A total of 189 metabolites were identified and 13 differential metabolites were screened. Compared to the non-regression group, the serum level of fatty acids was higher in the R group. At baseline, the expression of PPARγ in hepatic stellate cells was positively correlated with adrenic acid (r 2 = 0.451, p = 0.046). The expression of PPARγ in both groups increased after treatment, and the expression of PPARγ in the R group was restored in HSCs much more than that in the NR group (p = 0.042). The adrenic acid and arachidonic acid (AA) in the R group also upgraded more than the NR group after treatment (p = 0.037 and 0.014). Conclusion: Baseline serum differential metabolites, especially fatty acids, were identified in patients with HBV-related cirrhosis patients who achieved cirrhosis regression. Upregulation of adrenic acid and arachidonic acid in serum and re-expression of PPARγ in HSCs may play a crucial role in liver fibrosis improvement.

Protocol of the HISTOTHERM study: assessing the response to hyperthermia and hypofractionated radiotherapy in recurrent breast cancer

Introduction

Breast cancer is globally the leading cancer in women, and despite the high 5-year survival rate the most frequent cause of cancer related deaths. Surgery, systemic therapy and radiotherapy are the three pillars of curative breast cancer treatment. However, locoregional recurrences frequently occur after initial treatment and are often challenging to treat, amongst others due to high doses of previous radiotherapy treatments. Radiotherapy can be combined with local hyperthermia to sensitize tumor cells to radiation and thereby significantly reduce the required radiation dose. Therefore, the combination treatment of mild local hyperthermia, i.e. locally heating of the tissue to 39-43°C, and re-irradiation with a reduced total dose is a relevant treatment option for previously irradiated patients. The mechanisms of this effect in the course of the therapy are to date not well understood and will be investigated in the HISTOTHERM study.

Methods and analyses

Patients with local or (loco)regional recurrent breast cancer with macroscopic tumors are included in the study. Local tumor control is evaluated clinically and histologically during the course of a combination treatment of 60 minutes mild superficial hyperthermia (39 - 43°C) using water-filtered infrared A (wIRA) irradiation, immediately followed by hypofractionated re-irradiation with a total dose of 20-24 Gy, administered in weekly doses of 4 Gy. Tumor and tumor stroma biopsies as well as blood samples will be collected prior to treatment, during therapy (at a dose of 12 Gy) and in the follow-up to monitor therapy response. The treatment represents the standard operating procedure for hyperthermia plus re-irradiation. Various tissue and blood-based markers are analyzed. We aim at pinpointing key mechanisms and markers for therapy response which may help guiding treatment decisions in future. In addition, quality of life in the course of treatment will be assessed and survival data will be evaluated.

Registration

The study is registered at the German Clinical Trials Register, Deutsches Register Klinischer Studien (DRKS00029221).

Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm

In the present era of climate change, underutilized crops such as rice beans and adzuki beans are gaining prominence to ensure food security due to their inherent potential to withstand extreme conditions and high nutritional value. These legumes are bestowed with higher nutritional attributes such as protein, fiber, vitamins, and minerals than other major legumes of the Vigna family. With the typical nutrient evaluation methods being expensive and time-consuming, non-invasive techniques such as near infrared reflectance spectroscopy (NIRS) combined with chemometrics have emerged as a better alternative. The present study aims to develop a combined NIRS prediction model for rice bean and adzuki bean flour samples to estimate total starch, protein, fat, sugars, phytate, dietary fiber, anthocyanin, minerals, and RGB value. We chose 20 morphometrically diverse accessions in each crop, of which fifteen were selected as the training set and five for validation of the NIRS prediction model. Each trait required a unique combination of derivatives, gaps, smoothening, and scatter correction techniques. The best-fit models were selected based on high RSQ and RPD values. High RSQ values of >0.9 were achieved for most of the studied parameters, indicating high-accuracy models except for minerals, fat, and phenol, which obtained RSQ <0.6 for the validation set. The generated models would facilitate the rapid nutritional exploitation of underutilized pulses such as adzuki and rice beans, showcasing their considerable potential to be functional foods for health promotion.

Unveiling microbial biomarkers of ruminant methane emission through machine learning

Background

Enteric methane from cow burps, which results from microbial fermentation of high-fiber feed in the rumen, is a significant contributor to greenhouse gas emissions. A promising strategy to address this problem is microbiome-based precision feed, which involves identifying key microorganisms for methane production. While machine learning algorithms have shown success in associating human gut microbiome with various human diseases, there have been limited efforts to employ these algorithms to establish microbial biomarkers for methane emissions in ruminants.

