Telehealth Treatment for Opioid Use Disorder During Pregnancy.

This cohort study evaluates opioid use disorder (OUD) treatment and pregnancy outcomes among pregnant patients receiving OUD care through a multistate telemedicine program in the US.

Association between a genetic index for digital dermatitis resistance and the presence of digital dermatitis, heel horn erosion and interdigital hyperplasia in Holstein cows.

Digital dermatitis (DD) is a polybacterial disease endemic to most UK dairy farms. It poses a major financial and welfare threat and is characterized by high incidence and recurrence rates. We aimed to investigate the association between the UK estimated breeding value for resistance to digital dermatitis, the Digital Dermatitis Index (DDI) and the frequency of DD, heel horn erosion (HHE), and interdigital hyperplasia (IH) in a population of Holstein dairy cows. We enrolled and genotyped 2,352 cows from 4 farms in a prospective cohort study. Foot lesion records were recorded by veterinary surgeons for each animal at 4 time points during a production cycle, starting at approximately 2 mo before calving and ending in late lactation. Importantly, these records were not used in the calculation of the DDI. Lesion records were matched to the animal's own DDI (n = 2,101) and their sire's DDI (n = 1,812). Digital Dermatitis Index values in our study population ranged from -1.41 to +1.2 and were transformed to represent distance from the mean expressed in standard deviations. The relationship between the DDI and the presence of DD was investigated using a logistic regression model, with farm, parity, and a farm-parity interaction fitted as covariates. A multivariable logistic regression model was fitted to evaluate the relationship between HHE and DDI with farm fitted as a covariate. Finally, a univariable logistic regression model with DDI as explanatory variable was used to investigate the relationship between IH and DDI. The odds ratio of an animal being affected by DD was 0.69 for one standard deviation (SD) increase in the animal's DDI (95% confidence interval (CI) = 0.63-0.76). The odds of HHE and IH were 0.69 (95%CI = 0.62-0.76) and 0.58 (95%CI = 0.49-0.68) respectively for one SD increase in DDI. The adjusted probability of DD was 32% (95% CI = 27-36%) for cows with mean DDI value of 0 while it was 24% (95% CI = 20-29%) in cows with a DDI value of +1. Sire DDI breeding values were standardized in the same way and then binned into terciles creating an ordinal variable representing bulls of high, medium, and low genetic merit for DD resistance. The daughters of low genetic merit bulls were at 2.05 (95% CI = 1.60-2.64), 1.96 (95% CI = 1.53-2.50), and 2.85 (95% CI = 1.64-5.16) times greater odds of being affected by DD, HHE, and IH respectively compared with the daughters of high genetic merit bulls. The results of this study highlight the potential of digital dermatitis genetic indexes to aid herd management of DD, and suggest that breeding for resistance to DD, alongside environmental and management control practices, could reduce the prevalence of the disease.

Retention in Telehealth Treatment for Opioid Use Disorder Among Rural Populations: A Retrospective Cohort Study.

Introduction: There are limited studies to date on telemedicine treatment outcomes for opioid use disorder (OUD) among rural populations. Methods: This was a retrospective cohort study of rural adults enrolled in telemedicine OUD treatment. Study outcomes were percent retained in care and adherence to buprenorphine assessed by urine drug screens at 1, 3, and 6 months. Results: From April 1, 2020, through January 31, 2022, 1,816 rural patients across 14 states attended an initial telemedicine visit and received a clinical diagnosis of OUD. Participants had the following characteristics: mean age 37.7 years (±8.6); 52.4% female; and 66.7% Medicaid. At 1, 3, and 6 months, 74.8%, 61.5%, and 52.3% of participants were retained in care, and 69.0%, 56.0%, and 49.2% of participants were adherent, respectively. Conclusions: Telemedicine is an effective approach for treating OUD in rural populations, with retention comparable to in-person treatment.

Modelling growth in Suffolk and Charollais sheep populations using random regression models and validation of constrained polynomial correlation values.