Methods

In this study, we aim to identify potential methane biomarkers for methane emission from ruminants by employing regression algorithms commonly used in human microbiome studies, coupled with different feature selection methods. To achieve this, we analyzed the microbiome compositions and identified possible confounding metadata variables in two large public datasets of Holstein cows. Using both the microbiome features and identified metadata variables, we trained different regressors to predict methane emission. With the optimized models, permutation tests were used to determine feature importance to find informative microbial features.

Results

Among the regression algorithms tested, random forest regression outperformed others and allowed the identification of several crucial microbial taxa for methane emission as members of the native rumen microbiome, including the genera Piromyces, Succinivibrionaceae UCG-002, and Acetobacter. Additionally, our results revealed that certain herd locations and feed composition markers, such as the lipid intake and neutral-detergent fiber intake, are also predictive features for methane emissions.

Conclusion

We demonstrated that machine learning, particularly regression algorithms, can effectively predict cow methane emissions and identify relevant rumen microorganisms. Our findings offer valuable insights for the development of microbiome-based precision feed strategies aiming at reducing methane emissions.

Deceased Organ Donor Management and Organ Distribution From Organ Procurement Organization-Based Recovery Facilities Versus Acute-Care Hospitals

Introduction: Organ recovery facilities address the logistical challenges of hospital-based deceased organ donor management. While more organs are transplanted from donors in facilities, differences in donor management and donation processes are not fully characterized. Research Question: Does deceased donor management and organ transport distance differ between organ procurement organization (OPO)-based recovery facilities versus hospitals? Design: Retrospective analysis of Organ Procurement and Transplant Network data, including adults after brain death in 10 procurement regions (April 2017-June 2021). The primary outcomes were ischemic times of transplanted hearts, kidneys, livers, and lungs. Secondary outcomes included transport distances (between the facility or hospital and the transplant program) for each transplanted organ. Results: Among 5010 deceased donors, 51.7% underwent recovery in an OPO-based recovery facility. After adjustment for recipient and system factors, mean differences in ischemic times of any transplanted organ were not significantly different between donors in facilities and hospitals. Transplanted hearts recovered from donors in facilities were transported further than hearts from hospital donors (median 255 mi [IQR 27, 475] versus 174 [IQR 42, 365], P = .002); transport distances for livers and kidneys were significantly shorter (P < .001 for both). Conclusion: Organ recovery procedures performed in OPO-based recovery facilities were not associated with differences in ischemic times in transplanted organs from organs recovered in hospitals, but differences in organ transport distances exist. Further work is needed to determine whether other observed differences in donor management and organ distribution meaningfully impact donation and transplantation outcomes.

TSPAN4 and migrasomes in atherosclerosis regression correlated to myocardial infarction and pan-cancer progression

The migrasomes formation is mediated by the assembly of micron-scale tetraspanin macrodomains and the recruitment of tetraspanin 4 (TSPAN4). However, the physiological functions of TSPAN4 on migrasomes are less known. The TSPAN4 expression in macrophages in single-cell sequencing data, GEO datasets and TCGA database were determined. TSPAN4 expression was highly associated with atherosclerosis regression-related macrophages, intraplaque hemorrhage and ruptured plaques. TSPAN4 expression was upregulated in spontaneous MI and inducible MI mice model. Besides, TSPAN4 expression was highly correlated with tumor-associated macrophages. The study provided a critical role of TSPAN4 aberrant expression in the progression of atherosclerosis and pan-cancer, and the intervention of TSPAN4 and migrasomes may save dying patients' lives and improve their prognosis.

The Effect of Perfusate Temperature on Delayed Graft Function in Deceased Donor Renal Transplantation

Introduction: Renal allograft hypothermic machine perfusion results in a decreased incidence of delayed graft function compared with static cold storage. Ensuring perfusate temperatures remain within the target range of 4-10 °C may impact delayed graft function rates. Project Aims: To identify whether this target was achieved and, if not, whether higher perfusate temperature was associated with delayed graft function. Design: In this retrospective cohort study, transplanted grafts from deceased donors placed on hypothermic machine perfusion pump from June 2019 to August 2020 were analyzed. Measurements were recovered after 5, 15, 60, and 180 min of perfusion. Univariable and multivariable analyses were performed to identify predictors of delayed graft function. Results: A total of 113 grafts from 94 donors were analyzed. Of these, 21 (19%) developed delayed graft function. On univariable logistic regression, variables associated with delayed graft function included older donor age (OR 1.08, P = .002), higher Kidney Donor Profile Index score (OR 1.03, P = .024), and higher 5-min perfusate temperature (T5 min; OR 1.49, P = .014). A higher T5 min was also associated with delayed graft function in multivariable logistic regression models (OR 1.58, P = .005; OR 1.37, P = .08). Grafts with T5 min >10 °C were more likely to experience delayed graft function than those with T5 min <10 °C (OR 4.5, P = .006). Conclusion: Higher early perfusate temperature was an independent predictor of delayed graft function and may be due to inadequate cooling of the circuit prior to placing grafts on pump. Quality improvement initiatives targeting early perfusate temperatures of ≤10 °C may reduce delayed graft function incidence.

Regression-based multiple treatment effect estimation under covariate-adaptive randomization

Covariate-adaptive randomization methods are widely used in clinical trials to balance baseline covariates. Recent studies have shown the validity of using regression-based estimators for treatment effects without imposing functional form requirements on the true data generation model. These studies have had limitations in certain scenarios; for example, in the case of multiple treatment groups, these studies did not consider additional covariates or assumed that the allocation ratios were the same across strata. To address these limitations, we develop a stratum-common estimator and a stratum-specific estimator under multiple treatments. We derive the asymptotic behaviors of these estimators and propose consistent nonparametric estimators for asymptotic variances. To determine their efficiency, we compare the estimators with the stratified difference-in-means estimator as the benchmark. We find that the stratum-specific estimator guarantees efficiency gains, regardless of whether the allocation ratios across strata are the same or different. Our conclusions were also validated by simulation studies and a real clinical trial example.

The Determinants and Consequences of Living Donor Discussion Direction

Introduction: Living donor discussions in which kidney transplant candidates discuss living kidney donation with their social network are an important step in the living donor kidney transplant process. No prior research has investigated whether who initiates discussion or influences evaluation agreement rates or how these processes may contribute to disparities. Research Questions: This study aimed to determine how common candidate- and potential-donor-initiated discussions were, at what rate each discussion type resulted in agreement to be evaluated for living donation, and what sociodemographic characteristics predicted living donor discussion and agreements. Design: A 2015 cross-sectional survey at a single, large Southeastern US transplant center measured kidney transplant candidates' social networks, including whether they had a donor discussion, who initiated it, and whether the discussion resulted in the donor evaluation agreement. Candidate-network member pairs' probability of having a candidate-initiated discussion, potential-living donor-initiated discussion, or no discussions were compared in multinomial logistic regression, and the probability of the discussion resulted in evaluation agreement was evaluated in multinomial logistic regression. Results: Sixty-six kidney transplant candidates reported on 1421 social network members. Most (80%) candidate/network-member pairs did not have a living donor discussion, with candidate-initiated discussions (11%) slightly more common than potential-donor-initiated discussions (10%). Evaluation agreement was much more common for potential-donor-initiated (72%) than for candidate-initiated discussions (39%). Potential-donor-initiated discussions were more common for White candidates (16%) than for Black candidates (7%). Conclusion: Potential-donor-initiated discussions resulted in evaluation agreement much more frequently than candidate-initiated discussions. This dynamic may contribute to racial living donation disparities.

Prediction of Model Generated Patellofemoral Joint Contact Forces Using Principal Component Prediction and Reconstruction

It is not currently possible to directly and noninvasively measure in vivo patellofemoral joint contact force during dynamic movement; therefore, indirect methods are required. Simple models may be inaccurate because patellofemoral contact forces vary for the same knee flexion angle, and the patellofemoral joint has substantial out-of-plane motion. More sophisticated models use 3-dimensional kinematics and kinetics coupled to a subject-specific anatomical model to predict contact forces; however, these models are time consuming and expensive. We applied a principal component analysis prediction and regression method to predict patellofemoral joint contact forces derived from a robust musculoskeletal model using exclusively optical motion capture kinematics (external approach), and with both patellofemoral and optical motion capture kinematics (internal approach). We tested this on a heterogeneous population of asymptomatic subjects (n = 8) during ground-level walking (n = 12). We developed equations that successfully capture subject-specific gait characteristics with the internal approach outperforming the external. These approaches were compared with a knee-flexion based model in literature (Brechter model). Both outperformed the Brechter model in interquartile range, limits of agreement, and the coefficient of determination. The equations generated by these approaches are less computationally demanding than a musculoskeletal model and may act as an effective tool in future rapid gait analysis and biofeedback applications.

Clinical course of cervical intraepithelial neoplasia grade 2: a population-based cohort study

BACKGROUND

Cervical intraepithelial neoplasia grade 2 has historically been the threshold for surgical excision, but because of high regression rates, many countries are transitioning to active surveillance. However, estimates for regression rates are based on small studies with heterogeneous definitions of regression and progression.

OBJECTIVE

This study aimed to describe regression and progression rates of cervical intraepithelial neoplasia grade 2 using nationwide healthcare registry data.