Random regression modelling has been used across multiple animal species to model longitudinal data. The random regression model for growth accounts for the genetic correlation between measures of the same trait over time and the wide environmental variability in growth, but this requires adequate weight records across the age range. However, contemporary management practices in sheep in the United Kingdom generally focus on growing lambs and neglect mature weight recordings. This study examined modelling strategies for growth data in Suffolk and Charollais sheep, provided by the Agriculture and Horticulture Development Board, with polynomial random regression modelling with many early life weight recordings but limited weight recordings in mature animals. Two methods were employed to model the data. In Method A, missing mature weight records were predicted for those animals that did not have a recorded mature weight. The animals were sorted into groups based on the identity of their sires and the year in which the animal was born. Mature weight values were predicted within each group with a multiple regression model. The dataset, including predicted values, was analysed with random regression models using polynomials and simple linear regression for animal and permanent environmental (PE) effects. In Method B, the dataset with missing mature weight records was analysed using a random linear regression animal model with random animal and PE effects. Due to problems of convergence because the parameters were close to the boundary space, fixing the correlation between the intercept and slope of the Legendre polynomial at different levels was investigated. The heritability values resulting from the model with a fixed correlation between intercept and slope parameters at 0.5 for the Suffolk dataset resulted in heritability values ranging from 0.2 to 0.5 from 1 to 619 days of age. Corresponding estimates for the Charollais dataset ranged from 0.18 to 0.49 from 1 to 640 days of age. For the Suffolk data, the genetic correlations ranged from 1.00 to 0.08 between weight at day 1 to weight at day 619, while for the Charollais, the correlations ranged from 1.00 to 0.05 from 1 to 640 days of age. Validation procedures were undertaken using a multitrait approach to examine the estimated breeding values when the correlation between the intercept and slope are fixed at different levels. The results indicated that fixing the correlation at 0.5 gave the most appropriate estimates for the Suffolk and Charollais datasets.

Changes in swine ammonia emissions associated with improved production management.

Swine manure management and storage have been implicated as major sources of increasing agricultural ammonia (NH3 ) emissions resulting in increased ammonium deposition in North Carolina. This study was conducted to establish how improvements in manure and animal management have affected lagoon nutrient loading and subsequent NH3 emissions determined from measured lagoon chemistry and climate data. Archived lagoon chemistry analyses from 182 farm lagoons (106,000 sample analyses) were used to evaluate trends in lagoon chemical properties. Process and empirical (statistical) NH3 volatilization models were used with the data to calculate changes in NH3 emissions from 2001 through 2018. Lagoon nutrient trends for finisher and sow farms showed that annual averages of nutrients had decreases ranging from 18 to 93%, except for a 41% increase in copper for finisher primary lagoons. Because of reduced nitrogen and pH in the lagoons, a process model of NH3 emissions suggested decreases from primary lagoons of 49 and 25% from finisher and sow farm lagoons, respectively. Empirical (statistical) models predicted even larger relative NH3 decreases (up to 54%).

Genetic parameters for carcase measurements and age at slaughter in commercial cattle.