STUDY DESIGN

This was a nationwide population-based cohort study on women aged 18 to 40 years who had undergone active surveillance for cervical intraepithelial neoplasia grade 2 in Denmark from 1998 to 2020. This study excluded women with a previous record of cervical intraepithelial neoplasia grade 2 or worse or surgical excision. Cumulative incidence functions were used to estimate the rates of regression and progression at 6, 12, 18, and 24 months after diagnosis. In addition, a modified Poisson regression was used to estimate the crude and adjusted relative risks of progression within 24 months stratified by index cytology and age.

RESULTS

During the study period, 11,056 women underwent active surveillance, 6767 of whom regressed and 3580 of whom progressed within 24 months. This corresponded to regression rates of 62.9% (95% confidence interval, 61.9-63.8) and progression rates of 33.3% (95% confidence interval, 32.4-34.2) at 24 months of follow-up. Most women regressed (90%) or progressed (90%) within the first 12 months. Women with high-grade index cytology had a higher risk of progression than women with normal index cytology (adjusted relative, 1.58; 95% confidence interval, 1.43-1.76), whereas there was no difference in the risk of progression between women aged 30 and 40 years and women aged 23 to 29 years (adjusted relative risk, 0.98; 95% confidence interval, 0.88-1.10).

CONCLUSION

The observed high regression rates of cervical intraepithelial neoplasia grade 2 supported the transition in clinical management from surgical excision to active surveillance, particularly among women with low-grade or normal index cytology.

Evaluation of the utilisation of energy and phosphorus in field peas fed to broiler chickens

1. The regression method was used to estimate the utilisation of energy (Experiment 1) and phosphorus (Experiments 2 and 3) in two field peas (FP) cultivars fed to broiler chickens.2. On d 17 post hatching, 240 birds were assigned to one of five experimental diets in a randomised complete block design with body weight (BW) as a blocking factor in Experiment 1. Whereas, 192 birds were allotted to one of three experimental diets on d 19 post-hatching in Experiments 2 and 3. There were eight replicate cages per diet, with six birds per cage in Experiment 1 and eight birds per cage in Experiments 2 and 3.3. Field pea cultivars Hampton (FPH) or 4010 (FP4) was incorporated at either 150 or 300 g/kg into a maize-soybean meal-based reference diet in Experiment 1. Using semi-purified diets, FPH was included at 200, 400, or 600 g/kg in Experiment 2, whereas FP4 was included at 215, 430 or 645 g/kg in Experiment 3.4. In Experiment 1 a linear decrease (P < 0.01) was observed in metabolisable energy (ME) and nitrogen-corrected ME (MEn) with inclusion of FPH in the diets, whereas both linear and quadratic effects (P < 0.05) were observed with inclusion of FP4. The regression-determined ileal digestible energy, ME and MEn were 13.70, 12.69 and 11.93 MJ/kg DM in FPH and 12.63, 13.20 and 12.52 MJ/kg DM in FP4, respectively. The ileal digestible and retainable P intakes were linearly increased (P < 0.01) with higher inclusion of FPH and FP4 in Experiments 2 and 3, respectively. The respective true ileal digestibility and true total tract utilisation of P in FPH were 74.6% and 68.3% and for FP4 were 74.3% and 61.7%, respectively. In conclusion, the estimated energy and P utilisation values could be used in diet formulations.

A Descriptive, Correlational Study of Perceptions of Adult Kidney Transplant Recipients and Those Waiting for a Kidney Transplant About Managing Their Medications During a Pandemic

Introduction: Little is known about COVID-19 impact on patient medication management. Research Question: The aim was to describe medication management, healthcare team interactions, and adherence during the COVID-19 pandemic in kidney transplant patients and those on the kidney transplant wait list. Design: Using a descriptive, correlational design 340 adults from a midwestern US transplant program were recruited. The Managing Medications in the Midst of a Pandemic Survey measured healthcare team encounters and medication management. The Basel assessment of adherence to medications scale measured medication adherence. Results: The response rate was 35% (119/340). During the pandemic, 88% had practiced/were currently practicing socially distancing, 85% had worn/were currently wearing a face mask in public, 18% had been/were currently diagnosed with COVID-19 and 82% received the vaccine. Medication management: 76% planned and organized their own medications. Healthcare team interactions: 89% met in the office, 20% via phone, 12% telehealth, and 13% delayed seeing a healthcare provider because of COVID-19 concerns. Pharmacy interactions: 11% changed their method of obtaining medications from pharmacy due to social distancing. Medication adherence implementation was problematic with 19% missing a dose; results from the binary logistic regression suggested that those with higher levels of education were more likely to report missing a dose. Conclusions: Patients acted to prevent COVID-19 but some still contracted the virus. The pandemic changed healthcare team medication management interactions. Adherence implementation problems were nearly 20%. Findings are relevant to the transplant healthcare team to understand the impact of a pandemic on patient/team interactions and medication adherence.