Genetic parameters were estimated for cold carcase weight (CCW), carcase conformation (CON), carcase fat class (FAT), age at slaughter (AGE) and average daily carcase gain (ADCG) in 14 common UK breeds of cattle. These included crossbred animals but purebred datasets were also analysed for the most populous sire-breeds. Heritability estimates for beef breeds that were significant ranged from 0.24 to 0.44, 0.12 to 0.35, 0.12 to 0.36, 0.15 to 0.38 and 0.26 to 0.43 for CCW, CON, FAT, AGE and ADCG, respectively. For Holstein-Friesian, a dairy breed, heritability estimates were consistently lower than most beef breeds with estimates of 0.12, 0.13, 0.13, 0.06 and 0.15 for CCW, CON, FAT, AGE and ADCG, respectively. In all breed groups, genetic correlations were positive between CCW, CON and ADCG. In general, genetic correlations were moderate between CCW and CON (0.13 to 0.77), moderate to strong between CCW and ADCG (0.57 to 0.98) and weak or moderate between CON and ADCG (0.12 to 0.82). Genetic correlations for FAT with CCW (- 0.20 to - 0.42) and CON (- 0.16 to - 0.52) tended to be negative in the beef breed but were positive in the dairy breed, although not significant between CCW and FAT. For most beef breeds genetic correlations between AGE and carcase traits were not significant with the exceptions of AGE and CCW for Simmental (- 0.15) and Salers (- 0.24), AGE and CON for Limousin (0.15) and Simmental (0.14) and AGE and FAT from three sire-breeds (- 0.17 to - 0.35). However, the correlation between AGE and ADCG was negative and moderate to strong in magnitude (- 0.23 to - 0.67) in all beef breeds as expected since faster-growing animals reach slaughter age earlier. For Holstein-Friesian, all genetic correlations with AGE were negative and moderate to strong. Genetic correlations indicate that selection for increased carcase weight should simultaneously increase growth rate and improve conformation in all breeds and reduce carcase fatness in the majority of beef breeds. The results indicate that there is genetic variation in all five traits suitable for undertaking genetic improvement of carcase traits and age at slaughter; however, there are apparent breed differences. The use of abattoir-derived phenotypes for undertaking genetic improvement is an example where the supply chain can work together to share information to enable the cattle industry to move forward.

Predicting pregnancy status from mid-infrared spectroscopy in dairy cow milk using deep learning.

Accurately identifying pregnancy status is imperative for a profitable dairy enterprise. Mid-infrared (MIR) spectroscopy is routinely used to determine fat and protein concentrations in milk samples. Mid-infrared spectra have successfully been used to predict other economically important traits, including fatty acid content, mineral content, body energy status, lactoferrin, feed intake, and methane emissions. Machine learning has been used in a variety of fields to find patterns in vast quantities of data. This study aims to use deep learning, a sub-branch of machine learning, to establish pregnancy status from routinely collected milk MIR spectral data. Milk spectral data were obtained from National Milk Records (Chippenham, UK), who collect large volumes of data continuously on a monthly basis. Two approaches were followed: using genetic algorithms for feature selection and network design (model 1), and transfer learning with a pretrained DenseNet model (model 2). Feature selection in model 1 showed that the number of wave points in MIR data could be reduced from 1,060 to 196 wave points. The trained model converged after 162 epochs with validation accuracy and loss of 0.89 and 0.18, respectively. Although the accuracy was sufficiently high, the loss (in terms of predicting only 2 labels) was considered too high and suggested that the model would not be robust enough to apply to industry. Model 2 was trained in 2 stages of 100 epochs each with spectral data converted to gray-scale images and resulted in accuracy and loss of 0.97 and 0.08, respectively. Inspection on inference data showed prediction sensitivity of 0.89, specificity of 0.86, and prediction accuracy of 0.88. Results indicate that milk MIR data contains features relating to pregnancy status and the underlying metabolic changes in dairy cows, and such features can be identified by means of deep learning. Prediction equations from trained models can be used to alert farmers of nonviable pregnancies as well as to verify conception dates.

A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra.

Lactoferrin (LF) is a glycoprotein naturally present in milk. Its content varies throughout lactation, but also with mastitis; therefore it is a potential additional indicator of udder health beyond somatic cell count. Condequently, there is an interest in quantifying this biomolecule routinely. First prediction equations proposed in the literature to predict the content in milk using milk mid-infrared spectrometry were built using partial least square regression (PLSR) due to the limited size of the data set. Thanks to a large data set, the current study aimed to test 4 different machine learning algorithms using a large data set comprising 6,619 records collected across different herds, breeds, and countries. The first algorithm was a PLSR, as used in past investigations. The second and third algorithms used partial least square (PLS) factors combined with a linear and polynomial support vector regression (PLS + SVR). The fourth algorithm also used PLS factors, but included in an artificial neural network with 1 hidden layer (PLS + ANN). The training and validation sets comprised 5,541 and 836 records, respectively. Even if the calibration prediction performances were the best for PLS + polynomial SVR, their validation prediction performances were the worst. The 3 other algorithms had similar validation performances. Indeed, the validation root mean squared error (RMSE) ranged between 162.17 and 166.75 mg/L of milk. However, the lower standard deviation of cross-validation RMSE and the better normality of the residual distribution observed for PLS + ANN suggest that this modeling was more suitable to predict the LF content in milk from milk mid-infrared spectra (R2v = 0.60 and validation RMSE = 162.17 mg/L of milk). This PLS +ANN model was then applied to almost 6 million spectral records. The predicted LF showed the expected relationships with milk yield, somatic cell score, somatic cell count, and stage of lactation. The model tended to underestimate high LF values (higher than 600 mg/L of milk). However, if the prediction threshold was set to 500 mg/L, 82% of samples from the validation having a content of LF higher than 600 mg/L were detected. Future research should aim to increase the number of those extremely high LF records in the calibration set.

Ethical aspects in the use of radiation in medicine: update from ICRP Task Group 109.

Whereas scientific evidence is the basis for recommendations and guidance on radiological protection, professional ethics is critically important and should always guide professional behaviour. The International Commission on Radiological Protection (ICRP) established Task Group 109 to advise medical professionals, patients, families, carers, the public, and authorities about the ethical aspects of radiological protection of patients in the diagnostic and therapeutic use of radiation in medicine. Occupational exposures and research-related exposures are not within the scope of this task group. Task Group 109 will produce a report that will be available to the different interested parties for consultation before publication. Presently, the report is at the stage of a working document that has benefitted from an international workshop organised on the topic by the World Health Organization. It presents the history of ethics in medicine in ICRP, and explains why this subject is important, and the benefits it can bring to the standard biomedical ethics. As risk is an essential part in decision-making and communication, a summary is included on what is known about the dose-effect relationship, with emphasis on the associated uncertainties. Once this theoretical framework has been presented, the report becomes resolutely more practical. First, it proposes an evaluation method to analyse specific situations from an ethical point of view. This method allows stakeholders to review a set of six ethical values and provides hints on how they could be balanced. Next, various situations (e.g. pregnancy, elderly, paediatric, end of life) are considered in two steps: first within a realistic, ethically challenging scenario on which the evaluation method is applied; and second within a more general context. Scenarios are presented and discussed with attention to specific patient circumstances, and on how and which reflections on ethical values can be of help in the decision-making process. Finally, two important related aspects are considered: how should we communicate with patients, family, and other stakeholders; and how should we incorporate ethics into the education and training of medical professionals?

Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning.

Bovine tuberculosis (bTB) is a zoonotic disease in cattle that is transmissible to humans, distributed worldwide, and considered endemic throughout much of England and Wales. Mid-infrared (MIR) analysis of milk is used routinely to predict fat and protein concentration, and is also a robust predictor of several other economically important traits including individual fatty acids and body energy. This study predicted bTB status of UK dairy cows using their MIR spectral profiles collected as part of routine milk recording. Bovine tuberculosis data were collected as part of the national bTB testing program for Scotland, England, and Wales; these data provided information from over 40,500 bTB herd breakdowns. Corresponding individual cow life-history data were also available and provided information on births, movements, and deaths of all cows in the study. Data relating to single intradermal comparative cervical tuberculin (SICCT) skin-test results, culture, slaughter status, and presence of lesions were combined to create a binary bTB phenotype labeled 0 to represent nonresponders (i.e., healthy cows) and 1 to represent responders (i.e., bTB-affected cows). Contemporaneous individual milk MIR spectral data were collected as part of monthly routine milk recording and matched to bTB status of individual animals on the single intradermal comparative cervical tuberculin test date (±15 d). Deep learning, a sub-branch of machine learning, was used to train artificial neural networks and develop a prediction pipeline for subsequent use in national herds as part of routine milk recording. Spectra were first converted to 53 × 20-pixel PNG images, then used to train a deep convolutional neural network. Deep convolutional neural networks resulted in a bTB prediction accuracy (i.e., the number of correct predictions divided by the total number of predictions) of 71% after training for 278 epochs. This was accompanied by both a low validation loss (0.71) and moderate sensitivity and specificity (0.79 and 0.65, respectively). To balance data in each class, additional training data were synthesized using the synthetic minority over sampling technique. Accuracy was further increased to 95% (after 295 epochs), with corresponding validation loss minimized (0.26), when synthesized data were included during training of the network. Sensitivity and specificity also saw a 1.22- and 1.45-fold increase to 0.96 and 0.94, respectively, when synthesized data were included during training. We believe this study to be the first of its kind to predict bTB status from milk MIR spectral data. We also believe it to be the first study to use milk MIR spectral data to predict a disease phenotype, and posit that the automated prediction of bTB status at routine milk recording could provide farmers with a robust tool that enables them to make early management decisions on potential reactor cows, and thus help slow the spread of bTB.

Using e-cigarettes for smoking cessation: evaluation of a pilot project in the North West of England.

AIMS: E-cigarettes have been advocated as an effective smoking cessation intervention, with evidence indicating that they are substantially less harmful than conventional cigarettes. As a result, a pilot to encourage people to swap from conventional cigarettes to e-cigarettes was conducted in 2018 in a socially deprived area in the North West of England. This evaluation highlights the key findings from the pilot. METHODS: An analysis of secondary data at 4 weeks (n = 1022) was undertaken to predict those who used solely used e-cigarettes (i.e. had quit tobacco, as confirmed by a carbon monoxide test, CO < 10 ppm) from baseline characteristics, using chi-square tests and logistic regression. Baseline data were demographics, smoking levels and service provider type. RESULTS: Of the 1022 participants who engaged with the pilot 614 were still engaged at 4 weeks, of whom 62% had quit; quitting was more likely in younger participants (aged 18-24) and less likely in those who were sick and disabled. Of those who still smoked tobacco at week 4 (n = 226), smoking had reduced from a baseline of 19.1 cigarettes/day to 8.7. Overall, 37% (381) of those initially enrolled were confirmed to be using an e-cigarette on its own at follow-up. Successful quit was associated with occupation (unemployed, 33% vs intermediate, 47%, p = .023) and residing in the less deprived quintiles of deprivation (50% vs 34% in the most deprived quintile, p = .016). CONCLUSIONS: Making the conservative assumption that all those not in contact at 4 weeks were still smoking tobacco, for every five people entering the scheme, three people stayed on the programme and reduced their cigarette smoking and one person cut out tobacco altogether. E-cigarettes appear to be an effective nicotine replacement therapy; however, further research is required to determine whether e-cigarette users are more likely to reduce their overall nicotine consumption in the longer term.

Energy profiling of dairy cows from routine milk mid-infrared analysis.

The balance of body energy within and across lactations can have health and fertility consequences for the dairy cow. This study aimed to create a large calibration data set of dairy cow body energy traits across the cow's productive life, with concurrent milk mid-infrared (MIR) spectral data, to generate a prediction tool for use in commercial dairy herds. Detailed phenotypic data from 1,101 Holstein Friesian cows from the Langhill research herd (SRUC, Scotland) were used to generate energy balance (EB) and effective energy intake (EI), both in megajoules per day. Pretreatment of spectral data involved standardization to account for drift over time and machine. Body energy estimates were aligned with their spectral data to generate a prediction of these traits based on milk MIR spectroscopy. After data edits, partial least squares analysis generated prediction equations with a coefficient of determination from split sample 10-fold cross validation of 0.77 and 0.75 for EB and EI, respectively. These prediction equations were applied to national milk MIR spectra on over 11 million animal test dates (January 2013 to December 2016) from 4,453 farms. The predictions generated from these were subject to phenotypic analyses with a fixed regression model highlighting differences between the main dairy breeds in terms of energy traits. Genetic analyses generated heritability estimates for EB and EI ranging from 0.12 to 0.17 and 0.13 to 0.15, respectively. This study shows that MIR-based predictions from routinely collected national data can be used to generate predictions of dairy cow energy turnover profiles for both animal management and genetic improvement of such difficult and expensive-to-record traits.

Anti-N-Methyl-D-Aspartate Receptor Encephalitis: A Review of Psychiatric Phenotypes and Management Considerations: A Report of the American Neuropsychiatric Association Committee on Research.

OBJECTIVE: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is an autoimmune disorder characterized by prominent neuropsychiatric symptoms. Given the nature of its pathophysiology, psychiatrists tend to be one of the first clinicians encountering patients with the disease. METHODS: In the present review of patients described in the literature with psychiatric symptoms, the authors aimed to characterize the psychiatric symptoms of the disease and its management in adults and adolescents as well as children (≤12 years old). A total of 544 patients fulfilled the inclusion criteria. RESULTS: The authors found that 77% of patients with NMDAR encephalitis presented initially with psychiatric symptoms. These were mostly agitation (59%) and psychotic symptoms (in 54%, especially disorganized behavior and visual-auditory hallucinations), with agitation even more commonly being the presenting symptom in children (66%). Where psychotic symptoms were detailed, visual (64%) and auditory (59%) hallucinations were the most common, as well as persecutory delusions (73%). However, delusions were not clearly characterized in most cases. Catatonia was described in 42% of adult patients and 35% of children. Of the patients with documented exposure to antipsychotics, 33% were suspected to have an adverse drug reaction (notably, neuroleptic malignant syndrome in 22% of the cases). CONCLUSIONS: On the basis of these findings, it is important to consider anti-NMDAR encephalitis in the differential diagnosis of patients with an acute onset psychosis, especially in association with agitation, catatonia, or adverse response to antipsychotics. Furthermore, it is important to use antipsychotics with caution in patients with suspected or confirmed anti-NMDAR encephalitis.

Therapeutic Uses of Seizures in Neuropsychiatry.

Although seizures typically indicate a state of brain dysfunction, there are circumstances in which the biological effects of a seizure may exert therapeutic benefits. The standard technique for inducing controlled therapeutic seizures in humans is electroconvulsive therapy (ECT), a treatment that involves the application of an electrical stimulus to the scalp of a patient under general anesthesia and muscle relaxation. This review discusses the contemporary use of ECT for treating certain mental and neurologic disorders and previews two experimental forms of seizure therapy that are related to ECT and may hold promise for the future: focal electrically administered seizure therapy and magnetic seizure therapy.

Genome-wide association analyses based on a multiple-trait approach for modeling feed efficiency.

Genome-wide association (GWA) of feed efficiency (FE) could help target important genomic regions influencing FE. Data provided by an international dairy FE research consortium consisted of phenotypic records on dry matter intakes (DMI), milk energy (MILKE), and metabolic body weight (MBW) on 6,937 cows from 16 stations in 4 counties. Of these cows, 4,916 had genotypes on 57,347 single nucleotide polymorphism (SNP) markers. We compared a GWA analysis based on the more classical residual feed intake (RFI) model with one based on a previously proposed multiple trait (MT) approach for modeling FE using an alternative measure (DMI|MILKE,MBW). Both models were based on a single-step genomic BLUP procedure that allowed the use of phenotypes from both genotyped and nongenotyped cows. Estimated effects for single SNP markers were small and not statistically important but virtually identical for either FE measure (RFI vs. DMI|MILKE,MBW). However, upon further refining this analysis to develop joint tests within nonoverlapping 1-Mb windows, significant associations were detected between either measure of FE with a window on each of Bos taurus autosomes BTA12 and BTA26. There was, as expected, no overlap between detected genomic regions for DMI|MILKE,MBW and genomic regions influencing the energy sink traits (i.e., MILKE and MBW) because of orthogonal relationships clearly defined between the various traits. Conversely, GWA inferences on DMI can be demonstrated to be partly driven by genetic associations between DMI with these same energy sink traits, thereby having clear implications when comparing GWA studies on DMI to GWA studies on FE-like measures such as RFI.